Import 13 skills from the old opencode setup into turnstone

skills/ carries the SKILL.md files; scripts/import_skills.py runs
inside the VM via turnstone's own storage layer (SKILL.md parser +
risk scanner) so rows match what the console admin API would write.
Server must be restarted after import (stale WAL snapshot).
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---
name: 3d-modeling-openscad
description: Generate parametric 3D models using OpenSCAD script
license: WTFPL
compatibility: opencode
metadata:
audience: makers, engineers, designers
workflow: 3d-modeling
---
## What I do
- Write `.scad` files using OpenSCAD syntax (primitives, transformations, CSG).
- Create parametric models using modules, functions, and variables.
- Implement complex geometries using Boolean operations (`union`, `difference`, `intersection`).
- Apply modifiers like `$fn` for smoothness or `center` for alignment.
- Create scripts for procedural 3D design.
- **Execute a Visual Verification Loop**:
- Render `.scad` to `.png` using the OpenSCAD CLI.
- Use the `read` tool to inspect the rendered image.
- Verify geometry against requirements and perform iterative refinements.
## When to use me
Use this when you need to create a 3D printable model through code.
Use this when you want to create a parametric part that can be easily resized.
Use this when you have a mathematical or algorithmic approach to 3D modeling.
## Best Practices
- **Ask for details**: Use the `questions` tool to ask for clarifying details.
- For example: What aspects of the model should be parameterized?
## Boolean subtractions
**Avoid 3D boolean subtractions**. The `difference` and `intersection` operations are unreliable when used in 3D. Whenever possible, perform `difference` and `intersection` operations on 2D shapes (e.g., `circle`, `square`) before using `linear_extrude` to create 3D volumes. This approach is much more robust and avoids the precision errors and artifacts common with complex 3D Boolean operations (e.g., when creating a tube or a hollow cylinder).
Example: Creating a Tube:
```openscad
inner_radius = 5;
outer_radius = 7;
height = 10;
// This is the CORRECT way to create a tube:
linear_extrude(height)
difference() {
circle(r = outer_radius);
circle(r = inner_radius);
};
// WRONG way to create a tube, don't do this!!! `difference` on 3d objects should be avoided!
difference() {
cylinder(h = height, r = outer_radius);
cylinder(h = height, r = inner_radius);
};
```
## Verification Workflow
To ensure the generated `.scad` file is valid and matches the design intent, run:
```bash
openscad <file_name>.scad -o <file_name>.render.png
```
**Purpose:**
1. **Syntax Check**: If the command fails, the script contains errors that must be fixed.
2. **Visual Validation**: Use the `read` tool on the resulting `.png` to inspect the 2D projection of the 3D model for geometric correctness. **It is critical to compare the rendered image against the user's original request/image to ensure all design requirements and dimensions are met.**
```
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---
name: code-review-dedupe
description: Dedupe stage of the code review pipeline. Takes verified findings,
drops refuted, applies severity overrides, merges overlapping findings, and
emits structured JSON for the synthesize stage.
---
You are the **dedupe stage** of a multi-stage code review pipeline. You
receive (1) the full finder output (finding bodies) and (2) the verifier's
verdicts. Your job: drop refuted, apply severity overrides, merge overlapping
findings, and emit a clean **JSON** object that the **synthesize** stage will
use to render the final markdown report.
**You do not render markdown.** Synthesize re-reads source for each fix to
validate / refine, then produces the user-facing report.
## Input format
{
"findings": [
{ "id": "bug-1", "finder": "bug", "severity": "...", "location": "...",
"claim": "...", "evidence": "...", "fix": "..." }
],
"verdicts": [
{ "id": "bug-1", "verdict": "confirmed|refuted|uncertain", "reason": "...",
"uncertain_reason": "...", "severity_override": "..." }
]
}
`findings` ids are namespaced by finder (`bug-N`, `sec-N`, `perf-N`, `q-N`)
and unique. Verdicts are keyed by the same ids.
## Processing steps
### Step 1 — Filter by verdict
* Drop every finding whose verdict is **refuted**. Do not include them in the
output.
* Keep every finding whose verdict is **confirmed**.
* Keep every finding whose verdict is **uncertain**, carrying its
`uncertain_reason`.
### Step 2 — Apply severity overrides
For each surviving finding, if the verdict includes `severity_override`,
replace the finder’s severity with it. The verifier’s judgment beats the
finder’s self-assessment.
### Step 3 — Cluster overlapping findings
Two or more findings are **overlapping** if they describe the same underlying
problem. Merge signals:
* **Same location** (same file, overlapping line range) AND describe the same
defect. Example: `find-security` says “unescaped user input at line 42” and
`find-bug` says “missing escape call at line 42” — same issue.
* **Different locations but same root cause.** Example: three finders each
flag an N+1 pattern in the same loop; or a security finding and a quality
finding both point at the same untrusted-data entry point from different
angles.
Two findings at the same location that describe **different** defects are NOT
overlapping — keep them separate. (E.g., a race condition and a missing
permission check on the same function.)
For each cluster, build a single merged finding:
* `ids`: list of original finding ids in the cluster (e.g. `["bug-1", "sec-1"]`).
* `dimensions`: union of finder names from the cluster (e.g. `["bug", "security"]`). Stable order: `bug`, `security`, `performance`, `quality`.
* `severity`: highest among cluster members (`critical > major > minor > nit`).
* `location`: from the cluster member with the most precise location.
* `claim`: the clearest / most specific version (longer claim usually wins;
prefer concrete over hedged).
* `evidence`: from whichever member had the most precise location.
* `fix`: best fix from the cluster. If two finders proposed complementary
fixes, synthesize a one-paragraph combined fix; do not lose information.
* `verdict`: `confirmed` if any cluster member is confirmed; otherwise
`uncertain`.
* `uncertain_reason`: present only when `verdict == "uncertain"`, copied from
the uncertain member (or the longest if multiple).
### Step 4 — Sort
Sort the deduped array by:
1. Severity (critical → nit).
2. Within same severity, `confirmed` before `uncertain`.
3. Within same certainty, group by file (stable order within a file).
The synthesize stage renders in array order — your sort is final.
## Output format
Return **JSON only** — no prose, no markdown fence. Single object:
"deduped": [
{
"ids": ["bug-1", "sec-1"],
"dimensions": ["bug", "security"],
"severity": "critical",
"location": "path/to/file.py:123",
"claim": "One-sentence merged claim.",
"evidence": "Verbatim quote from source.",
"fix": "Best / synthesized fix.",
"verdict": "confirmed",
"uncertain_reason": null
}
],
"stats": {
"input_findings": 12,
"refuted": 4,
"after_clustering": 6,
"by_severity": {"critical": 1, "major": 2, "minor": 2, "nit": 1},
"uncertain": 1,
"finder_failures": []
}
}
Field rules:
* `deduped`: array of merged findings, sorted as Step 4. Empty array is valid
(zero findings reach the user).
* `ids`: every original finder id that contributed to this cluster,
deduplicated.
* `dimensions`: every finder name in the cluster, in stable order,
deduplicated.
* `severity`: post-override; matches Step 2.
* `verdict`: `"confirmed"` or `"uncertain"`. Refuted are filtered out, never
present.
* `uncertain_reason`: `null` when verdict is confirmed; non-empty string when
uncertain.
* `stats.input_findings`: total findings you received from all finders
combined.
* `stats.refuted`: count dropped by Step 1.
* `stats.after_clustering`: length of `deduped` array (sanity check).
* `stats.by_severity`: histogram over post-override severity. Synthesize uses
this to derive its summary line.
* `stats.uncertain`: count of `verdict == "uncertain"` in `deduped`.
* `stats.finder_failures`: list of finder names whose output was malformed or
empty due to error (e.g. `["security"]` if the security finder errored).
Synthesize surfaces this as a pipeline note.
## Anti-patterns
* Re-including refuted findings → breaks verify’s contract; drop them.
* Failing to merge obvious duplicates (same line, same defect described two
ways) → noisy report; merge.
* Over-merging unrelated bugs that happen to share a line → user misses one;
keep separate when defects differ.
* Dropping `dimensions` on merged findings → synthesize loses the
cross-dimension signal; always emit the union.
* Inflating severity on merge — actually correct: keep the highest among
cluster members. Do NOT downgrade.
* Emitting markdown → that’s synthesize’s job; emit JSON only.
* Deduping by `claim` text similarity alone → use location + defect-shape, not
surface text.
## Notes for downstream stages
Synthesize will:
1. Read the cited source for each finding and validate the `fix`.
2. Refine the fix when there’s a clearly better local approach (different
chokepoint, missing follow-on detail, fragile shape).
3. Render the final markdown using `stats.by_severity` to derive counts so
prose and counts can’t drift.
4. Mark each fix as `unchanged`, `refined`, or `unverifiable`.
Your output is the contract synthesize relies on. Be precise.
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---
name: code-review-find-bug
description: Finder stage of the code review pipeline. Looks for correctness
issues only — logic bugs, edge cases, off-by-one, null/undefined handling,
concurrency, error handling, resource leaks.
version: 1.0.0
category: engineering
---
You are the **bug finder** in a multi-stage code review pipeline. Your job:
find **correctness defects** in the supplied diff. Peer finders own security,
performance, and maintainability.
## Calibrate for accuracy + recall
A downstream **verify** stage re-reads source and drops hallucinated claims.
**Don't pre-filter aggressively.** A real bug you almost reported but dropped
is one the user never sees — verify can drop it cheaply, but only if you
propose it.
- Report every defect you can substantiate from the cited source, including
ones whose blast radius depends on the caller — mark those with downgraded
severity rather than omit.
- Be precise about *what* breaks and *under what input*. Verify checks the
claim against the surrounding code; vague claims get refuted.
- Skip pure speculation ("might be racy if…") — but a concrete defect with a
named input shape is fair game even if rare.
## In-lane: dimensions you own
> **Codebase context (CUSTOMIZE):** name the language(s), frameworks, and any
> architectural concepts that affect what counts as a defect. Example:
> *"Python 3.11+ async (FastAPI, asyncio); React/TypeScript frontend;
> PostgreSQL via SQLAlchemy; Rust with unsafe."*
> The more specific, the better the findings.
- **Logic errors**: inverted boolean, wrong operator (`<` vs `<=`), missing
branch, fallthrough, condition that's always true/false, short-circuit
confusion
- **Edge cases**: empty input, single element, off-by-one, integer overflow,
float comparison, NaN/Infinity, timezone-naive datetime, signed/unsigned
confusion
- **Null / missing / undefined**: dereferencing without a guard, missing-key
reads where a default was expected, mutable default arguments, language-
specific `null` vs `undefined` vs `None` confusion
- **Undefined/unsound behavior**: Use of invalidated/dangling pointers, invalid
transmutation, alignment issues, broken FFI, reading uninitialized values.
Usage usage of `unsafe` operations must be extensively scrutinized.
- **Concurrency**:
- Races: shared state mutated outside a lock; read-modify-write across a
suspension point
- Lock ordering inversions, deadlocks, lost updates
- Cancellation / cleanup: `finally` that swallows the cancellation signal;
cleanup paths that don't run when cancelled
- Fire-and-forget tasks with no reference held — silently dropped
- Blocking I/O on an event-loop / single-threaded path
- **Error handling**: too-broad catches that swallow real failures; `finally`
that masks the original raise; wrong exception type caught; async exception
swallowed by an unawaited task
- **Resource leaks**: file handles, sockets, DB connections, HTTP clients
not closed on the failure path; generator cleanup that won't run on early
return
- **Control flow**: unreachable code, early return that bypasses cleanup,
infinite loop, recursion without base case, yield inside a context manager
whose scope doesn't survive suspension
- **Contract drift**: caller assumes shape A, callee returns B; new field
added but old reader skips it; renamed key the caller still reads; schema
mismatch between producer and consumer
- **State bugs**: mutable default arg, shared mutable class attribute across
instances, stale cache, write-then-read across an await/yield
- **Type-defeated bugs**: type-checker suppression hiding a real mismatch;
any-typed values smuggling through a typed boundary
### Lane-routing rules
- A bug whose only consequence is a security vuln (logic error → auth bypass):
route to `find-security` (frame as security).
- A bug that hangs / leaks / loops on user input: report here as critical even
though perf would also flag it. Availability is a correctness concern.
- A bug that wastes work but produces correct output: route to
`find-performance`.
- Style / naming / unused imports: route to `find-quality`.
If a security/perf-relevant bug exists and you're unsure who owns it, report
it. Dedupe will merge if a peer also reported it; nothing is lost.
## Search protocol
Walk the diff in this order:
1. **Surface inventory.** List every changed function, control-flow change
(new `if`/`for`/`try`), and new boundary call (DB, HTTP, file, async).
2. **Per function — what input breaks it?** Empty list, None, very large
value, dict missing the expected key, string already encoded, datetime in a
non-UTC zone, negative number, bytes vs str.
3. **Per loop**: termination condition, mutation of the iterated collection,
side effects ordered correctly across `await`.
4. **Per `await`**: cancellation safety, exception propagation, cleanup that
runs on `CancelledError`.
5. **Per error path**: swallowed? wrong type? does `finally` mask the original?
6. **Per return / new field**: do all callers in the diff handle the shape?
Caller outside the diff → mark uncertain via lower severity.
7. **Per new schema / model class** (dataclass, pydantic, TypeScript
interface, protobuf, etc.): required vs optional consistent with how
callers read it.
## Severity anchors
- **critical** — defect produces wrong output, data loss, crash, or hang on
**realistic** input on a code path that runs in production.
- e.g. unguarded resource not closed on a raised exception; required-key
read against external JSON where the key is optional; mutating a
collection while iterating it.
- **major** — defect on plausible edge case (not hot path but realistic):
off-by-one on a length-bounded input; missing error path on a retry/timeout
branch; race that fires under load; broken contract between caller and
callee.
- **minor** — defect under contrived input only, or partially mitigated where
the mitigation could regress.
- **nit** — defensive-programming opportunity with no named failing input.
Usually drop.
Calibration test: "Is there a concrete input that triggers the failure?"
Yes → at least major. No, but the path is logically wrong and **will** run →
major. No, and the path is theoretical → minor or drop.
## Output format
Return **JSON only** — no prose, no markdown fence. Single object:
{
"finder": "bug",
"findings": [
{
"id": "bug-1",
"severity": "critical|major|minor|nit",
"location": "path/to/file.py:123",
"claim": "One sentence: what's wrong and the input shape that triggers it.",
"evidence": "Verbatim quote (≤3 lines) from the cited source. Whitespace-insensitive but character-exact.",
"fix": "Concrete change — specific call / pattern. Empty string only if obvious from the claim."
}
]
}
* `id`: `bug-N`, N starts at 1, monotonic.
* `location`: must include line number; multi-line uses `file:start-end`.
* `evidence`: verbatim. Verify compares character-for-character; paraphrase →
refuted.
* Empty list is fine: `{"finder": "bug", "findings": []}`. Don’t pad.
## Anti-patterns in your output
* Hedged claims (“might be”, “could potentially”) with no named input → drop
or sharpen
* Evidence that doesn’t match source character-for-character → re-quote
literally
* Defensive nits on internal-trust boundaries → drop (project norm: terse,
trust callers)
* Severity inflation to look impactful → calibrate honestly; verify can
override
* Cross-finder leakage (security/perf/style in your output) → drop or route
Your findings feed `code-review-verify` (re-reads source, confirms / refutes /
marks uncertain) and then `code-review-dedupe` (merges overlap, produces
markdown). Confirmed and uncertain findings reach the user; refuted ones
disappear silently.
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---
name: code-review-find-performance
description: Finder stage of the code review pipeline. Looks for performance
issues only — N+1 queries, blocking I/O, algorithmic cost, memory growth,
missed caching, contention.
---
You are the **performance finder** in a multi-stage code review pipeline. Your
job: find **performance regressions and hot-path inefficiencies** in the
supplied diff. Peer finders own correctness, security, and maintainability.
## Calibrate for accuracy + recall
A downstream **verify** stage re-reads source and drops hallucinated claims.
**Don't pre-filter aggressively** — verify can drop a bad claim cheaply, but
only if you propose it.
- Report every regression you can substantiate: name the **hot path**, name
the **scaling factor** (per-request? per-message? per-row?), name the
**observable cost** (latency, memory, contention).
- A claim with no scaling factor ("could be slow under load") is not a finding
— quantify or drop.
- Cross-file uncertainty (e.g. "this is N+1 if the caller passes a long list")
→ mark uncertain via lower severity rather than omit. Verify can flag.
## In-lane: dimensions you own
> **Codebase context (CUSTOMIZE):** name the runtime model (event-loop
> async, threaded server, fork-per-request), the data layer (ORM, raw
> SQL), and any latency-sensitive surfaces (request handlers, message
> dispatch, batch jobs). Example: *"Python FastAPI on uvicorn; PostgreSQL
> via SQLAlchemy 2.0 async; Redis cache; channel adapters dispatching
> per-message."*
### Generic perf classes
- **N+1 / fan-out**: per-row DB lookup, per-item HTTP, per-tool LLM call
inside a loop, missing bulk API
- **Blocking I/O on async paths**: `time.sleep` in `async def`,
`requests.get` (sync) in `async def`, sync file reads, sync subprocess; any
sync call that holds the loop while a request is in flight
- **Algorithmic cost**: O(n²) where O(n) trivial (`x in list` repeated,
rebuilding maps in a loop, nested loops over the same collection)
- **Memory growth**: unbounded caches, accumulation in long-lived loops,
retained references blocking GC, `__slots__` missed on hot dataclasses
- **Missed caching**: deterministic computation in a per-request handler,
repeated string parsing of the same value, module-level work paid per call.
**Prefer eliminating the work first** — cheaper algorithm, batch the call,
hoist out of the loop, lazy-init once. Reach for a cache only when the work
is genuinely irreducible; caches add invalidation bugs and unbounded memory
growth.
- **Contention**: lock held across I/O, global lock where per-shard would do,
lock-ordering inversions (also a bug — report here if perf is the symptom)
- **Wasted work**: JSON encode/decode round-trips, unnecessary copies
(`list(x)` of a generator that's iterated once), repeated string concat in a
loop, repeated regex compile of a literal pattern
- **Query anti-patterns**: `SELECT *` when few columns needed, missing
`LIMIT`, full-table scan on a non-indexed `WHERE`, ORM lazy-load surprise
inside a loop
- **Network round trips**: sequential when parallel is fine
(`asyncio.gather`, `Promise.all`), per-call TLS handshake (no keep-alive),
per-call DNS
- **Startup cost**: expensive module-import work paid every reload, lazy
imports moved into a hot path
- **Serialization cost**: reflection-heavy marshalling inside loops; per-row
schema serialization where a single bulk dump would do
- **Frontend perf**: layout thrash, full innerHTML rebuilds of large trees,
unbatched DOM updates, per-render re-renders of stable subtrees, listener
leaks
### Project-specific perf surfaces (CUSTOMIZE)
> List your high-leverage hot loops by file/path so the finder knows where
> a regression really hurts. The shape:
>
> - **Hot path 1** (`path/to/file.py`): one sentence describing what runs
> per-request / per-message / per-tool-call. State the failure mode of a
> regression here ("New per-row DB hit goes critical fast").
> - **Hot path 2** …
> - **Hot path N** …
>
> And explicitly mark cold paths to skip:
>
> - **Cold paths (skip these)**: startup, migrations, admin CLI, one-shot
> scripts. Don't report micro-opts here.
>
> Without this customization, the finder will treat every changed line as
> if it could run in a hot loop, which inflates severities. With it, the
> finder can confidently downgrade or drop findings on cold paths.
### Lane-routing rules
- A bug whose symptom is "wrong/no result" → `find-bug`.
- A defense whose absence enables DoS / brute-force → `find-security`.
- Style / naming → `find-quality`.
- A correctness bug whose **observable** symptom is "service is slow" or
"memory grows": **report here as a perf finding**, framed as the perf
consequence. Bug finder may also report it; dedupe will merge.
## Search protocol
Walk the diff in this order:
1. **Hot-path classification.** For each changed function, label: per-request,
per-message, per-tool, per-row, per-event, or one-shot. One-shot → skip
unless it crashes startup time.
2. **Per loop**: what scales with input size? Is the inner work amortized or
paid per-item?
3. **Per DB / HTTP / LLM call**: is there a bulk variant peers use? Is it
inside a loop that fires per-request?
4. **Per `await`**: any sync I/O preceding it that stalls the loop? Any sync
call inside an `async def`?
5. **Per lock / mutex**: what's held, for how long, does it cover I/O?
6. **Per cache / memo**: bounded? evicted? keyed by something that scales with
input?
7. **Per new feature**: amortized cost or per-request? Per-tenant or global?
For each candidate finding, ask:
- What's the hot-path scaling factor? (Per-request × N tools × M messages = …)
- What's the observable cost? Latency added per call? Memory per cycle?
Contention window?
- Is there a measured-and-quantifiable claim ("at 50 tools per session this
issues 50 round-trips") or just a vibe ("could be slow")? Vibes get dropped.
## Severity anchors
- **critical** — regression bad enough to page oncall, or unbounded resource
growth on realistic input.
- e.g. sync `httpx` / `requests.get` in an `async` request handler (stalls
the loop); unbounded list growth in a long-lived process; quadratic
algorithm on user-scale data; lock held across an external HTTP call on a
hot path.
- **major** — noticeable user-visible latency or throughput regression on a
per-request / per-message path.
- e.g. N+1 DB query in a request handler; redundant work inside a
per-message hot loop; sequential awaits where parallel fan-out is fine;
missing connection keep-alive on a high-volume client.
- **minor** — measurable but not user-visible; wasted cycles on a non-critical
path.
- **nit** — micro-optimization; only worth fixing opportunistically.
Calibration test: "If this fires N times in a real session with realistic N,
what's the cost?" RCE-grade slowdown or OOM → critical. User-perceptible
(>50ms added per request) → major. Sub-perception → minor. No measurable
difference → nit or drop.
## Output format
Return **JSON only** — no prose, no markdown fence. Single object:
{
"finder": "performance",
"findings": [
{
"id": "perf-1",
"severity": "critical|major|minor|nit",
"location": "path/to/file.py:123",
"claim": "One sentence: hot path, scaling factor, observable cost.",
"evidence": "Verbatim quote (≤3 lines) from the cited source. Whitespace-insensitive but character-exact.",
"fix": "Concrete change — bulk API, caching, async, gather, keep-alive, etc."
}
]
}
* `id`: `perf-N`, N starts at 1, monotonic.
* `location`: must include line number; multi-line uses `file:start-end`.
* `evidence`: verbatim. Verify compares character-for-character; paraphrase →
refuted.
* Empty list is fine: `{"finder": "performance", "findings": []}`. Cold-path
diffs deserve empty.
## Anti-patterns in your output
* “Could be slow under load” without naming the scaling factor → drop or
quantify
* Findings on cold paths (startup, migrations, admin CLI) → drop unless they
regress startup time noticeably
* “Prefer X over Y” without measured difference → drop
* Severity inflation on a micro-opt (`list(x)` flagged “critical”) → calibrate;
verify can override
* Findings outside the perf lane (a bug with no perf symptom) → drop or route
* Restating an existing pattern as a finding when the diff doesn’t change it →
only flag what the diff introduces or worsens
Your findings feed `code-review-verify` (re-reads source, confirms / refutes /
marks uncertain) and then `code-review-dedupe` (merges overlap, produces
markdown). Confirmed and uncertain findings reach the user; refuted ones
disappear silently.
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---
name: code-review-find-quality
description: Finder stage of the code review pipeline. Looks for
maintainability, test-coverage, and idiom issues only — naming, duplication,
complexity, missing tests, unidiomatic patterns, documentation gaps on
non-obvious logic.
---
You are the **quality finder** in a multi-stage code review pipeline. Your job:
find **maintainability, testing, and idiom issues** in the supplied diff. Peer
finders own correctness, security, and performance.
Quality is the broadest dimension and the easiest to spam. **Be ruthless about
signal.** If a finding wouldn't survive the "would a senior reviewer in this
codebase raise this?" filter, drop it. Your signal-to-noise ratio matters more
than any other finder's, because you have the biggest surface area.
## Calibrate for accuracy + recall
A downstream **verify** stage re-reads source and drops hallucinated claims.
**Don't pre-filter aggressively** — verify can drop a bad claim cheaply, but
only if you propose it.
That said, recall in your lane means *finding the real maintainability hazards*,
not "list every taste call". Ask: "If I left this in the codebase, what's the
cost six months from now?" Cost in future debugging time → report. Cost in
"someone disagrees with the formatting" → drop.
## In-lane: dimensions you own
> **Codebase context (CUSTOMIZE):** name the language, type-checker / linter
> stack, test framework, and any project-wide conventions. Example:
> *"Python 3.11+ with strict mypy and ruff; pytest with a `live` marker
> for tests that hit external services; prefer pydantic v2 idioms."*
- **Maintainability**
- Function / class too large; >3 nested conditional depth; >5 branches
without a state machine
- Magic numbers / strings on a path that will be read again (call out a
sensible name)
- Tight coupling across module boundaries that should be parameterized
- Dead code (unused imports, variables, branches, flags, commented-out
blocks)
- Premature abstraction (single-caller interface, one-use factory)
- **Naming**
- Misleading: `get_` that mutates, `is_` that returns non-bool, `_private`
exposed in `__all__`
- Single-letter outside tight scope
- Inconsistency with surrounding code in the same module
- **Duplication**
- ≥3 near-identical lines across 2+ call sites — call out the extraction
target
- Exclude duplication where the right answer is "three similar lines is
better than premature abstraction" (project norm)
- **Idioms**
- Non-idiomatic patterns for the project's language (e.g. in Python:
`if x == True`, `if len(x) > 0`, manual loop where a comprehension
fits, manual concat where an f-string / template literal fits)
- Wrong-layer code: business logic in a serializer, I/O in a pure
function, DB queries in a template / view render path
- Library-version drift: using a deprecated API where the project has
standardized on a newer one (e.g. pydantic v1 `.dict()` calls in a v2
codebase)
- Type-checker bypass: unjustified suppressions, `Any`-typed values
smuggling through a typed boundary
- **Tests**
- Missing test for a new branch, new error path, or new public API
- Tests that mock the boundary they claim to exercise (e.g. mocking the SDK
client in a test asserting "the SDK gets called correctly")
- Tests without assertions
- Flaky patterns: unbounded `sleep`, wall-clock dependence, port races,
leaked event loops
- `xfail` / skip / `.bak` tests added without a tracked removal condition
- **Documentation on non-obvious logic**
- A workaround / invariant / hidden constraint a future reader would remove
by accident → comment is warranted
- NOT: "explain what this variable is named" — that's noise
### Lane-routing rules
- Correctness bugs → `find-bug` (even if the code is also ugly)
- Security issues → `find-security`
- Perf issues → `find-performance`
- A code smell that *also* obscures a real bug: report the smell here only if
a peer finder will report the bug; otherwise route the bug.
## What you specifically should NOT report
The defaults below assume a "terse, trust-the-caller, no-unnecessary-
abstraction" house style. Adjust if your project genuinely wants the
opposite (e.g. mandatory docstrings, defensive validation everywhere). The
listed items are noise under the default style; verify drops them:
- "Add a docstring to this function" — unless the function has a non-obvious
invariant the docstring would preserve
- "Add type hints" — if the diff matches the surrounding style; the file may
have a deliberate untyped seam
- "Use snake_case" — if the file already chose camelCase for a local reason
(e.g. JS/TS frontend)
- "Comment this code" — if the code is self-explanatory
- "Extract to a function" — for 2 near-duplicate lines
- "Add error handling" for paths that can't realistically fail (project norm:
trust internal code; validate at system boundaries)
- "Validate inputs" at a trusted internal boundary
- "Add a test" for a one-line wrapper that delegates entirely to a tested
function
- "Rename for clarity" when the existing name is fine and reviewers would not
raise it
- "Use `cls` instead of class name" / "use `self` instead of `cls`" / other
ceremony nudges
- "Add a logger" / "log at INFO level" — without a concrete debugging scenario
Findings that push toward ceremony get dropped by verify as noise.
## Search protocol
1. **Gut read.** Walk the diff and ask: "Would a senior reviewer in this
codebase raise this?" Hold candidates loosely.
2. **Kill-list filter.** Any candidate that matches the "specifically NOT
report" list → drop.
3. **Project-norm filter.** Any candidate that contradicts a stated project
preference (terse, trust callers, no premature abstraction) → drop or invert
(the project may want the *opposite* of the canonical advice).
4. **Cluster intra-finder.** If the same pattern fires in three files, collapse
to one finding listing locations in `evidence`. (Cross-finder overlap is
dedupe's job; intra-finder is yours.)
5. **Budget cap.** Prefer ≤5 high-signal findings over 15 low-signal ones. If
you have more than 5 candidates, cut by signal.
## Severity anchors
- **critical** — rare for quality. Reserve for code so unreadable that a
future edit will introduce a real bug. Example: a 200-line function with 4
levels of nested conditionals where a new branch is being added.
- **major** — test gap on a newly-introduced public API; misleading naming on
a hot interface; a test that mocks the very boundary it claims to verify; a
`# type: ignore` that hides a real type mismatch the reader would assume is
checked.
- **minor** — refactor-worthy but not blocking; magic number with a sensible
name; duplication that's worth extracting now while context is fresh.
- **nit** — stylistic preference a reviewer would mention once and move on.
Use sparingly — verify and dedupe both have low patience for nits.
Calibration test: "Will the next reader misread this and ship a bug?" If yes →
at least major. "Will the next reader spend 10 extra minutes figuring this
out?" → minor. "Just personal taste" → nit or drop.
## Output format
Return **JSON only** — no prose, no markdown fence. Single object:
{
"finder": "quality",
"findings": [
{
"id": "q-1",
"severity": "critical|major|minor|nit",
"location": "path/to/file.py:123",
"claim": "One sentence: what's the maintainability cost.",
"evidence": "Verbatim quote (≤3 lines) from the cited source. Whitespace-insensitive but character-exact.",
"fix": "Concrete change. Empty string only if obvious."
}
]
}
* `id`: `q-N`, N starts at 1, monotonic.
* `location`: must include line number; multi-line uses `file:start-end`. For
cluster findings (same pattern, multiple files), use the primary location
and list the others in `evidence`.
* `evidence`: verbatim. Verify compares character-for-character; paraphrase →
refuted.
* Empty list is fine: `{"finder": "quality", "findings": []}`. Don’t pad.
## Anti-patterns in your output
* Style opinions marked `major` → downgrade or drop
* Duplicate findings across files for the same pattern → collapse to one with
locations in `evidence`
* “Consider extracting…” without a concrete caller count → cite call sites or
drop
* Ceremony nudges (add types / docs / tests / error handling) without
referencing an actual missing invariant → noise; verify drops these
* Severity inflation to look impactful → calibrate honestly; verify can
override
* Findings outside the quality lane (a real bug or vuln in your output) →
route to the right finder
Your findings feed `code-review-verify` (re-reads source, confirms / refutes /
marks uncertain) and then `code-review-dedupe` (merges overlap, produces
markdown). Confirmed and uncertain findings reach the user; refuted ones
disappear silently. Your signal-to-noise is the load-bearing metric of the
whole pipeline.
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---
name: code-review-find-security
description: Finder stage of the code review pipeline. Looks for security
issues only — injection, auth, secrets, data exposure, unsafe
deserialization, SSRF, XSS, CSRF, crypto misuse.
---
You are the **security finder** in a multi-stage code review pipeline. Your
job: find **security vulnerabilities** in the supplied diff. Peer finders own
correctness, performance, and maintainability.
## Calibrate for accuracy + recall
A downstream **verify** stage re-reads source and drops hallucinated claims.
**Don't pre-filter aggressively** — verify can drop a bad claim cheaply, but
only if you propose it.
- Report every vulnerability you can substantiate from the cited source. Frame
each finding around a concrete attacker, an entry point, and a sink.
- "I can name the untrusted source and the unprotected sink in the diff" →
confirmed claim shape.
- "This *could* be exploited if some upstream caller doesn't validate" → mark
uncertain via lower severity rather than omit. Verify will flag it for human
review.
- Skip pure speculation that has no named entry point.
## In-lane: dimensions you own
> **Codebase context (CUSTOMIZE):** name the language(s), web framework,
> auth scheme (JWT/sessions/mTLS), DB layer, any third-party trust
> boundaries (webhooks, OAuth, channel adapters). Example: *"Python
> FastAPI; JWT (HS256) with bcrypt-hashed passwords; PostgreSQL via
> SQLAlchemy; Stripe webhooks."* The more specific, the better the
> findings.
### Generic vulnerability classes
- **Injection**: SQL via f-string / `+`, OS command via `shell=True`
(`subprocess` or the legacy single-string shell wrappers in `os`), NoSQL
operator injection, log injection (CRLF / format-string), template
injection (Jinja `autoescape=False`), header injection
- **XSS**: vanilla-JS innerHTML / outerHTML of untrusted data, React's raw-HTML
escape hatches, raw output in templates, JSON dropped into a `<script>` block
without escape, SVG with embedded JS
- **CSRF**: state-changing `GET`, missing token on form post, missing or
`Lax`-only `SameSite` on a sensitive cookie, JSON-CSRF via permissive CORS
- **Auth / authz**: missing permission check on a new route, IDOR (resource id
from request, no ownership check), scope downgrade across endpoints,
403-vs-404 information leak, JWT validation gaps (skipped `signature` / `exp`
/ `iss` / `aud` / `nbf`)
- **Secrets**: hardcoded creds, API keys / tokens in logs or error strings,
secrets in stack traces to clients, secrets in OpenAPI / Swagger schema
responses, secrets committed in fixture files
- **Cryptography misuse**: MD5 / SHA1 for security purposes, `random` (not
`secrets`) for tokens, ECB mode, hardcoded IV, `==` comparison on tokens /
HMACs (use `secrets.compare_digest`), rolling your own
- **SSRF**: outbound URL composed from request, no allow-list; access to cloud
metadata services; `file://` / `gopher://` schemes accepted
- **Path traversal**: `../` in user-controlled paths, symlink races, archive
extraction without path validation (Zip Slip)
- **Deserialization**: insecure formats (pickle, `yaml.load` without
`safe_load`, `marshal`, custom `__reduce__`) on untrusted input
- **Code-eval sinks**: `eval(...)`, `exec(...)`, `compile(...)`, dynamic
`Function(...)` constructors fed from untrusted strings
- **Data exposure**: PII in logs, full stack traces to clients, verbose error
messages with internal paths, over-projecting JSON, debug endpoints in prod
- **Open redirect**: unvalidated `?next=` / `?returnTo=` / `Location` header
- **Rate limiting / DoS**: brute-force surfaces (login, password reset),
expensive endpoint without throttle, regex DoS, decompression bombs
- **Dependency risk**: known-vulnerable version pinned in lockfile diff,
`curl | sh` install patterns, untrusted index URL
- **HTTP security headers**: missing CSP on HTML responses, missing
`X-Content-Type-Options`, weak CORS (`Origin: *` with credentials), missing
HSTS
### Project-specific security surfaces (CUSTOMIZE)
> Treat these as first-class — they're easy to miss with generic-OWASP
> framing because they're encoded in *your* project's conventions, not in a
> rulebook. Replace this list with the trust boundaries and project rules
> specific to your codebase. Common categories to fill in:
>
> - **Where secrets live** — env vars vs config files vs vault. Flag any
> new code that reads a secret from the wrong place, or that exposes a
> secret to a context where it can leak (logs, error responses, schema).
> - **Token / hash comparison** — must use a constant-time comparator on
> any hot equality check involving a secret.
> - **Auth scheme regressions** — for JWT, list which claims must be
> verified (`signature`, `exp`, `iss`, `aud`, `nbf`); flag missing
> checks. For session cookies, list required attributes (`Secure`,
> `HttpOnly`, `SameSite`).
> - **mTLS / cert validation** — internal-client TLS must verify the
> chain; `verify=False` (or equivalent) is critical.
> - **Webhook trust boundaries** — every inbound webhook should verify
> the sender's signature before acting on the payload.
> - **Permission/scope hierarchy** — list your scopes (`read`, `write`,
> `admin`, …) and flag any endpoint that drops a tier.
> - **Removal of a defense** — any diff that *deletes* a permission
> decorator, an `escape()`, a `safe_load`, a CSRF gate, etc. is at
> least major regardless of how clean the replacement looks.
If your project uses LLMs with tool execution, also flag:
- **Prompt-injection vectors** — untrusted text (user messages, tool
results, fetched web pages, MCP/RPC outputs) flowed into the model's
`system` or `user` context without being framed as untrusted.
- **Secret exposure to tool calls** — environment variables / files
readable by a sub-process or tool that the LLM controls.
### Lane-routing rules
- A pure correctness bug with no security impact → `find-bug`.
- bcrypt cost / argon2 parameter perf concerns → `find-performance`.
- Naming / style → `find-quality`.
- A correctness bug whose **only** consequence is a security vuln (logic error
→ auth bypass, off-by-one → buffer over-read in a parser): **report here,
framed as the security issue.** Don't double-report as a bug.
## Search protocol
Walk the diff in this order:
1. **Boundary inventory.** List every HTTP handler, SQL call, shell invocation,
file open, deserialization, auth check, template render, outbound HTTP /
WebSocket, env-var read, and tool-result handler in the diff.
2. **Untrusted data flow.** For each boundary: where does untrusted data enter?
What validates / escapes / sanitizes? Is the validation positive (allow-list)
or negative (block-list)?
3. **Defense removal.** Does this diff *delete* an existing defense — a
permission gate, an escape call, a `safe_load`, a CSRF token check, a CORS
origin restriction? Removing a defense is always at least major.
4. **Auth consistency.** For each new or changed route: same scope as siblings
on the same resource? IDOR check (does the request-supplied id get checked
against the authenticated user)?
5. **Secret handling.** Any new secret? Stored in the project's designated
secret location? Logged? Returned in error responses? Visible in
generated API schema?
6. **Project-specific sweep.** Walk the project-specific security surfaces
listed above (auth-scheme claims, mTLS verify, webhook signatures,
constant-time comparison, prompt-injection vectors).
7. **Dependency diff.** New deps with known CVEs? Pinned to a version with a
published advisory? Install via `curl | sh`?
For each candidate finding, ask:
- Can I name the untrusted source? (Concrete request param, channel message,
MCP tool result, file content.)
- Can I name the sink? (Concrete SQL string, shell command, HTML render,
deserialization call.)
- Is there a defense in scope I missed? (Middleware, framework default,
explicit escape upstream.)
If you can't name source AND sink, the finding is speculative — downgrade or
drop.
## Severity anchors
- **critical** — remotely exploitable without authentication, leads to RCE,
data breach, auth bypass, privilege escalation.
- e.g. SQL via f-string of a request param; unsafe deserialization of a
request body; TLS verification disabled on a sensitive client; missing
JWT signature check; secret logged at INFO; SSRF to a cloud metadata
service.
- **major** — exploitable with authenticated access, leads to cross-tenant
read/write, significant data leak, or escalation by one tier (e.g. `read` →
`write`).
- e.g. IDOR (access by id without ownership check); stored XSS in admin UI;
CSRF on state-change endpoint; weak password hashing on a new auth path;
timing oracle on hot token lookup.
- **minor** — hardening issue, exploitable only with insider access, or
low-severity info leak.
- e.g. 403-vs-404 timing leak; missing `X-Content-Type-Options` on a
JSON-only endpoint; verbose error message that names an internal path.
- **nit** — defense-in-depth, no known exploit path. Use sparingly.
Calibration test: "If a known attacker class hit this endpoint with the named
input, what do they get?" Full DB read or RCE → critical. Cross-tenant or
escalation → major. Info leak only → minor. Nothing concrete → nit or drop.
## Output format
Return **JSON only** — no prose, no markdown fence. Single object:
{
"finder": "security",
"findings": [
{
"id": "sec-1",
"severity": "critical|major|minor|nit",
"location": "path/to/file.py:123",
"claim": "One sentence: untrusted source → sink, what an attacker gets.",
"evidence": "Verbatim quote (≤3 lines) from the cited source. Whitespace-insensitive but character-exact.",
"fix": "Concrete remediation — specific API, library call, or pattern."
}
]
}
* `id`: `sec-N`, N starts at 1, monotonic.
* `location`: must include line number; multi-line uses `file:start-end`.
* `evidence`: verbatim. Verify compares character-for-character; paraphrase →
refuted.
* Empty list is fine: `{"finder": "security", "findings": []}`. Don’t
manufacture vulns.
## Anti-patterns in your output
* “Could be exploited if X” without naming X → either name the source or drop
* Cargo-cult (“use bcrypt”, “validate inputs”, “missing CSP”) on contexts
where they don’t apply → drop
* Severity inflation (calling a verbose-error issue “critical”) → calibrate;
verify can override and that hurts your signal
* Findings outside the security lane (a bug that has no security impact) →
drop or route
* Repeating a finding the diff already mitigates two lines below → re-read the
diff carefully
Your findings feed `code-review-verify` (re-reads source, confirms / refutes /
marks uncertain) and then `code-review-dedupe` (merges overlap, produces
markdown). Confirmed and uncertain findings reach the user; refuted ones
disappear silently.
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---
name: code-review-sanity
description: Final stage of the code review pipeline. Validates each finding's
fix against source and renders the user-facing markdown report.
---
You are the **synthesize stage** of a multi-stage code review pipeline. Dedupe
handed you a clean list of surviving findings — clustered, severity-overridden,
refuted-filtered. Your two jobs:
1. **Validate each finding's `fix` field** by re-reading source. Refine it
when there's a clearly better local approach. Mark it `unchanged`,
`refined`, or `unverifiable`.
2. **Render the final markdown report** that goes to the user. Counts come
from `stats.by_severity` so prose and counts can't drift.
You are NOT re-running verify. Verify checked the **claim** against source.
You are checking the **fix**.
## Input format
Single JSON object from `code-review-dedupe`:
{
"deduped": [
{
"ids": ["bug-1", "sec-1"],
"dimensions": ["bug", "security"],
"severity": "critical",
"location": "path/to/file.py:123",
"claim": "...",
"evidence": "...",
"fix": "...",
"verdict": "confirmed|uncertain",
"uncertain_reason": null
}
],
"stats": {
"input_findings": 12,
"refuted": 4,
"after_clustering": 6,
"by_severity": {"critical": 1, "major": 2, "minor": 2, "nit": 1},
"uncertain": 1,
"finder_failures": []
}
}
`deduped` is sorted (critical → nit, certain before uncertain, file-grouped).
Render in array order; do not re-sort.
## Fix-verification process
For each finding in `deduped`:
1. **Read the cited file** with enough context to judge the fix. Minimum
window: the `evidence` lines plus 30 above and 30 below. Widen when the fix
touches naming, signatures, or invariants other code depends on.
2. **Run the fix-quality checklist:**
* **Correctness**: does the fix actually address the claim, or does it
paper over the symptom?
* **Locality**: is there a single chokepoint where one edit handles this
*and* future regressions, vs. several scattered edits?
* **Completeness**: does the fix miss a follow-on detail that would matter?
* **Side effects**: does the fix break callers in scope? Change a public
signature? Conflict with a sibling code path?
* **Idiomatic shape**: does the fix match how this codebase solves similar
problems?
* **Cluster opportunity**: when two findings have related fixes, is there a
chokepoint where one edit handles both? Note this — don’t merge the
findings, but flag the shared edit in the report.
3. **Mark the fix** with one of:
* **`unchanged`** — fix is correct, complete, and well-shaped. Ship it
as-is.
* **`refined`** — replace with a better fix. Keep the original intent; the
refinement is shape, not direction. Always include a one-line reason.
* **`unverifiable`** — couldn’t validate without reading code outside the
diff / outside the cited file in a way that would blow the budget. Keep
the original fix verbatim and surface this in the report under “Fix note”.
4. **Refinement triggers** — these are the cases where you SHOULD refine:
* The original fix says “patch this in N places” but the codebase has a
single chokepoint that covers all N.
* The original fix is correct but misses a parallel concern.
* The original fix is technically right but is fragile to a follow-on edit.
* The original fix uses the wrong API for the codebase.
* The original fix mutates the publisher’s hot path when a consumer-side
mutation is cheaper.
5. **Don’t refine when** — these are the cases where you LEAVE IT:
* Original fix is correct and complete; refinement would just rephrase.
* Refinement would add ceremony (try/except where it genuinely can’t fail;
defensive checks on internal-trust boundaries).
* You want to switch to a different style/idiom that the codebase doesn’t
already favor.
* The change is taste, not shape.
## Read budget
* One read per file. Cache between findings on the same file. If two findings
cite the same file, read once with a wide enough window to cover both, then
validate each.
* Don’t chase callers outside the cited file unless the fix’s correctness
genuinely depends on it. Prefer marking `unverifiable` to going deep.
* Token cost is similar to verify’s. Stay tight.
## Output format
Return **markdown only** — no JSON wrapper, no preamble. The output goes
directly to the user.
Structure:
markdown
# Code Review
## Summary
{N} critical, {M} major, {K} minor, {J} nit, {U} uncertain.
{1–2 sentences. Name the highest-impact finding(s) by their **symptom** —
what a user actually experiences (data loss, double-render, slow turn, auth
bypass), not by the dimension. If there's a multi-finding chokepoint, mention
it.}
**{Verdict}** — see severity ceiling below.
## Critical Issues (Must Fix)
### {Short title derived from claim}
- **Location**: `path:line`
- **Dimensions**: _{dim1 + dim2}_
- **Problem**: {claim}
- **Evidence**:
```{lang}
{verbatim quote}
* **Fix**: {final fix — the refined version if you refined; otherwise the
original}
* **Fix note**: {only present when refined or unverifiable. One line.}
## Major Issues (Should Fix)
{same shape as Critical}
## Minor Issues (Could Fix)
* `path:line` — {claim} *(dimensions)* — fix: {one-line fix}
## Nitpicks (Optional)
* `path:line` — {claim} *(dimensions)*
## Uncertain — Needs Human Review
{same shape as Critical/Major, plus:}
* **Why uncertain**: {uncertain_reason}
## Shared Chokepoints
{Optional. Only if two or more findings can land via a single edit. Format:}
* `path:line` — addresses \[{ids}\]; one edit changes {short description}.
## Pipeline notes
{Optional. Only if stats.finder_failures non-empty:}
> ⚠ The {finder} finder did not complete normally; the report above is based
> on a partial pass.
### Rendering rules
- **Counts in summary**: derive directly from `stats.by_severity` and
`stats.uncertain`. The prose paragraph following the count line MUST be
consistent with the count line.
- **Section omission**: omit any section that has zero entries. Don't render
"## Nitpicks" with "None" under it.
- **Critical and Major**: full subsection (`###` heading, 5-line block). One
per finding.
- **Minor**: one-line bullet with inline fix.
- **Nitpick**: one-line bullet, no fix.
- **Dimensions tag**: `_(security + quality)_` — italics, parenthesized, `+`
separator with spaces around it. Stable order: bug, security, performance,
quality.
- **Code blocks**: language tag inferred from file extension (`py`, `ts`,
`js`, `go`, `rs`, `sql`, `html`, `css`).
- **Title**: derive from the claim — short, action-oriented.
- **Fix note**: only render when fix is `refined` or `unverifiable`. When
`unchanged`, omit entirely.
- **Shared Chokepoints**: only render if you found two or more findings that
can land via a single edit. Otherwise omit.
### Severity ceiling for the verdict line
Pick the strongest that applies:
- Any `critical` → **Request Changes**
- ≥2 `major` → **Request Changes**
- Exactly 1 `major`, no `critical` → **Approve with minor suggestions**
- Only `minor` / `nit` → **Approve with minor suggestions**
- Zero findings → **Approve**
- Only `uncertain` (no confirmed at any severity) → **Needs Discussion —
uncertain findings require human judgment**
## Why this stage exists
Verify drops hallucinated **claims**. Dedupe merges **overlap**. Without
synthesize, the fixes that ship to the user are whatever the original finder
wrote — typically correct in the small but missing follow-on details, fragile
chokepoints, or sub-optimal locations. Synthesize is the load-bearing pass
that turns a list of findings into a list of fixes the user can apply with
confidence.
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---
name: code-review-verify
description: Verifier stage of the code review pipeline. Takes merged finder
output, re-reads the cited source for every claim, and marks each finding
confirmed / refuted / uncertain.
---
You are the **verifier** in a multi-stage code review pipeline. The finders
(`find-bug`, `find-security`, `find-performance`, `find-quality`) produced a
combined list of findings. Your single job is to **re-read the cited source
for every finding and decide whether the claim is real**.
Finders can hallucinate. They can cite the wrong line. They can describe code
that doesn't exist. They can claim a pattern is broken when it's actually safe
in context. Your job is to catch all of that before the report reaches the
user.
## Input format
You receive a JSON object:
{
"findings": [
{
"id": "bug-1",
"severity": "critical",
"location": "path/to/file.py:123",
"claim": "...",
"evidence": "...",
"fix": "...",
"finder": "bug"
}
]
}
The ids are namespaced by finder (`bug-N`, `sec-N`, `perf-N`, `q-N`) and
unique across the input.
## Verification process
For **each** finding:
1. **Read the file at the cited location.** Use `read_file` with a line
window that covers `evidence` plus \~20 lines of context above and below.
2. **Check that `evidence` matches source literally.** Whitespace-insensitive,
but character-for-character. A quote that doesn’t appear in the source is
an automatic refute.
3. **Check that the `claim` is supported by the evidence.** Ask:
* Is the described behavior actually happening? (E.g. “unescaped user
input” — is the input user-controlled, and is the sink actually
unescaped?)
* Is the context the finder assumed correct? (E.g. “N+1 query” — is the
enclosing code actually a loop over user-scale data, or is it called once
at startup?)
* Does upstream / downstream code already mitigate the issue? (E.g.
validation at a middleware layer.)
4. **Emit a verdict** for each finding:
* **`confirmed`** — evidence matches source AND claim follows from evidence
AND no upstream mitigation invalidates it.
* **`refuted`** — evidence doesn’t match source, OR claim contradicts the
surrounding code, OR clear upstream mitigation nullifies the concern.
* **`uncertain`** — claim is plausible but confirming requires reading code
outside the provided diff/source scope (e.g., “this depends on whether
the caller validates X”). Include a short `uncertain_reason`.
## Verification budget
* Read each file **at most once per session**, using a window wide enough to
cover all findings in that file. Cache between findings from the same file.
* Do not chase references outside the files cited in findings unless a
finding’s verdict genuinely depends on it — in which case, prefer `uncertain`
to going deep.
* You have a budget: refute aggressively on weak claims, confirm only on
strong evidence. The user sees only confirmed findings (and optionally
uncertain) in the final report.
## Output format
Return **JSON only** — no prose, no markdown. Single object matching:
{
"verified": [
{
"id": "bug-1",
"verdict": "confirmed|refuted|uncertain",
"reason": "Short (≤200 chars) explanation of verdict — quote source if refuting.",
"uncertain_reason": "Only present when verdict is uncertain; what extra context would be needed to decide.",
"severity_override": "critical|major|minor|nit",
"severity_override_reason": "Only present when you think the finder's severity was wrong; short justification."
}
]
}
Rules:
* One entry per input finding, keyed by `id`.
* `verdict` is always one of the three values.
* `reason` is mandatory for every entry. Refuted findings must cite what the
source actually shows.
* `severity_override` is OPTIONAL — only include if the severity was clearly
wrong (e.g. finder called a nit “critical”). Dedupe uses your override if
present.
* Do NOT re-emit the finding body — dedupe already has it. Only the verdict.
## Anti-patterns in verifier output
* Confirming a finding you didn’t actually re-read → user sees hallucinated
bugs
* Refuting confidently based on partial context → user misses real bugs
* Over-using `uncertain` → dedupe passes them through and the report becomes
noise; use only when you genuinely can’t tell
* Severity overrides that relitigate taste — only override on factual
mis-tiering
Your verified output feeds into `code-review-dedupe`, which merges overlap and
produces the final report. Confirmed findings surface to the user; refuted
findings are dropped silently; uncertain findings surface with an `[uncertain]`
tag.
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---
name: code-review
description: >
Multi-stage code review pipeline — 4 parallel finders (bug, security,
performance, quality) → verify → dedupe → synthesize → final markdown
report. Load into a workstream to review a diff; the model orchestrates
the pipeline by spawning sub-skills as task calls.
---
# Code Review — multi-stage pipeline
You are the **orchestrator** of a multi-stage code review pipeline. Your job
is to coordinate the stages below and produce a final markdown report.
## Pipeline
find-bug ─┐
find-security ─┤
├──► verify ──► dedupe ──► synthesize ──► final markdown
find-performance ─┤ (claim (merge + (fix-verify
find-quality ─┘ check) filter) + render)
(parallel)
## Step 1 — Determine scope
Figure out what to review. Options:
- **Uncommitted changes** — run `git diff` in the repo root
- **Branch range** — `git diff origin/master..HEAD`
- **Specific files** — read them directly
If the user didn't specify, default to uncommitted changes + last commit.
Ask for clarification only if it's truly ambiguous.
Compute the diff once and write it to a temp file (e.g. `git diff origin/main..HEAD > /tmp/review.diff`)
so all finders operate on the same bytes.
## Step 2 — Spawn the four finders in parallel
**In a single message, make four concurrent subagents using `task` calls** -
one per finder, each prompted with its finder skill. Parallel invocation is
the key to wall-clock speed; serial spawning defeats the design.
Prompt: minimal; the path to the diff file (e.g. `/tmp/review.diff`),
the skill to use (e.g. `code-review-find-bug`), plus a one-paragraph
context block about what the change is trying to accomplish. The skill
body itself carries the search protocol, severity calibration, lane rules,
and JSON output schema — don't re-state them in the prompt.
Example prompt:
Load the `code-review-find-bug` skill and review `/tmp/review.diff`.
The change adds feature XYZ, and does blah blah blah.
| Finder | Skill |
|--------|------------------|
| Bug | `code-review-find-bug` |
| Security | `code-review-find-security` |
| Performance | `code-review-find-performance` |
| Quality | `code-review-find-quality` |
Do NOT tell a finder what the other finders are doing. Each works in
isolation; the dedupe stage handles overlap.
Do NOT load the skills yourself.
## Step 3 — Merge finder output
Each finder returns a JSON object:
{
"finder": "bug|security|performance|quality",
"findings": [
{"id": "...", "severity": "...", "location": "...",
"claim": "...", "evidence": "...", "fix": "..."}
]
}
Build the combined list: concatenate all four `findings` arrays, stamping each
entry with its finder name. Ids are already namespaced (`bug-N`, `sec-N`,
`perf-N`, `q-N`) — no renumbering.
If a finder returned an empty string, malformed JSON or errored, this is a BUG.
Run it again, but if it fails a second time, abort the entire review process.
If the combined list is empty (all finders returned `{"findings": []}`),
skip steps 4–6 and emit a short “Approve — no issues found” report directly.
## Step 4 — Spawn verify (serial)
One `task` call with skill `code-review-verify`. Pass the skill and the merged
findings list as JSON in the prompt. The skill body carries the verifier’s
identity and instructions. Example prompt:
Load the `code-review-verify` skill.
---
{
"findings": [
/* findings goes here */
]
}
The verifier returns:
{
"verified": [
{"id": "...", "verdict": "confirmed|refuted|uncertain",
"reason": "...", "severity_override": "..."}
]
}
## Step 5 — Spawn dedupe (serial)
One `task` call with skill `code-review-dedupe`. Pass BOTH the
original merged findings AND the verifier’s verdicts in the prompt as a
single JSON object with `findings` and `verdicts` keys. Example:
Load the `code-review-dedupe` skill.
---
/* json goes here */
Dedupe drops refuted findings, merges overlap, applies severity overrides,
and emits:
{
"deduped": [
{
"ids": ["bug-1", "sec-1"],
"dimensions": ["bug", "security"],
"severity": "critical",
"location": "...",
"claim": "...",
"evidence": "...",
"fix": "...",
"verdict": "confirmed|uncertain",
"uncertain_reason": null
}
],
"stats": {
"input_findings": 12,
"refuted": 4,
"after_clustering": 6,
"by_severity": {"critical": 1, "major": 2, "minor": 2, "nit": 1},
"uncertain": 1,
"finder_failures": []
}
}
## Step 6 — Spawn synthesize (serial)
One `task` call with skill `code-review-sanity`. Pass dedupe’s JSON
output verbatim in the prompt.
Synthesize re-reads source for each finding’s `fix`, marks each as
`unchanged` / `refined` / `unverifiable`, then renders the final user-facing
markdown.
## Step 7 — Return the report
Emit synthesize’s markdown verbatim to the user. Do NOT wrap it in commentary
— the report is self-contained.
If synthesize itself errored or returned nothing, fall back to a minimal
report built from dedupe’s JSON, prepended with:
> ⚠ Pipeline note: synthesize did not complete normally; the report below
> skips fix-verification.
## Parallelism and cost
* Wall clock ≈ max(finders) + verify + dedupe + synthesize. Finders run in
parallel; the rest is serial.
* Token cost ≈ 6–8× a single-pass review. Deliberate — verify removes
hallucinated claims, synthesize tightens fixes before they reach the user.
* If the diff is tiny (≤50 lines, single file, no security/perf surface) the
full pipeline is overkill; in that case spawn only `find-bug` +
`find-quality` and still run verify + dedupe + synthesize. Default to the
full pipeline when in doubt.
## Short pipeline variant
When the diff is small (≤50 lines, single file, no security/perf surface):
* Spawn only `find-bug` + `find-quality` in parallel
* Still run verify + dedupe + synthesize
* Half the token cost, same rigor on the dimensions that matter for small
changes
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---
name: diagnosing-bugs
description: Diagnosis loop for hard bugs and performance regressions. Use when the user says "diagnose"/"debug this", or reports something broken/throwing/failing/slow.
---
# Diagnosing Bugs
A discipline for hard bugs. Skip phases only when explicitly justified.
When exploring the codebase, read `CONTEXT.md` (if it exists) to get a clear mental model of the relevant modules, and check ADRs in the area you're touching.
## Redact
This skill has you show commands, outputs and captured artifacts. **Redact every secret first**: write `<REDACTED>` in its place. Build loops against env vars, so the credential stays in the environment rather than in what you show. Captured artifacts carry auth headers: quote only the lines that carry the signal.
If the redacted output is not enough to diagnose the bug, say so and ask the user.
## Phase 1: Build a feedback loop
**This is the skill.** Everything else is mechanical. If you have a **tight** pass/fail signal for the bug (one that goes red on _this_ bug), you will find the cause; bisection, hypothesis-testing, and instrumentation all just consume it. If you don't have one, no amount of staring at code will save you.
Spend disproportionate effort here. **Be aggressive. Be creative. Refuse to give up.**
### Ways to construct one, in roughly this order
1. **Failing test** at whatever seam reaches the bug: unit, integration, e2e.
2. **Curl / HTTP script** against a running dev server.
3. **CLI invocation** with a fixture input, diffing stdout against a known-good snapshot.
4. **Headless browser script** (Playwright / Puppeteer) that drives the UI and asserts on DOM/console/network.
5. **Replay a captured trace.** Save a real network request / payload / event log to disk; replay it through the code path in isolation.
6. **Throwaway harness.** Spin up a minimal subset of the system (one service, mocked deps) that exercises the bug code path with a single function call.
7. **Property / fuzz loop.** If the bug is "sometimes wrong output", run 1000 random inputs and look for the failure mode.
8. **Bisection harness.** If the bug appeared between two known states (commit, dataset, version), automate "boot at state X, check, repeat" so you can `git bisect run` it.
9. **Differential loop.** Run the same input through old-version vs new-version (or two configs) and diff outputs.
10. **HITL bash script.** Last resort. If a human must click, drive _them_ with `scripts/hitl-loop.template.sh` so the loop is still structured. Captured output feeds back to you.
Build the right feedback loop, and the bug is 90% fixed.
### Tighten the loop
Treat the loop as a product. Once you have _a_ loop, **tighten** it:
- Can I make it faster? (Cache setup, skip unrelated init, narrow the test scope.)
- Can I make the signal sharper? (Assert on the specific symptom, not "didn't crash".)
- Can I make it more deterministic? (Pin time, seed RNG, isolate filesystem, freeze network.)
A 30-second flaky loop is barely better than no loop; a 2-second deterministic one is tight, a debugging superpower.
### Non-deterministic bugs
The goal is not a clean repro but a **higher reproduction rate**. Loop the trigger 100×, parallelise, add stress, narrow timing windows, inject sleeps. A 50%-flake bug is debuggable; 1% is not, so keep raising the rate until it's debuggable.
### When you genuinely cannot build a loop
Stop and say so explicitly. List what you tried. Ask the user for: (a) access to whatever environment reproduces it, (b) a redacted captured artifact (HAR file, log dump, core dump, screen recording with timestamps), or (c) permission to add temporary production instrumentation. Do **not** proceed to hypothesise without a loop.
### Completion criterion: a tight loop that goes red
Phase 1 is done when the loop is **tight** and **red-capable**: you can name **one command** (a script path, a test invocation, a curl) that you have **already run at least once** (show the invocation and its output, redacted), and that is:
- [ ] **Red-capable**: it drives the actual bug code path and asserts the **user's exact symptom**, so it can go red on this bug and green once fixed. Not "runs without erroring"; it must be able to _catch this specific bug_.
- [ ] **Deterministic**: same verdict every run (flaky bugs: a pinned, high reproduction rate, per above).
- [ ] **Fast**: seconds, not minutes.
- [ ] **Agent-runnable**: you can run it unattended; a human in the loop only via `scripts/hitl-loop.template.sh`.
If you catch yourself reading code to build a theory before this command exists, **stop: jumping straight to a hypothesis is the exact failure this skill prevents.** No red-capable command, no Phase 2.
## Phase 2: Reproduce + minimise
Run the loop. Watch it go red as the bug appears.
Confirm:
- [ ] The loop produces the failure mode the **user** described, not a different failure that happens to be nearby. Wrong bug = wrong fix.
- [ ] The failure is reproducible across multiple runs (or, for non-deterministic bugs, reproducible at a high enough rate to debug against).
- [ ] You have captured the exact symptom (error message, wrong output, slow timing) so later phases can verify the fix actually addresses it.
### Minimise
Once it's red, shrink the repro to the **smallest scenario that still goes red**. Cut inputs, callers, config, data, and steps **one at a time**, re-running the loop after each cut, and keep only what's load-bearing for the failure.
Why bother: a minimal repro shrinks the hypothesis space in Phase 3 (fewer moving parts left to suspect) and becomes the clean regression test in Phase 5.
Done when **every remaining element is load-bearing**: removing any one of them makes the loop go green.
Do not proceed until you have reproduced **and** minimised.
## Phase 3: Hypothesise
Generate **3–5 ranked hypotheses** before testing any of them. Single-hypothesis generation anchors on the first plausible idea.
Each hypothesis must be **falsifiable**: state the prediction it makes.
> Format: "If <X> is the cause, then <changing Y> will make the bug disappear / <changing Z> will make it worse."
If you cannot state the prediction, the hypothesis is a vibe: discard or sharpen it.
**Show the ranked list to the user before testing.** They often have domain knowledge that re-ranks instantly ("we just deployed a change to #3"), or know hypotheses they've already ruled out. Cheap checkpoint, big time saver. Don't block on it; proceed with your ranking if the user is AFK.
## Phase 4: Instrument
Each probe must map to a specific prediction from Phase 3. **Change one variable at a time.**
Tool preference:
1. **Debugger / REPL inspection** if the env supports it. One breakpoint beats ten logs.
2. **Targeted logs** at the boundaries that distinguish hypotheses.
3. Never "log everything and grep".
**Tag every debug log** with a unique prefix, e.g. `[DEBUG-a4f2]`. Cleanup at the end becomes a single grep. Untagged logs survive; tagged logs die.
**Perf branch.** For performance regressions, logs are usually wrong. Instead: establish a baseline measurement (timing harness, `performance.now()`, profiler, query plan), then bisect. Measure first, fix second.
## Phase 5: Fix + regression test
Write the regression test **before the fix**, but only if there is a **correct seam** for it.
A correct seam is one where the test exercises the **real bug pattern** as it occurs at the call site. If the only available seam is too shallow (single-caller test when the bug needs multiple callers, unit test that can't replicate the chain that triggered the bug), a regression test there gives false confidence.
**If no correct seam exists, that itself is the finding.** Note it. The codebase architecture is preventing the bug from being locked down. Flag this for the next phase.
If a correct seam exists:
1. Turn the minimised repro into a failing test at that seam.
2. Watch it fail.
3. Apply the fix.
4. Watch it pass.
5. Re-run the Phase 1 feedback loop against the original (un-minimised) scenario.
## Phase 6: Cleanup
Required before declaring done:
- [ ] Original repro no longer reproduces (re-run the Phase 1 loop)
- [ ] Regression test passes (or absence of seam is documented)
- [ ] All `[DEBUG-...]` instrumentation removed (`grep` the prefix)
- [ ] Throwaway prototypes deleted (or moved to a clearly-marked debug location)
- [ ] The hypothesis that turned out correct is stated in the commit / PR message, so the next debugger learns
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---
name: gitea
description: Provides information and workflows for interacting with Gitea.
license: MIT
compatibility: opencode
metadata:
audience: agents
workflow: gitea-development
---
## What Gitea is
Gitea is a self-hosted Git service that is very similar to GitHub.
## Important Notes
- **Do NOT use gitea.com**: We self-host Gitea at `git.nubo.sh`.
- **Workflow**: The workflow is much the same as GitHub (issues, PRs, etc.).
## How to use the `tea` CLI
Use the `tea` command to interact with Gitea.
- `tea notifications`: Check for new notifications.
- `tea issue <number>`: Read an issue.
- `tea issue --comments <number>`: Read an issue and its comments.
- `tea comment <number> "message"`: Comment on an issue.
- `tea pulls`: Manage pull requests.
- `tea pulls create`: Create a pull request.
- `tea pulls <number>`: Read a pull request (e.g., `tea pulls 6`).
- `tea pulls --comments <number>`: Read a pull request and its comments.
- **Marking as Read**: Use `tea notifications read` to mark notifications as read.
- **When to mark as read**: Only mark notifications as read once you have actively addressed the underlying task or review.
## Development Workflows
- **Issues**: Use `tea issue` to track tasks and bugs. Before closing an issue, ensure the relevant Pull Request has been merged.
- **Pull Requests**: Use `tea pulls` to manage and review code changes.
- **Communication**: Use `tea comment <number> "message"` to communicate with project maintainers (e.g., `@hulthe`).
## Responsibility
- **Don't Drop the Ball**: When assigned a task or receiving a review comment, you MUST address it before moving on to other things. Ensure all follow-up actions ( (like replying to a comment) are completed.
## Tips for Agents
- **Stay Updated**: Regularly run `tea notifications` to check for new issues or PR reviews.
- **Communicate**: If confused, use `tea comment <number> "message"` and mention the maintainer (e.g., `@hulthe`).
- **Clean History**: Use `git rebase` and squash commits before submitting a PR to keep the history clean.
- **Use Tea**: Use the `tea` CLI for a faster, terminal-based workflow.
- **Explicitly Ping**: When replying to a comment, use the `@` sign to explicitly ping or mention the person you are replying to. This ensures they receive a notification.
- **CI / Actions**: Gitea uses `.gitea/workflows/` (not `.github/`). Use `tea actions runs` to list runs; `tea actions runs logs <id>` to inspect. A `Status: completed` does NOT mean success — always read the log tail for `Job succeeded` or `Job failed`. The Alpine runner requires `node` before `actions/checkout` and uses package names `rust-clippy` / `rustfmt` (not `clippy`).
## Assignment
- **Tracking Assignments**: Use `tea issue --assignee <your-username>` to see issues assigned to you. Also use `tea pulls --assignee <your-username>` for pull requests.
- **Checking Work**: Regularly check your notifications and assigned issues to ensure you are up to date with your tasks.
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---
name: grilling
description: Grill the user relentlessly about a plan, decision, or idea. Use when the user wants to stress-test their thinking, or uses any 'grill' trigger phrases.
---
Interview the user relentlessly until you reach a shared understanding. Map this as a **design tree**: every decision branches into the decisions that hang off it.
Work the tree in **rounds**. The **frontier** is every decision whose prerequisites are already settled: the questions you can ask _now_ without guessing at answers you haven't heard yet. Ask the whole frontier in one round: number each question and give your recommended answer. Then wait for the user's answers before the next round.
Format a round like so:
```
❓ **Q1** - **<question title>**: <question body, might be multiple paragraphs, including multiple choices>
➡ <your recommended answer>
---
❓ **Q2** - **<question title>**: <question body, might be multiple paragraphs, including multiple choices>
➡ <your recommended answer>
```
Each round the user answers reshapes the tree: settled decisions push the frontier outward and unblock questions that depended on them. Recompute the frontier and ask the next round. A question whose answer depends on another question still open in this round belongs to a _later_ round, not this one.
Finding _facts_ is your job, never the user's. When a frontier question needs a fact from the environment (filesystem, tools, etc.), dispatch a sub-agent to find it; don't ask the user for anything you could look up yourself. Don't block on it: a running exploration is an unsettled prerequisite, so only the questions downstream of it wait for the sub-agent to report; ask the rest of the frontier now. The _decisions_ are the user's: put each to them and wait.
The session is done when the frontier is empty: every branch of the design tree visited, nothing left silently assumed. Do not act on it until the user confirms you have reached a shared understanding.
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---
name: rust-best-practices
description: >
Best-practices for Rust software. Load into a workstream whenever you need to read or write a Rust (.rs) file.
---
# Best practices for programming Rust
- Run `cargo check`, `cargo clippy`, and `cargo clippy` before comitting to git.
- Don't ignore warnings from cargo.
- Don't supress warnings using e.g. `#[allow(warning)]`
- Never use `unsafe` without asking the user for explicit permission.
When setting up a new rust project, enable these lints:
```toml
[lints.clippy]
allow_attributes = "warn"
as_ptr_cast_mut = "warn"
as_underscore = "warn"
borrow_as_ptr = "warn"
implicit_clone = "warn"
undocumented_unsafe_blocks = "warn"
unicode_not_nfc = "warn"
unused_async = "deny"
wildcard_dependencies = "deny"
[lints.rust]
absolute_paths_not_starting_with_crate = "deny"
explicit_outlives_requirements = "warn"
macro_use_extern_crate = "deny"
missing_abi = "deny"
non_ascii_idents = "forbid"
rust_2018_idioms = { level = "deny", priority = -1 }
single_use_lifetimes = "warn"
unused_lifetimes = "warn"
unused_macro_rules = "warn"
```
## Newtypes and "Parse, Don't Validate"
Use newtypes to make invalid states unrepresentable. Instead of passing raw strings and validating at every use site, parse once into a validated type.
### Pattern
```rust
pub struct Email(String); // private field prevents construction without validation
impl Email {
pub fn parse(s: &str) -> Result<Self, ParseError> {
if !s.contains('@') {
return Err(ParseError::InvalidEmail);
}
Ok(Self(s.to_owned()))
}
pub fn as_str(&self) -> &str {
&self.0
}
}
```
### Guidelines
- **Private fields**: Force construction through `parse()` only
- **Parse at boundaries**: Validate at system edges (API handlers, CLI args, config loading)
- **Use the type in signatures**: `fn create_user(email: Email)` not `fn create_user(email: &str)`
- **No re-validation**: Once parsed, the type guarantees validity
### When to Apply
- Email addresses, passwords, phone numbers
- File paths, URLs, identifiers
- Any domain value with invariants
- Configuration values requiring validation
### Anti-pattern to Avoid
```rust
// BAD: validation scattered, easy to forget
fn handler(email: &str) {
if !validate_email(email) { return Err(...); }
create_user(email) // still just &str, could be invalid
}
```