agentleFS
scolladonscolladon/craft/skills/review/SKILL.md

review

Skill2 starsChanged 34 days agoMITView on GitHub

Craft phase 6 - parallel multi-dimension review with per-dimension convergence; the session applies every fix. Also useful standalone on any branch.

192 other projects use a version of this.

Adopted by

Most recently taken up by

Used with

Projects with it also use close-spec, design-with-images and 10 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • dzhngSkill

    close-spec

    When a spec under specs/<feature>/ is done shipping (a write-spec build, or any planned task that produced a spec), archive it to specs/done/ and rewrite it from a build-plan into a durable rationale — the why, the principles, the invariants — pointing back to the real code for the how. Use when implementation has landed and the plan no longer matches what shipped, or the user says a feature/spec is finished. Pairs with [write-spec](../write-spec/SKILL.md) (the plan this closes) and [review](../review/SKILL.md) (run before closing).

    Also in 5 projects that use this

  • dzhngSkill

    design-with-images

    Explore UI and visual design with image generation, then iterate the real implementation against the user-selected concept. Use when brainstorming visual directions, offering mockup variants, or implementing an approved generated design; do not stop at the mockups or the first code approximation.

    Also in 5 projects that use this

  • LambenthanSkill

    daily-arxiv

    每日从 arXiv 拉取新论文,过滤相关性,auto-ingest 高优先级论文,检测 SOTA 更新

    Also in 5 projects that use this

  • LambenthanSkill

    exp-eval

    实验判决门:Review LLM 独立评判实验结果 → 4 种判决路径 → 自动更新 claims confidence、ideas status、graph edges

    Also in 5 projects that use this

  • +3 more from dzhng
  • +5 more from Lambenthan

The file

We split this skill into the rules it gives an agent

4 rules. 0 are common, 1 is distinctive, and 3 are very specific to this project.

  • 0

    Common

    Most of the 156 edited versions we read keep these.

  • 1

    Distinctive

    This author's own choices. Worth a look.

  • 3

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    MUST be stored repo-RELATIVE (strip the repoRoot prefix) — never an absolute path, which would leak $HOME/username into the committed store.

    line 24
  2. Very specific:

    Normalize findings: before applying, pipe each reviewer's raw output through node …

    line 47
  3. Very specific:

    After a compaction, a dimension with no FINDINGS line for the current cycle is re-spawned, because its reviewer output may have been lost …

    line 58
  4. Distinctive:

    BEFORE committing. Everything else: fix-all-then-converge, no user round-trip.

    line 88

“Common” is measured: kept by at least 6 in 10 of the 156 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

craft:review

Preamble (always runs — non-overridable)

  1. Manifest read (lint if standalone). Standalone: scope = current branch vs default branch; establish global-context preconditions (checkout root).
  2. Probe harness knobs from phase.harness (the resolved descriptor the orchestrator holds for this phase), each with a strong fallback when the knob is absent: dimensions (default code, security, tests, perf — a repo context: may refine their definitions); passes = reviewers per dimension (default 1); max_cycles (default 3); convergence (default low-only). Gates as in implementation's preamble; gates.review-batch optional extra.
  3. Memory read/write surface (advisory). READS: recurring findings entries as advisory watch-items — prepended to each reviewer spawn's injected block so reviewers check these locations first. A cached finding pre-empts re-discovery effort; it never replaces the full-diff review. WRITES (appended to the run record as produced; saved to the store once at Done): findings that recurred this run, keyed by file + pattern, with severity. file MUST be stored repo-RELATIVE (strip the repoRoot prefix) — never an absolute path, which would leak $HOME/username into the committed store. Per ADR-123 whitelist: no provenance refs, no code snippets, no prose explanation body, no PII. RETRACTS: the run retracts a findings entry when it re-checked that entry's own file + pattern at that location and the pattern is absent — a mechanical re-check, never a judgment call, and only for the concern this phase owns. Emit MEMORY-RETRACT(findings): <file> <pattern>, file repo-RELATIVE under the same rule the WRITES clause states above.

Procedure (default body — a manifest override: replaces everything below)

  1. Round 1 — full scope: fan out exactly phase.harness.reviewPlan.passes read-only craft:reviewer per dimension in parallel (one message, dimensions.length × reviewPlan.passes spawns). This count is engine-emitted and binding — the walk MUST spawn exactly that many reviewers per dimension, no more, no fewer. A resolved product above eight also lands an advisory record in the run record — it never changes this count. Each carries: its dimension + definition; the working directory; the diff scope; the design doc path (if any); global + review-phase context: files verbatim. Perf calibrates to the diff — zero findings legitimate. Tests dimension: do NOT run the executing-harness techniques (a dedicated phase owns it) — but suspected-benign harness findings MAY be flagged as advisory notes (keep them for the validation phase).
  2. Normalize findings: before applying, pipe each reviewer's raw output through node "${CRAFT_ROOT:-${CLAUDE_PLUGIN_ROOT}}/engine/bin/normalize-findings.js" to obtain a canonical Finding[] ({file, line, severity, finding, fix?, status?}). Key on these fields — never on whether the reviewer emitted a JSON array or a per-line list.
    • Once every pass of a dimension has returned for the cycle, write that dimension's merged canonical Finding[] — all its passes — in one Bash call to <dir>/<dimension>.c<cycle>.json, so a second pass never overwrites the first. <dir> is one mktemp -d "${TMPDIR:-/tmp}/craft-review.XXXXXX" per review phase, reused across cycles: out of tree, the same throwaway discipline as validation's $out.
    • Append FINDINGS(<dimension>): c<cycle> <path> n=<count> via "${CRAFT_ROOT:-${CLAUDE_PLUGIN_ROOT}}/scripts/run-ledger.sh" append <run-id> review.
    • After a compaction, a dimension with no FINDINGS line for the current cycle is re-spawned, because its reviewer output may have been lost before it was persisted.
    • Standalone (no craft run): skip the append; the file still lands.
  3. Fixes — session-owned: the actionable set is status ∈ {absent, VERIFIED, SUSPECT, PROBE} — engage each of these (apply the fix, or investigate and either fix it or record it as RULED-OUT). RULED-OUT is record-only: write it to the run record as "examined, not a defect" and drop it from the fix set. Apply every accepted actionable finding yourself, batched per dimension; each batch gates on the targeted checks (gates.part over touched files) + gates.review-batch before its conventional commit (e.g. refactor(<scope>): apply code-review fixes); gates.phase after the round. A finding whose locus is outside the review's diff scope is an impact, not an actionable finding: it gets its verdict under the run skill invariant Impacts: fold or defer at discovery.
  4. Converge per dimension, up to max_cycles cycles (default 3), per phase.harness.reviewPlan.stop_rule (engine-emitted, binding). Both rules below count only actionable findings (Step 3) — a RULED-OUT record never blocks convergence:
    • low-only → converged once only LOW-severity actionable findings remain; NO relaunch.
    • none → no convergence loop; single pass only.
    • non-low-count<=<n> → stop when the count of remaining non-LOW actionable findings (severity ≥ MEDIUM, off the normalized Finding[]) is ≤ n. The threshold n is read directly from the rule string — no re-derivation. MEDIUM+ → fresh reviewer scoped to the FIX DELTA only (prior findings + fix commits' diff; mission: verify resolutions + review the fix diff). The prior-findings payload carries RULED-OUT records too, labelled: do not re-raise a RULED-OUT claim unless the fix diff reintroduces the condition. This threaded payload is a bounded, status-tagged findings-state, never an accumulated transcript. Fresh agent each cycle — never continue a reviewer.
  5. Security gate: HIGH/CRITICAL security findings — show the user the fix diff BEFORE committing. Everything else: fix-all-then-converge, no user round-trip.
  6. Record per-dimension outcomes in the run record.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.