review-capture

name

review-capture

description

Wrapper skill that invokes any review skill/command and captures its findings into a structured YAML drop file under .claude/review-drops/<branch>/. Use when running parallel code reviews across multiple terminals (each terminal captures one reviewer's output) so a later /merge-reviews pass can dedup, verify, and consolidate before execution. Accepts any target review skill (e.g. ce:review, multi-reviewer-patterns, asm-forge, review-pipeline, perf-pipeline, cache-correctness-review, security-reviewer, etc.).

review-capture

Turn any review skill into a drop-file producer. Delegate to the target skill fully, then extract its findings into a canonical YAML drop under .claude/review-drops/<branch>/ so a later merge phase can consolidate across all reviewers.

When to Use

• Running N parallel review terminals, each with a different skill.
• Want a single canonical artifact per review for later aggregation.
• Need a historical record of what a review produced and what HEAD looked like at the time.

When NOT to Use

• Running a single review and fixing immediately. Use the skill directly.
• The target skill mutates code in-band and cannot be run read-only. Do not wrap such skills — capture the fixes via git instead.

Invocation

/review-capture <target-skill> [target-args...]

Examples:

/review-capture ce:review
/review-capture asm-forge src/fs_cache_handlers.rs::read_header
/review-capture perf-pipeline
/review-capture cache-correctness-review
/review-capture review-pipeline --no-execute

Procedure

Follow these steps in order. Do not skip steps even if the target skill produces no findings.

1. Resolve drop directory and snapshot HEAD

BRANCH=$(git rev-parse --abbrev-ref HEAD)
DROP_DIR=".claude/review-drops/${BRANCH}"
mkdir -p "${DROP_DIR}"
HEAD_SHA=$(git rev-parse HEAD)
HEAD_SHORT=$(git rev-parse --short HEAD)
TIMESTAMP=$(date -u +%Y%m%dT%H%M%SZ)
# Record for use in step 5 — do not parse these from logs later.

If not in a git repo, abort and tell the user. The branch name is the merge-key — merges are per-branch by design.

2. Capture the target identity

• TARGET_SKILL: the first arg the user passed (e.g. ce:review, asm-forge, perf-pipeline).
• TARGET_ARGS: everything after the target-skill name.
• SOURCE_SLUG: TARGET_SKILL lowercased with : and / replaced by - (e.g. ce:review → ce-review). Used in the output filename.

3. Delegate to the target skill

Invoke the target skill via the Skill tool. Pass TARGET_ARGS as its args. Let it run to completion — follow its instructions fully, including any sub-agents, tool calls, or verification steps it performs.

Rules during delegation:

• Do NOT short-circuit the target skill's behavior. If it wants to spawn 6 review sub-agents, let it. If it wants to run benchmarks, let it.
• Do NOT filter the target skill's output. Its full reasoning trail is preserved in target_raw_output (step 5) for audit.
• Do NOT execute any fixes, even if the target skill suggests them. /review-capture is capture-only. Execution is a separate phase.
• If the target skill normally both reviews and executes (e.g. review-pipeline), ask the user whether to pass a --no-execute or equivalent flag before delegating, or note in the drop file that execution steps were skipped.

4. Extract findings into the canonical schema

After the target skill completes, read back everything it produced in this conversation (your own messages containing its output, any tool results, any sub-agent reports). Extract structured findings using the schema below.

Extraction rules:

• Each discrete finding/observation/recommendation = one entry under findings:.
• kind discriminates the shape — use one of: code_review, perf_regression, codegen_observation, benchmark_result, design_concern, test_gap, documentation_gap, other. If none fit, use other and put everything relevant in claim + details.
• Preserve file paths, line numbers, function names, and metric measurements exactly as the target skill produced them. Do not reinterpret or round.
• If the target skill produced prose without clear finding boundaries, split on paragraphs or logical units. When in doubt, one finding per distinct claim.
• If the target skill produced nothing actionable, still write the drop file with findings: [] — that's a valid capture result.

5. Write the drop file

Path: ${DROP_DIR}/${SOURCE_SLUG}-${HEAD_SHORT}-${TIMESTAMP}.yaml

Canonical schema:

# Envelope
source_skill: <e.g. ce:review>
source_slug: <e.g. ce-review>
target_args: [<e.g. "src/cache.rs">]
head_sha: <full SHA>
head_short: <short SHA>
branch: <branch name>
captured_at: <ISO-8601 UTC>
captured_by: review-capture
notes: |
  Free-text notes from this capture run — e.g. "target skill asked for
  manual sub-agent intervention, user confirmed X". Empty string if none.

# Full target output preserved verbatim for audit and re-extraction if
# the extracted findings prove incomplete.
target_raw_output: |
  <entire target skill output, with line-leading pipe preserved>

# Structured findings. Each entry MUST have `id`, `kind`, `severity`,
# `claim`. Other fields are kind-specific.
findings:
  - id: <source-slug>-001
    kind: code_review
    severity: critical|high|medium|low|info
    file: src/cache.rs
    line_start: 412
    line_end: 418
    claim: "Holds mutex across clone() of potentially-large Entry"
    code_quote: |
      let guard = self.lock.lock().unwrap();
      let entry = self.map.get(&key).cloned();
      drop(guard);
      entry
    suggested_fix: "Scope the clone inside a block; drop guard before return"
    rationale: "Lock contention spikes under hot-key fanout"

  - id: <source-slug>-002
    kind: perf_regression
    severity: high
    benchmark: cache_io/read_hit
    baseline_ns: 1240
    current_ns: 1580
    delta_pct: 27.4
    significance: "p<0.01"
    suggested_fix: "Investigate the hash re-computation added in HEAD"
    claim: "read_hit regressed 27% vs baseline with high significance"

  - id: <source-slug>-003
    kind: codegen_observation
    severity: medium
    function: fs_cache_handlers::read_header
    file: src/fs_cache_handlers.rs
    line_start: 240
    line_end: 262
    hot_instruction: "ldr x8, [x0, #16]"
    issue: "Unaligned 8-byte load in hot loop"
    suggested_fix: "Reorder struct to align header pointer to 16 bytes"
    claim: "Hot loop contains unaligned load that inhibits vectorization"

  - id: <source-slug>-004
    kind: test_gap
    severity: medium
    file: src/cache.rs
    line_start: 890
    line_end: 905
    claim: "Stale-while-revalidate branch has no test coverage"
    suggested_fix: "Add integration test covering SWR path with upstream timeout"

Field conventions:

• severity: use the target skill's severity verbatim if it assigned one. Otherwise infer: critical (data loss, deadlock, security hole), high (perf regression > 10%, correctness bug in hot path), medium (minor bug, design concern), low (style, naming), info (observation only).
• id: <source-slug>-NNN zero-padded to 3 digits, numbered in the order they appear in the target output.
• file, line_start, line_end: only include if the finding is anchored to specific code. Omit for benchmark/design-level findings.
• code_quote: verbatim copy from HEAD for anchored findings. The merge phase uses this to detect stale findings.
• suggested_fix: the action the target skill recommends. Omit if the finding is informational only.

6. Report back

Print a concise summary to the user:

Captured <N> findings from <target-skill> at <HEAD_SHORT>
Drop file: .claude/review-drops/<branch>/<filename>.yaml

Severity breakdown: critical=<n> high=<n> medium=<n> low=<n> info=<n>

Other captures in this branch:
  - <existing-drop-file-1>.yaml (<source>, <N> findings)
  - <existing-drop-file-2>.yaml (<source>, <N> findings)

Run /merge-reviews when all review terminals have finished.

List sibling drop files so the user knows which reviewers have already captured.

Rules

Do NOT execute fixes

This skill captures. It never modifies code, writes to files outside ${DROP_DIR}, or applies fixes. If the target skill attempted to apply fixes, note it in notes: — do not let those fixes land through this skill.

Preserve raw output

target_raw_output is mandatory. Even when extraction seems complete, the raw output is the ground truth for audit and for re-extraction if a future tool version improves parsing.

SHA at capture time, not at extraction time

head_sha reflects HEAD when the capture started, not when extraction finished. If the user commits mid-run the capture is still a valid snapshot of the earlier state. The merge phase re-verifies against current HEAD and flags drift.

One drop file per invocation

Never append to an existing drop file. Each /review-capture invocation produces exactly one new file. Timestamps in filenames ensure no collisions between concurrent terminals.

Do not spawn sub-agents yourself

The target skill may spawn sub-agents as part of its own workflow. /review-capture itself does not spawn any — it runs in the current conversation, delegates, reads back output, writes one file, exits.

Failure modes

• Target skill not found or refused: report the error, write no drop file, exit.
• Target skill ran but produced no findings: write the drop file with findings: [] and notes: "no findings produced". This is a valid outcome — merge phase will ignore empty drops.
• Not in a git repo: abort with a clear message; this skill requires a branch for drop directory routing.
• Extraction ambiguous: when output is unstructured prose, extract conservatively (one finding per distinct claim). Record in notes: that extraction was best-effort and recommend reviewing target_raw_output directly.