Shapes turns action-first: action leads, steps numbered, state restated. Use for ADHD-friendly output. Do not use to trim tokens; use response-compression.
日本語の概要は準備中です。原文の説明を表示しています。
Runs a three-tier codebase audit (git history, targeted scans, full review) with gating. Use when auditing a codebase before release or after incidents.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Always runs first. Analyzes git log, diff stats, and blame to identify areas of concern without reading any source files.
Run the Tier 1 script for the target commit range (default: current branch against the trunk):
python3 "${CLAUDE_PLUGIN_ROOT}/scripts/tiered_audit.py" --base {base}
python3 "${CLAUDE_PLUGIN_ROOT}/scripts/tiered_audit.py" --base {base} --json
It runs four git queries (file churn, fix-on-fix commit
subjects, per-commit diff size, newly added files), applies
the thresholds in pensive.skills.tiered_audit (3+ files in
a module with one changed more than twice, 200+ changed lines
in one commit, 5+ new files in a module), and prints each
flag beside the [E{n}] Command: that produced it, so the
findings file can cite the evidence verbatim. --json adds
escalation_targets and an escalate verdict for Tier 2.
Verification: an empty report on a branch you know is
busy means the range is wrong. Check that {base} resolves
with git merge-base {base} HEAD before trusting a quiet
result.
Write findings to .coordination/agents/tier1-audit.findings.md:
---
agent: tier1-audit
tier: 1
evidence_count: {N}
---
## Summary
{1-2 sentence overview of what the git history reveals}
## Churn Hotspots
{top 10 most-changed files with change counts}
For each flagged file, include:
- Location: path/to/file.py:line (most-changed function or block)
- Anchor: `verbatim source text at that line`
[E1] Command: git log --format="" --name-only ...
Output: {relevant output}
## Fix-on-Fix Patterns
{commits that fix previous commits in the same area}
[E2] Command: git log --oneline ... | grep -iE ...
Output: {relevant output}
## New File Clusters
{modules with 5+ new files}
## Large Diffs
{commits with 200+ line changes}
## Escalation Recommendation
{list of areas flagged for Tier 2, or "no escalation needed"}
After Tier 1 completes, check findings against the
escalation criteria in modules/escalation-criteria.md.
If NO criteria are met: audit is complete. Report findings.
If criteria ARE met: list flagged areas and proceed to Tier 2 for each area sequentially.
Runs only for areas flagged by Tier 1. Each flagged area is audited one at a time, not in parallel.
For each flagged area:
One findings file per area:
.coordination/agents/tier2-{area-name}.findings.md
Each file follows the output contract for audits (see imbue:proof-of-work/modules/output-contracts).
Requires explicit user approval. See
modules/escalation-criteria.md for the gate protocol.
Tier 3 should use dedicated sessions (one per area) with file-based coordination, NOT parallel subagents.
All tiers use this contract:
output_contract:
required_sections:
- summary
- evidence
min_evidence_count: 3 # Tier 1
# min_evidence_count: 8 # Tier 2
expected_artifacts: []
retry_budget: 1
strictness: normal
Tier 2 raises the minimum evidence count to 8 because it reads source files and should produce deeper analysis.
Verification: After each tier completes, verify the
findings file exists and contains at least the minimum
evidence count ([E1], [E2], etc.) before proceeding
to the next tier or reporting results.
tiered-audit:findings-verified)Write findings to .review/findings.json, run the citation verifier
(Skill(imbue:review-core) Step 5), and drop or label UNVERIFIED any
the verifier rejects.
.coordination/agents/tier1-audit.findings.md and contains
at least 3 evidence entries ([E1]–[E3]).Location + verbatim Anchor
confirmed by citation_verifier.py (exit 0), or unverified
findings were dropped or labeled UNVERIFIED.まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Shapes turns action-first: action leads, steps numbered, state restated. Use for ADHD-friendly output. Do not use to trim tokens; use response-compression.
日本語の概要は準備中です。原文の説明を表示しています。
Inverts burden of proof for code additions. Use when reviewing PRs, planning refactors, or running unbloat to challenge every addition's necessity.
日本語の概要は準備中です。原文の説明を表示しています。
Tracks per-agent token usage and flags waste in parallel dispatch. Use when evaluating parallel agent efficiency or after a multi-agent run.
日本語の概要は準備中です。原文の説明を表示しています。
Coordinates Claude agent teams via filesystem protocol. Use when orchestrating parallel agents with task dependencies. Do not use for single-agent tasks.
日本語の概要は準備中です。原文の説明を表示しています。
Evaluates API surface design, consistency, and exemplar alignment. Use when reviewing public API changes or before releasing a new API surface.
日本語の概要は準備中です。原文の説明を表示しています。
Selects architecture paradigm via research before scaffolding. Use when architecture is undecided and the choice needs justification and documentation.
日本語の概要は準備中です。原文の説明を表示しています。