Shapes turns action-first: action leads, steps numbered, state restated. Use for ADHD-friendly output. Do not use to trim tokens; use response-compression.
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Runs a three-tier codebase audit (git history, targeted scans, full review) with gating. Use when auditing a codebase before release or after incidents.
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Run score Not tested
See how to installReview the instructions the agent will receive before you install.
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
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Shapes turns action-first: action leads, steps numbered, state restated. Use for ADHD-friendly output. Do not use to trim tokens; use response-compression.
The English summary is being prepared. Showing the original description.
Inverts burden of proof for code additions. Use when reviewing PRs, planning refactors, or running unbloat to challenge every addition's necessity.
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Tracks per-agent token usage and flags waste in parallel dispatch. Use when evaluating parallel agent efficiency or after a multi-agent run.
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Coordinates Claude agent teams via filesystem protocol. Use when orchestrating parallel agents with task dependencies. Do not use for single-agent tasks.
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Evaluates API surface design, consistency, and exemplar alignment. Use when reviewing public API changes or before releasing a new API surface.
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Selects architecture paradigm via research before scaffolding. Use when architecture is undecided and the choice needs justification and documentation.
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