full-repo-review
Comprehensive 3-wave review of all repo source files, producing a prioritized issue backlog.
Best use case
full-repo-review is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Comprehensive 3-wave review of all repo source files, producing a prioritized issue backlog.
Teams using full-repo-review should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/full-repo-review/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How full-repo-review Compares
| Feature / Agent | full-repo-review | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Comprehensive 3-wave review of all repo source files, producing a prioritized issue backlog.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# Full-Repo Review : Codebase Health Check
Orchestrates a comprehensive 3-wave review against ALL source files in the
repository, not just changed files. Delegates the actual review to the
`comprehensive-review` skill. Produces a prioritized issue backlog instead of
auto-fixes.
**When to use**: Quarterly health checks, after major refactors, onboarding to
a new codebase, or any time you want a systemic view of codebase quality. This
is expensive (all files through all waves) -- use `comprehensive-review` for
PR-scoped work.
**How it differs from comprehensive-review**: This skill changes the SCOPE
phase to scan all source files instead of git diff, and changes the output from
auto-fix to a prioritized backlog report. The review waves themselves are
identical.
---
## Instructions
### Options
- **--directory [dir]**: Review only a single directory (e.g., `scripts/`) instead of the full repo. Useful for splitting a large repo into manageable chunks.
- **--skip-precheck**: Skip the `score-component.py` deterministic pre-check. Only use if the script is unavailable or you need faster iteration.
- **--min-severity [level]**: Only include findings at or above a severity threshold (CRITICAL, HIGH, MEDIUM) in the report. Default: include all.
---
### Phase 1: DISCOVER AND PRE-CHECK
**Goal**: Identify all source files and run deterministic health checks.
**Step 1: Discover source files**
Build the complete file list by scanning these directories. Always scan ALL
source files -- never fall back to git diff. The entire point of this skill is
codebase-wide coverage. If a specific `--directory` was provided, scope the
scan to that directory only.
```bash
# Python scripts (exclude test files and __pycache__)
find scripts/ -name "*.py" -not -path "*/tests/*" -not -path "*/__pycache__/*" 2>/dev/null
# Hooks (exclude test files and lib/)
find hooks/ -name "*.py" -not -path "*/tests/*" -not -path "*/lib/*" 2>/dev/null
# Skills (SKILL.md files only)
find skills/ -name "SKILL.md" 2>/dev/null
# Agents
find agents/ -name "*.md" 2>/dev/null
# Docs
find docs/ -name "*.md" 2>/dev/null
```
Log the total file count. If zero files found, STOP and report: "No source files discovered. Verify you are in the correct repository root."
If the file count is too large for a single session, split by directory
(`scripts/`, `hooks/`, `agents/`, `skills/` separately) rather than
cherry-picking "important" files -- selective review defeats the purpose.
**Step 2: Run deterministic pre-check**
Run scoring before the LLM review. Deterministic checks are cheap and catch
structural issues (missing frontmatter, no error handling section) that LLM
reviewers should not waste tokens rediscovering.
```bash
python3 ~/.claude/scripts/score-component.py --all-agents --all-skills --json
```
Parse the JSON output. Flag any component scoring below 60 (grade F) as a
CRITICAL finding for the final report. Components scoring 60-74 (grade C) are
HIGH findings.
Save the raw scores -- they go into the report's "Deterministic Health Scores"
section.
**GATE**: At least one source file discovered AND score-component.py ran
successfully. If the scoring script fails, proceed with a warning but do not
skip the review phase.
---
### Phase 2: REVIEW
**Goal**: Run the comprehensive-review pipeline against all discovered files.
This skill orchestrates scope and output only. The actual 3-wave review is
performed by `comprehensive-review` with `--review-only` mode.
**Step 1: Invoke comprehensive-review**
Invoke the `comprehensive-review` skill with these overrides:
- **Scope**: Pass the full file list from Phase 1 (use `--focus [files]` mode)
- **Mode**: Use `--review-only` to skip auto-fix. Output is a prioritized backlog for human triage, not patches -- full-repo auto-fix touches too many files at once and risks cascading breakage.
- **All waves**: Do NOT use `--skip-wave0` or `--wave1-only`. Full-repo review needs maximum coverage. Wave 0 per-package context is what makes full-repo review valuable; deterministic checks catch structure, but only the full 3-wave review catches logic and design issues.
The comprehensive-review skill handles Wave 0 (per-package), Wave 1 (foundation agents), and Wave 2 (deep-dive agents) internally.
**Step 2: Collect findings**
After comprehensive-review completes, gather all findings from its output. Each finding should have:
- **File**: path and line number
- **Severity**: CRITICAL / HIGH / MEDIUM / LOW
- **Category**: security, architecture, dead-code, naming, etc.
- **Description**: what the issue is
- **Suggested fix**: how to resolve it
**GATE**: comprehensive-review completed and produced findings output. If it
failed, include what partial findings exist and note the failure in the report.
---
### Phase 3: REPORT
**Goal**: Aggregate all findings into a prioritized backlog report.
**Step 1: Merge deterministic and LLM findings**
Combine:
- Phase 1 score-component.py results (structural health)
- Phase 2 comprehensive-review findings (deep analysis)
Deduplicate where both sources flag the same issue. Keep the higher severity.
**Step 2: Identify systemic patterns**
Look for patterns that appear in 3+ files:
- Repeated naming violations
- Consistent missing error handling
- Common anti-patterns across components
- Documentation gaps that follow a pattern
These go into a dedicated "Systemic Patterns" section -- they represent the
highest-leverage fixes because one pattern change improves many files.
**Step 3: Write the report**
Write `full-repo-review-report.md` to the repo root with this structure:
```markdown
# Full-Repo Review Report
**Date**: {date}
**Files reviewed**: {count}
**Total findings**: {count} (Critical: N, High: N, Medium: N, Low: N)
## Deterministic Health Scores
| Component | Score | Grade | Key Issues |
|-----------|-------|-------|------------|
| {name} | {n} | {A-F} | {summary} |
## Critical (fix immediately)
- **{file}:{line}** : [{category}] {description}
- Fix: {suggested fix}
## High (fix this sprint)
- ...
## Medium (fix when touching these files)
- ...
## Low (nice to have)
- ...
## Systemic Patterns
- **{pattern name}**: Seen in {N} files. {description}. Fix: {approach}.
## Review Metadata
- Waves executed: 0, 1, 2
- Duration: {time}
- Score pre-check: {pass/warn/fail}
```
The report is the final output. Do not auto-apply any fixes -- the user triages
findings and batches corrections into manageable PRs.
**GATE**: Report file exists at `full-repo-review-report.md` and contains at
least the severity sections and deterministic scores.
---
## Error Handling
| Error | Cause | Solution |
|-------|-------|----------|
| No source files found | Wrong working directory or empty repo | Verify cwd is repo root with `ls agents/ skills/ scripts/` |
| score-component.py fails | Missing script or dependency | Proceed with warning; the LLM review still runs. Note gap in report. |
| comprehensive-review times out | Too many files for single session | Split into directory-scoped runs: scripts/, hooks/, agents/, skills/ separately |
| Report write fails | Permission or path issue | Try writing to `/tmp/full-repo-review-report.md` as fallback |
---
## References
- [Report Template](references/report-template.md) -- Full structure for `full-repo-review-report.md` outputRelated Skills
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