claude-reviewer

name	claude-reviewer
description	Review any project's .claude/ setup against Claude Code best practices and produce an actionable improvement report. Use when: "/claude-reviewer", "review my claude config", "audit .claude setup", "check my CLAUDE.md", "how good is my claude config", "rate this project's claude setup". NOT for: reviewing individual skill quality (use /skill-reviewer), code review, PR review.
user-invocable	true
argument-hint	[--global] [--verbose] [--with-logs] [project-path]
allowed-tools	Read, Glob, Grep, Bash(python3*), Bash(git*)

/claude-reviewer - Claude Code Configuration Auditor

Review any project's .claude/ folder against best practices from Anthropic's Claude Code configuration guidance.

How It Differs from /skill-reviewer

	/skill-reviewer	/claude-reviewer
Input	A single skill directory	An entire project's .claude/ folder
Evaluates	Skill design quality (SKILL.md, scripts, references)	Config hygiene (CLAUDE.md, settings.json, rules/, commands/, skills/, agents/)
When	After building a skill	When setting up or auditing a project
Output	10-dimension skill score (max 29)	8-dimension config score (max 24)

Arguments

/claude-reviewer                      -> Review current working directory
/claude-reviewer ~/Code/myproject     -> Review specific project
/claude-reviewer --global             -> Review ONLY ~/.claude/ global config
/claude-reviewer --verbose            -> Include evidence quotes in report
/claude-reviewer --with-logs          -> Add transcript-based "unused skills" detection

$ARGUMENTS = [--global] [--verbose] [--with-logs] [project-path]

Parse the argument before starting:

• Extract --global flag if present.
• Extract --verbose flag if present. When set, include evidence quotes.
• Extract --with-logs flag if present. Forward it to scripts/fleet_audit.py in Phase 4 Step 1 for opt-in transcript scanning (unused-skill detection).
• Remaining token is the project path.
• Routing logic:
  • --global → Global mode: audit only ~/.claude/. Score 5 applicable dimensions (D1, D2, D4, D5, D6 — max 15). Ignore any project path. Use the Global Configuration report template only.
  • No --global → Project mode: audit the project path (or cwd if none given). Score all 8 dimensions (max 24). No global section.
• Ask the user for confirmation before reading home directory contents when --global is used.
• If the path doesn't exist, report "Directory not found: [path]."
• If the path has no .claude/ directory AND no CLAUDE.md (project mode only), report "No Claude Code configuration found at [path]."

Workflow

Phase 1: Structural Scan

Run the deterministic scanner:

python3 scripts/scan.py <project-path> --json [--global]

If the scanner fails (non-zero exit, invalid JSON), stop and report the error to the user. Do not attempt to score manually without scanner output.

This returns JSON with:

• Full inventory of all .claude/ components
• Structural scores for D1, D2, D3, D4, D7 (and partial D5, D6, D8)
• Anti-pattern detection
• CLAUDE.md content analysis (section headings, linter content, theory paragraphs)

Parse the JSON output. Use the structural scores directly for D1, D2, D3, D4, D7. Dimensions with score: null require your semantic evaluation in Phase 2.

Phase 2: Semantic Evaluation

For dimensions the scanner cannot fully score:

D5: Skills Setup — Read each skill's SKILL.md. Does the description explain when to trigger AND when NOT to use? Are allowed-tools minimally scoped (no bare Bash)? Does the skill use progressive disclosure (references/, scripts/)? Is the description trigger-focused, not generic?

D6: Agent Configuration — Read each agent file. Is the model selection appropriate? (haiku for read-only, sonnet for balanced, opus for complex.) Are tools minimally scoped? Does each agent have a clear, distinct purpose?

D8: Progressive Disclosure — Holistic judgment: does CLAUDE.md serve as a lean hub that delegates to .claude/ components? Are path-scoped rules used instead of cramming everything into CLAUDE.md? Is the overall structure proportional to the project's complexity?

Also review D3 and D4 for semantic quality the scanner cannot assess:

• D3: Are rule files each focused on a single concern? (Scanner caps at 2; bump to 3 if quality is excellent.)
• D4: Are commands genuinely useful team workflows? (Scanner caps at 2; bump to 3 if commands are high-value.)

Adjust a structural score only if you have specific evidence. Note the reason.

Phase 3: Detect Anti-Patterns

The scanner auto-detects structural anti-patterns. Add semantic ones:

• CLAUDE.md contains content that belongs in linter/formatter configs
• CLAUDE.md has long theory paragraphs (instruction drift)
• Rules files try to cover multiple concerns
• Commands missing $ARGUMENTS for parameterization
• Skills with generic descriptions that won't trigger correctly
• Agents with overly broad tool access

Phase 4: Generate Report

Step 1 — Fleet skill-cleaner audit. Run python3 scripts/fleet_audit.py <project-path> (or --global in global mode; append --with-logs if the user passed it). Include the script's output verbatim under ## Skill Fleet Audit, placed between Inventory and Scores in the report. The four signals — fleet budget, description trim candidates, duplicates across roots, opt-in unused — are informational; they do not affect the rubric score, but heavy/duplicate findings should surface in Top 3 Improvements where actionable, and per-skill trims flow through /skill-reviewer --fix <skill>. Methodology: skill-cleaner by @steipete.

Step 2 — Fill the report template. Read references/report-template.md and fill the appropriate sections.

Project mode: Score all 8 dimensions (D1-D8, max 24). Grade: 21-24=A, 16-20=B, 11-15=C, 6-10=D, 0-5=F.

Global mode: Use only the Global Configuration section of the template. Score 5 dimensions (D1, D2, D4, D5, D6 — max 15). Grade: 13-15=A, 10-12=B, 7-9=C, 4-6=D, 0-3=F.

Phase 5: Actionable Recommendations

For each dimension scoring below 2, provide a specific, actionable recommendation. Prioritize by expected impact (points gained per effort).

Always include Quick Wins — concrete items achievable in under 5 minutes.

Scoring Dimensions Summary

Dim	Name	Max	Scanner	Claude
D1	CLAUDE.md Quality	3	Partial (length, sections, linter content)	Content quality, conciseness judgment
D2	Permission Hygiene	3	Full	—
D3	Modular Instructions	3	Partial (existence, count, path scoping)	Single-concern quality, delegation
D4	Custom Commands	3	Partial (frontmatter, $ARGUMENTS, backtick)	Usefulness, workflow coverage
D5	Skills Setup	3	Structural signals only	Trigger quality, tool scoping, disclosure
D6	Agent Configuration	3	Structural signals only	Model selection, tool scoping, purpose
D7	Git Hygiene	3	Full	—
D8	Progressive Disclosure	3	Partial (component count)	Holistic organization quality

References

• references/best-practices.md — 8-dimension scoring rubric with anchors
• references/report-template.md — output format template
• scripts/scan.py — structural scanner (D1-D4, D7 full; D5, D6, D8 partial)
• scripts/fleet_audit.py — fleet-level skill-cleaner signals (Phase 4 Step 1): budget, trim candidates, duplicates, optional unused detection
• examples/sample-review.md — complete example report for calibration

Gotchas

Projects without .claude/ folder: Some projects use only a root CLAUDE.md with no .claude/ directory. This is valid for simple projects. Score D2-D6 as 0 but note "Minimal configuration — appropriate for simple/early-stage projects" rather than treating it as a failure.

Monorepo CLAUDE.md files: Some projects have CLAUDE.md at multiple levels (root, packages/, apps/). Only score the root-level one for D1. Note nested files as a strength.

Git operations require a git repo: scan.py's git checks fail on non-git directories. It handles this gracefully (skips git checks, scores D7 as N/A).

settings.local.json vs settings.json: The local file should be gitignored; the non-local file should be committed. Don't confuse them.

Empty .claude/ directories: Some projects create .claude/ but leave it empty (only settings.local.json from plugin installs). Treat as "no configuration" for scoring.

Global scope requires confirmation: When --global is used, ask the user before reading ~/.claude/ contents. Home directory contents are personal.