Dual Axis Skill Reviewer

Run the dual-axis reviewer script and save reports to reports/.

The script supports:

• Random or fixed skill selection
• Auto-axis scoring with optional test execution
• LLM prompt generation
• LLM JSON review merge with weighted final score
• Cross-project review via --project-root
• Non-scoring display of skills-index.yaml production verification declarations

When to Use

• Need reproducible scoring for one skill in skills/*/SKILL.md.
• Need improvement items when final score is below 90.
• Need both deterministic checks and qualitative LLM code/content review.
• Need to review skills in a different project from the command line.

Prerequisites

• Python 3.9+
• uv (recommended — auto-resolves pyyaml dependency via inline metadata)
• For tests: uv sync --extra dev or equivalent in the target project
• For LLM-axis merge: JSON file that follows the LLM review schema (see Resources)

Workflow

Determine the correct script path based on your context:

• Same project: skills/dual-axis-skill-reviewer/scripts/rundualaxis_review.py
• Global install: ~/.claude/skills/dual-axis-skill-reviewer/scripts/rundualaxis_review.py

The examples below use REVIEWER as a placeholder. Set it once:

# If reviewing from the same project:
REVIEWER=skills/dual-axis-skill-reviewer/scripts/run_dual_axis_review.py

# If reviewing another project (global install):
REVIEWER=~/.claude/skills/dual-axis-skill-reviewer/scripts/run_dual_axis_review.py

Step 1: Run Auto Axis + Generate LLM Prompt

uv run "$REVIEWER" \
  --project-root . \
  --emit-llm-prompt \
  --output-dir reports/

When reviewing a different project, point --project-root to it:

uv run "$REVIEWER" \
  --project-root /path/to/other/project \
  --emit-llm-prompt \
  --output-dir reports/

Step 2: Run LLM Review

• Use the generated prompt file in reports/skillreviewprompt<skill><timestamp>.md.
• Ask the LLM to return strict JSON output.
• When running inside Claude Code, let Claude act as orchestrator: read the generated prompt, produce the LLM review JSON, and save it for the merge step.

Step 3: Merge Auto + LLM Axes

uv run "$REVIEWER" \
  --project-root . \
  --skill <skill-name> \
  --llm-review-json <path-to-llm-review.json> \
  --auto-weight 0.5 \
  --llm-weight 0.5 \
  --output-dir reports/

Step 4: Optional Controls

• Fix selection for reproducibility: --skill <name> or --seed <int>
• Review all skills at once: --all
• Skip tests for quick triage: --skip-tests
• Change report location: --output-dir <dir>
• Increase --auto-weight for stricter deterministic gating.
• Increase --llm-weight when qualitative/code-review depth is prioritized.

Output

• reports/skillreview<skill>_<timestamp>.json
• reports/skillreview<skill>_<timestamp>.md
• reports/skillreviewprompt<skill><timestamp>.md (when --emit-llm-prompt is enabled)

When the target project has a skills-index.yaml entry with a complete verification block, JSON and Markdown reports also show its declared axes, notverified gaps, non-applicable axes, and allapplicableaxespassed. This section is informational only. It is excluded from auto/LLM/final scores and does not replace a live high-severity issue check.

Installation (Global)

To use this skill from any project, symlink it into ~/.claude/skills/:

ln -sfn /path/to/claude-trading-skills/skills/dual-axis-skill-reviewer \
  ~/.claude/skills/dual-axis-skill-reviewer

After this, Claude Code will discover the skill in all projects, and the script is accessible at ~/.claude/skills/dual-axis-skill-reviewer/scripts/rundualaxis_review.py.

Resources

• Auto axis scores metadata, workflow coverage, execution safety, artifact presence, and test health.
• Auto axis detects knowledge_only skills and adjusts script/test expectations to avoid unfair penalties.
• LLM axis scores deep content quality (correctness, risk, missing logic, maintainability).
• Final score is weighted average.
• If final score is below 90, improvement items are required and listed in the markdown report.
• Script: skills/dual-axis-skill-reviewer/scripts/rundualaxis_review.py
• LLM schema: references/llmreviewschema.md
• Rubric detail: references/scoring_rubric.md