dual-axis-skill-reviewer

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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/run_dual_axis_review.py
Global install: ~/.claude/skills/dual-axis-skill-reviewer/scripts/run_dual_axis_review.py

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

bash

# 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

bash

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

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

bash

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/skill_review_prompt_<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

bash

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/skill_review_<skill>_<timestamp>.json
reports/skill_review_<skill>_<timestamp>.md
reports/skill_review_prompt_<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, not_verified gaps, non-applicable axes, and all_applicable_axes_passed. 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/:

bash

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/run_dual_axis_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/run_dual_axis_review.py
LLM schema: references/llm_review_schema.md
Rubric detail: references/scoring_rubric.md