Dual axis skill reviewer

Skill BaggaT236/AI-Trading-Skills/skills/dual-axis-skill-reviewer

Ai agent claude trading skills for disiplined, repeatable trading workflows with a modern typescript platform

Install

npx -y skills add BaggaT236/AI-Trading-Skills --skill dual-axis-skill-reviewer

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What its author says it does

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Review skills in any project using a dual-axis method: (1) deterministic code-based checks (structure, scripts, tests, execution safety) and (2) LLM deep review findings. Use when you need reproducible quality scoring for `skills/*/SKILL.md`, want to gate merges with a score threshold (for example 90+), or need concrete improvement items for low-scoring skills. Works across projects via --project-root.

SKILL.md

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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

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:

# 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/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

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)

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/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