Dual Axis Skill Reviewer logo

Dual Axis Skill Reviewer

CommunityPopular
tradermonty
dual-axis-skill-reviewer

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.

Overview

Publishertradermonty
Repositoryclaude-trading-skills
Skill namedual-axis-skill-reviewer
Stars
2.8K
Forks
647
Bundled files
6
LicenseMIT
Links
Skill sourceRepositoryHomepage
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 6 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by tradermonty on GitHub. Read the source before you install it.

Installation

Install the Dual Axis Skill Reviewer AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/tradermonty/claude-trading-skills.git /tmp/claude-trading-skills
mkdir -p .claude/skills
cp -r /tmp/claude-trading-skills/skills/dual-axis-skill-reviewer .claude/skills/dual-axis-skill-reviewer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Dual Axis Skill Reviewer in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Dual Axis Skill Reviewer on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Dual Axis Skill Reviewer is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Dual Axis Skill Reviewer AI skill do?

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.

Why use Dual Axis Skill Reviewer on TypingMind?

Because you install it once and use it with any model. Dual Axis Skill Reviewer is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Dual Axis Skill Reviewer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/tradermonty/claude-trading-skills/tree/main/skills/dual-axis-skill-reviewer. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Dual Axis Skill Reviewer?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Dual Axis Skill Reviewer?

As many as you like. As long as a model supports skills, you can use Dual Axis Skill Reviewer with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Dual Axis Skill Reviewer AI skill free?

Yes. It is published on GitHub by tradermonty under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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