Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add BaggaT236/AI-Trading-Skills --skill dual-axis-skill-reviewergit clone --depth 1 https://github.com/BaggaT236/AI-Trading-SkillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/baggat236/ai-trading-skills/dual-axis-skill-reviewer)<a href="https://agentmods.dev/skills/baggat236/ai-trading-skills/dual-axis-skill-reviewer"><img src="https://agentmods.dev/badge/skills/baggat236/ai-trading-skills/dual-axis-skill-reviewer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/baggat236/ai-trading-skills/dual-axis-skill-reviewer"><img src="https://agentmods.dev/badge/skills/baggat236/ai-trading-skills/dual-axis-skill-reviewer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00096 | $0.01109 |
| Opus 5.5 | $0.00038 | $0.00444 |
| Sonnet 5.5 | $0.00019 | $0.00222 |
| Haiku 4.5 | $0.00010 | $0.00111 |
Grade A, and why
dual-axis-skill-reviewer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
92% identical to dual-axis-skill-reviewer — 6 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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-resolvespyyamldependency via inline metadata)- For tests:
uv sync --extra devor 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.
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 119 lines · 96 tokens per session scan A da0e092ea5b7
dual-axis-skill-reviewer is a skill published in the GitHub repository BaggaT236/AI-Trading-Skills (138 stars, last pushed 1mo ago), licensed MIT. It adds 96 tokens to every session and 1,109 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 92% identical to dual-axis-skill-reviewer, differing in 6 lines, and is treated as a copy.
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