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 athola/claude-night-market --skill skills-evalgit clone --depth 1 https://github.com/athola/claude-night-marketWrote 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/athola/claude-night-market/skills-eval)<a href="https://agentmods.dev/skills/athola/claude-night-market/skills-eval"><img src="https://agentmods.dev/badge/skills/athola/claude-night-market/skills-eval/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/athola/claude-night-market/skills-eval"><img src="https://agentmods.dev/badge/skills/athola/claude-night-market/skills-eval.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00018 | $0.01503 |
| Opus 5 | $0.00009 | $0.00751 |
| Sonnet 5 | $0.00004 | $0.00301 |
| Haiku 4.5 | $0.00002 | $0.00150 |
Grade A, and why
skills-eval 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 9d 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.
How it starts
The opening of the file, as written. The whole thing — 169 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skills Evaluation and Improvement
When NOT To Use
- Writing a new skill (use
abstract:skill-authoring) - Evaluating hooks (use
abstract:hooks-eval) - Evaluating rules (use
abstract:rules-eval)
Table of Contents
Overview
This framework audits Claude skills against quality standards to improve performance and reduce token consumption. Automated tools analyze skill structure, measure context usage, and identify specific technical improvements. Run verification commands after each audit to confirm fixes work correctly.
The skills-auditor provides structural analysis, while the improvement-suggester ranks fixes by impact. Compliance is verified through the compliance-checker. Runtime efficiency is monitored by tool-performance-analyzer and token-usage-tracker.
Quick Start
Basic Audit
Run a full audit of all skills or target a specific file to identify structural issues.
# Audit all skills
make audit-all
# Audit specific skill
make audit-skill TARGET=path/to/skill/SKILL.md
Analysis and Optimization
Use skill_analyzer.py for complexity checks and token_estimator.py to verify the context budget.
make analyze-skill TARGET=path/to/skill/SKILL.md
make estimate-tokens TARGET=path/to/skill/SKILL.md
Improvements
Generate a prioritized plan and verify standards compliance using improvement_suggester.py and compliance_checker.py.
make improve-skill TARGET=path/to/skill/SKILL.md
make check-compliance TARGET=path/to/skill/SKILL.md
Evaluation Workflow
Start with make audit-all to inventory skills and identify high-priority targets. For each skill requiring attention, run analysis with analyze-skill to map complexity. Generate an improvement plan, apply fixes, and run check-compliance to verify the skill meets project standards. Finalize by checking the token budget for efficiency.
What ships with it
14 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.
- modules/advanced-tool-use-analysis.md 3.3 KB
- modules/authoring-checklist.md 2.5 KB
- modules/evaluation-criteria.md 14 KB
- modules/evaluation-framework.md 3.0 KB
- modules/evaluation-workflows.md 2.1 KB
- modules/integration-testing.md 9.6 KB
- modules/integration.md 3.3 KB
- modules/performance-benchmarking.md 16 KB
- modules/pressure-testing.md 16 KB
- modules/skill-authoring-best-practices.md 10.0 KB
- modules/trigger-isolation-analysis.md 4.8 KB
- modules/troubleshooting.md 7.4 KB
- README.md 769 B
- scripts/README.md 6.4 KB
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.
- 9d ago First seen · 169 lines · 18 tokens per session scan A 0b4e4874025f
skills-eval is a skill published in the GitHub repository athola/claude-night-market (337 stars, last pushed yesterday), licensed MIT. It adds 18 tokens to every session and 1,503 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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