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 vinnie357/claude-skills --skill claude-skills-benchmarkgit clone --depth 1 https://github.com/vinnie357/claude-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/vinnie357/claude-skills/claude-skills-benchmark)<a href="https://agentmods.dev/skills/vinnie357/claude-skills/claude-skills-benchmark"><img src="https://agentmods.dev/badge/skills/vinnie357/claude-skills/claude-skills-benchmark/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/vinnie357/claude-skills/claude-skills-benchmark"><img src="https://agentmods.dev/badge/skills/vinnie357/claude-skills/claude-skills-benchmark.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 | $0.00038 | $0.00866 |
| Opus 5.5 | $0.00015 | $0.00346 |
| Sonnet 5.5 | $0.00008 | $0.00173 |
| Haiku 4.5 | $0.00004 | $0.00087 |
Grade A, and why
claude-skills-benchmark 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.
How it starts
The opening of the file, as written. The whole thing — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill Benchmarking
Evaluate Agent Skills through static analysis and evaluation-driven methodology. Source: Anthropic's skill evaluation guidance.
Static Analysis Checks
The check list, pass criteria, and failure keys are defined in test/validate-skills-quality.nu — that script is the single source of truth; do not restate its checks here. Run mise run test:skills-quality to produce the current scorecard.
The checks' intent: descriptions that trigger accurately (length caps, "Use when" triggers, third person), spec-compliant naming, bounded body size, working examples, resolvable links, one-level reference depth with no orphaned files, anti-fabrication rules present, and coverage recorded in the plugin's skills/sources.toml (each entry's skills array), verified by set difference against the plugin's declared skill set in test/validate-sources.nu.
Skill Categories
Classify each skill for appropriate evaluation:
Capability Uplift: Enhances Claude's core abilities (coding, analysis, reasoning). Stable across model versions. Test by comparing base model performance with and without the skill.
Encoded Preference: Encodes user-specific workflows, formatting, and conventions. May need updates when models change. Test by verifying fidelity to the encoded workflow.
Evaluation Methodology
Writing Evals
For each skill, create:
- 5-10 representative prompts that should trigger the skill
- 3-5 out-of-scope prompts that should NOT trigger the skill
- Expected behavior criteria for each prompt
In-scope: "Benchmark the elixir plugin's skills" → skill activates
Out-of-scope: "Write an ExUnit test for this module" → skill stays silent
A/B Testing
Compare skill performance using independent agents:
- Agent A runs with the skill loaded
- Agent B runs without the skill
- A comparator judges outputs blindly
- Track: pass rate, token usage, execution time
Multi-Model Testing
Test across model tiers to verify consistency:
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 Changed 3e516e77373a
- 2mo ago First seen · 89 lines · 38 tokens per session scan A 9f63c8ab9046
claude-skills-benchmark is a skill published in the GitHub repository vinnie357/claude-skills (27 stars, last pushed 14d ago), licensed MIT. It adds 38 tokens to every session and 866 once invoked, about $0.0002 per session on Opus 5.5. 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.
claude-skills-benchmark compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| claude-skills-benchmarkvinnie357/claude-skills | Skill | 27 | 14d ago | MIT | A | 38 | Claude Code |
| imagegenopenai/codex | Skill | 134k | today | Apache-2.0 | A | 113 | Codex |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 2d ago | MIT | A | 200 | Claude Code, Codex |
| playwright-climicrosoft/playwright | Skill | 100k | today | Apache-2.0 | A | 19 | Claude Code |
| browser-automationopenclaw/openclaw | Skill | 394k | today | MIT | A | 36 | Claude Code |
| systematic-debuggingobra/superpowers | Skill | 315k | 6d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 95 | Claude Code |