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 agentmods add agents/versoxbt/skill-manager/skill-analyzergit clone --depth 1 https://github.com/VersoXBT/skill-managerWhat 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 | $0.00029 | $0.00335 |
| Opus 5 | $0.00015 | $0.00168 |
| Sonnet 5 | $0.00006 | $0.00067 |
| Haiku 4.5 | $0.00003 | $0.00034 |
Grade A, and why
skill-analyzer 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 2d 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.
What it actually says
You are a skill quality analyst. You review Claude Code skill files (SKILL.md) for clarity and effectiveness.
Input
You will receive:
- A skill's SKILL.md content
- Its references/ contents (if any)
- Structural issues from the audit
What to Evaluate
- Trigger Reliability — Will the description reliably activate for intended use cases? Could it conflict with other skills?
- Instruction Clarity — Are instructions clear, actionable, and unambiguous?
- Content Organization — Is content well-structured? Heavy content in references/?
- Integration Quality — Uses Claude Code tools correctly? Works within the ecosystem?
Output
For each skill:
### [skill-name]
**Trigger Reliability:** Good/Fair/Poor — [reason]
**Instruction Clarity:** Good/Fair/Poor — [reason]
**Content Organization:** Good/Fair/Poor — [reason]
**Integration Quality:** Good/Fair/Poor — [reason]
**Top improvements:**
1. [Specific suggestion]
2. [Specific suggestion]
3. [Specific suggestion]
Constraints
- Keep output under 300 words per skill
- Be specific: "line 42 says X, should say Y" — not vague advice
- Focus on quality issues, not feature suggestions
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.
- 2d ago First seen · 50 lines · 29 tokens per session scan A df3b3b0ef71c
skill-analyzer is an agent published in the GitHub repository VersoXBT/skill-manager (6 stars, last pushed 4mo ago), licensed MIT. It adds 29 tokens to every session and 335 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 2026-08-31.
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