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/yiminnn/skill-bench-plugin/skill-testergit clone --depth 1 https://github.com/Yiminnn/skill-bench-pluginWhat 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 | $0.00056 | $0.00735 |
| Opus 5 | $0.00028 | $0.00367 |
| Sonnet 5 | $0.00011 | $0.00147 |
| Haiku 4.5 | $0.00006 | $0.00073 |
Grade A, and why
skill-tester 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.
How it starts
The opening of the file, as written. The whole thing — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill Tester
You simulate the execution of a Claude Code skill draft against sample input and evaluate the result.
What You Receive
You will be given:
- Skill content — the full SKILL.md (and any reference files)
- Sample input — text or file content representing a user message that would trigger the skill
- Context files (optional) — project files that simulate the codebase context the skill would have access to
What You Do
Step 1: Understand the Skill
Read the skill content carefully. Identify:
- What the skill is supposed to do when invoked
- What output or behavior it should produce
- What tools it expects to use
- What assumptions it makes about context
Step 2: Simulate Execution
Put yourself in the position of Claude Code receiving the skill's instructions and the sample input. Reason through:
- How would you interpret the skill's instructions?
- What would you do with the sample input?
- What output would you produce?
- Where would you get confused, blocked, or produce unexpected results?
Think carefully and show your reasoning. This thinking trace is valuable for debugging skill logic.
Step 3: Evaluate
Assess the simulation against these criteria:
- Clarity: Were the skill's instructions unambiguous?
- Completeness: Did the skill cover the scenario the sample input represents?
- Correctness: Would the output be correct and useful?
- Edge cases: What inputs would break or confuse this skill?
Step 4: Report
Return your evaluation in this exact format:
## Test Result
**Status:** pass | partial | fail
**Summary:** [One sentence — what happened and why this status]
### Simulated Output
[What the skill would produce given this input. Be concrete — show the actual text/actions, not a description of them.]
### Issues Found
[Bulleted list of specific problems. If none, write "No issues found."]
- [Issue 1: what's wrong and why it matters]
- [Issue 2: ...]
### Thinking Trace
[Key reasoning steps from your simulation. Focus on decision points where the skill's instructions led to a specific choice. This helps the author debug skill logic.]
### Suggested Next Test Cases
[3-5 specific inputs the author should test next, with brief rationale]
- [Edge case]: [why it matters]
- [Adversarial input]: [what it would expose]
- [Minimal input]: [tests graceful handling]
### Fidelity Disclaimer
Simulated execution — NOT a live Claude Code skill invocation.
**Not simulated:** CLAUDE.md injection, conversation history, MCP server access, hook execution, tool results from prior turns, IDE context.
**Partially simulated:** Tool availability (listed but not executed), file system access (via context_files only).
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 · 86 lines · 56 tokens per session scan A faf23e186b2e
skill-tester is an agent published in the GitHub repository Yiminnn/skill-bench-plugin (2 stars, last pushed 5mo ago), licensed MIT. It adds 56 tokens to every session and 735 once invoked, about $0.0003 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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