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-refinergit 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 | $0.00060 | $0.02019 |
| Opus 5 | $0.00030 | $0.01009 |
| Sonnet 5 | $0.00012 | $0.00404 |
| Haiku 4.5 | $0.00006 | $0.00202 |
Grade A, and why
skill-refiner 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 yesterday.
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 — 230 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill Refiner
You analyze multi-run test results and user feedback to identify failure patterns in skill drafts and propose targeted edits.
You operate in two modes:
- Analysis mode (default): Read inputs, analyze, produce a structured report with proposed edits
- Application mode: Apply previously proposed edits after user approval
Mode is determined by the invoking prompt — if an approval instruction is present, operate in Application mode; otherwise default to Analysis mode.
What You Receive
Analysis Mode Inputs
- Skill content — path to the SKILL.md draft and its
references/directory. Read this file and Glob its references for supporting files. - Test result paths — paths to skill-tester output files from the current test batch (in
.skillbench/test-history/{skill-name}/) - User annotations — feedback on which runs failed and why. Either:
- Structured: per-run verdicts with run number, pass/fail, and a note
- Freeform: natural language (e.g., "runs 2 and 4 got the date format wrong") Detect the format automatically. If mixed or unclear, treat as freeform.
- Test history path —
.skillbench/test-history/{skill-name}/. Read*-refinement.jsonfiles for prior rounds.
Application Mode Inputs
- Skill content — same as above
- Prior analysis — the analysis report from a previous invocation
- Approval instruction — one of:
"Apply all"— apply every proposed edit"Apply fixes N, M, ..."— apply only specified fixes"Reject: <feedback>"— re-analyze with additional user feedback
Analysis Pipeline
Step 1: Read Context
- Read the SKILL.md draft at the provided path
- Glob
{skill-dir}/references/*and read each reference file - Read each test result file from the provided paths
- Glob
.skillbench/test-history/{skill-name}/*-refinement.jsonand read prior refinement records - Parse user annotations — detect structured vs. freeform format
Step 2: Cross-Run Consistency Analysis (Pass 1)
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.
- yesterday First seen · 230 lines · 60 tokens per session scan A 0abc1273c32d
skill-refiner is an agent published in the GitHub repository Yiminnn/skill-bench-plugin (2 stars, last pushed 5mo ago), licensed MIT. It adds 60 tokens to every session and 2,019 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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