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 commands/evol-ai/skillcompass/eval-skillgit clone --depth 1 https://github.com/Evol-ai/SkillCompassWhat 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 | $0.00000 | $0.04709 |
| Opus 5 | $0.00000 | $0.02354 |
| Sonnet 5 | $0.00000 | $0.00942 |
| Haiku 4.5 | $0.00000 | $0.00471 |
Grade A, and why
eval-skill scanned grade A with 0 findings against 26 rules in 11 categories โ prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency โ 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 โ 417 lines โ stays where its author put it; the contents beside it link to each section on GitHub.
/eval-skill โ Six-Dimension Evaluation
๐ Enhanced with Local Validators: This command now uses local JavaScript validators for D1, D2, and D3 dimensions to significantly reduce token consumption while maintaining evaluation quality. Complex reasoning tasks (D4, D5, D6) continue to use LLM evaluation with local pre-analysis.
Prerequisites
- Recommended model: Claude Opus 4.6 (
claude-opus-4-6). The 6-dimension rubric requires complex multi-dimensional reasoning, nuanced security analysis, and consistent scoring across dimensions. Sonnet and Haiku may produce inconsistent dimension scores, miss subtle security findings in D3, and generate unreliable D5 comparative assessments. If not using an Opus-class model, treat results as approximate.
Arguments
<path>(required): Path to the SKILL.md file to evaluate.--scope [gate|target|full](optional, default:full): Evaluation scope.gate: D1 + D3 only (~8K tokens). Outputs"partial": true.target --dimension D{N}: specified dimension + D3 gate (~12K tokens). Outputs"partial": true.full: all 6 dimensions (~40K tokens). Default behavior.
--dimension D{N}(optional): Used with--scope targetto specify which dimension.--format [json|md|all](optional, default:json): Output format.--feedback <path>(optional): Path to a feedback signal JSON file.--ci(optional): CI-friendly mode. Suppresses interactive prompts, outputs JSON only, sets exit code (0=all PASS, 1=CAUTION, 2=FAIL).
Error Handling
- File not found: Stop immediately. Output
"Error: File not found: {path}"(translate at display time). - Not a SKILL.md: Warn
"Warning: filename is not SKILL.md โ continuing with evaluation."if applicable. - YAML malformed: Warn
"Warning: YAML frontmatter is malformed.", set D1 frontmatter_sub = 0, continue with remaining checks.
Steps
Step 1: Load Target
Parse arguments. Check current model โ if not an Opus-class model, output this warning (translate to the session locale at display time):
โ Warning: Current model is {model_name}. For reliable 6D evaluation, Claude Opus 4.6 is recommended. Results may be less consistent with other models.
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 ยท 417 lines ยท 0 tokens per session scan A 9806ae0335dd
eval-skill is a command published in the GitHub repository Evol-ai/SkillCompass (215 stars, last pushed 4mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 4,709 tokens. 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 2026-08-30.
Other commands, from other repositories
story-long-scan
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audit_docs
Audit all project metadata files for stale counts, version mismatches, broken references, and missing entries. Reports discrepancies without auto-fixing.
merge_session
Merge session branch(es) into main via rebase + fast-forward push. Use from inside a session worktree pane to land your work, or with --all to batch merge all sessions from the main repo.
cherry_pick_pr
Cherry-pick one or more commits onto a new branch from main and open a PR. Useful when a commit landed on the wrong branch or you want to split a multi-commit branch into separate PRs.
cti-report
Render case deliverables โ relationship graph (PNG/SVG/Mermaid) and a polished PDF/DOCX assessment. Usage: /cti-report [--graph|--pdf].
reskin
Extract a measured design signature (type ramp, accent + its budget, grid unit, radius, layout) from a reference image or site, write it to signature.json, and drive the build to match it โ "steal this vibe" as a spec, not pixels.