SkillCompass

Evaluate quality. Find the weakest link. Fix it. Prove it worked. Repeat.

GitHub · SKILL.md · Schemas · Changelog

Evaluate → find weakest link → fix it → prove it worked → next weakness → repeat.
Meanwhile, Skill Inbox watches your usage and tells you what needs attention.

Who This Is For

Quick Start

Prerequisites: Claude Opus 4.6 (complex reasoning + consistent scoring) · Node.js v18+ (local validators)

Claude Code

git clone https://github.com/Evol-ai/SkillCompass.git
cd SkillCompass && npm install

# User-level (all projects)
rsync -a --exclude='.git'  . ~/.claude/skills/skill-compass/

# Or project-level (current project only)
rsync -a --exclude='.git'  . .claude/skills/skill-compass/

First run: SkillCompass auto-triggers a brief onboarding — scans your installed skills (~5 seconds), offers statusLine setup, then hands control back. Claude Code will request permission for node commands; select "Allow always" to avoid repeated prompts.

OpenClaw

git clone https://github.com/Evol-ai/SkillCompass.git
cd SkillCompass && npm install
# Follow OpenClaw skill installation docs for your setup
rsync -a --exclude='.git'  . <your-openclaw-skills-path>/skill-compass/

If your OpenClaw skills live outside the default scan roots, add them to skills.load.extraDirs in ~/.openclaw/openclaw.json:

{
  "skills": {
    "load": {
      "extraDirs": ["<your-openclaw-skills-path>"]
    }
  }
}

Usage

/skillcompass is the single entry point. Use it with a slash command or just talk naturally — both work:

/skillcompass                              → see what needs attention
/skillcompass evaluate my-skill            → six-dimension quality report
"improve the nano-banana skill"            → fix weakest dimension, verify, next
"what skills haven't I used recently?"     → usage-based insights
"security scan this skill"                 → D3 security deep-dive

What It Does

The score isn't the point — the direction is. You instantly see which dimension is the bottleneck and what to do about it.

Each /eval-improve round follows a closed loop: fix the weakest → re-evaluate → verify improvement → next weakest. No fix is saved unless the re-evaluation confirms it actually helped.

Six-Dimension Evaluation Model

overall_score = round((D1×0.10 + D2×0.15 + D3×0.20 + D4×0.30 + D5×0.15 + D6×0.10) × 10)

Skill Inbox — Usage-Driven Suggestions

SkillCompass passively tracks which skills you actually use and surfaces suggestions when something needs attention — unused skills, stale evaluations, declining usage, available updates, and more. 9 built-in rules, all based on real invocation data.

• Suggestions have a lifecycle: pending → acted / snoozed / dismissed, with auto-reactivation when conditions change
• All data stays local — no network calls unless you explicitly request updates
• Tracking is automatic via hooks (~one line per skill invocation), zero configuration

Features

Evaluate → Improve → Verify

/eval-skill scores six dimensions and pinpoints the weakest. /eval-improve targets that dimension, applies a fix, and re-evaluates — only saves when the target dimension improved and security/functionality didn't regress. Then move to the next weakness.

Skill Lifecycle

SkillCompass covers the full lifecycle of your skills — not just one-time evaluation.

Install — auto-scans your inventory, quick-checks security patterns across packages and sub-skills.

Ongoing — usage hooks passively track every invocation. Skill Inbox turns this into actionable insights: which skills are never used, which are declining, which are heavily used but never evaluated, which have updates available.

On edit — hooks auto-check structure + security on every SKILL.md write through Claude. Catches injection, exfiltration, embedded shell. Warns, never blocks.

On change — SHA-256 snapshots ensure any version is recoverable. D3 or D4 regresses after improvement? Snapshot restored automatically.

On update — update checker reads local git state passively; network only when you ask. Three-way merge preserves your local improvements region-by-region.

Scale

One skill or fifty — same workflow. /eval-audit scans a whole directory and ranks results worst-first so you fix what matters most. /eval-evolve chains multiple improve rounds automatically (default 6, stops at PASS or plateau). --ci flag outputs machine-readable JSON with exit codes for pipeline integration.

Works With Everything

No point-to-point integration needed. The Pre-Accept Gate intercepts all SKILL.md edits regardless of source.

Design Principles

• Local-first: All data stays on your machine. No network calls except when you explicitly request updates.
• Read-only by default: Evaluation and reporting are read-only. Write operations (improve, merge, rollback) require explicit opt-in.
• Passive tracking, active decisions: Hooks collect usage data silently. Suggestions are surfaced, never auto-acted on.
• Dual-channel UX: Keyboard-selectable choices for actions, natural language for queries. Both always available.

Feedback Signal Standard

SkillCompass defines an open feedback-signal.json schema for any tool to report skill usage data:

/eval-skill ./my-skill/SKILL.md --feedback ./feedback-signals.json

Signals: trigger_accuracy, correction_count, correction_patterns, adoption_rate, ignore_rate, usage_frequency. The schema is extensible (additionalProperties: true) — any pipeline can produce or consume this format.

License

MIT — Use, modify, distribute freely. See LICENSE for details.