SkillCompass

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| | | |--|--| | What it is | A local-first skill quality evaluator and management tool for Claude Code / OpenClaw. Six-dimension scoring, usage-driven suggestions, guided improvement, version tracking. | | Pain it solves | Turns "tweak and hope" into diagnose → targeted fix → verified improvement. Turns "install and forget" into ongoing visibility over what's working, what's stale, and what's risky. | | Use in 30 seconds | /skillcompass — see your skill health at a glance. /eval-skill {path} — instant quality report showing exactly what's weakest and what to improve next. |

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

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Who This Is For

For

• Anyone maintaining agent skills and wanting measurable quality
• Developers who want directed improvement — not guesswork, but knowing exactly which dimension to fix next
• Teams needing a quality gate — any tool that edits a skill gets auto-evaluated
• Users who install many skills and need visibility over what's actually used, what's stale, and what's risky

Not For

• General code review or runtime debugging
• Creating new skills from scratch (use skill-creator)
• Evaluating non-skill files

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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>"]
    }
  }
}

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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

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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.

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Six-Dimension Evaluation Model

| ID | Dimension | Weight | What it evaluates | |:--:|-----------|:------:|-------------------| | D1 | Structure | 10% | Frontmatter validity, markdown format, declarations | | D2 | Trigger | 15% | Activation quality, rejection accuracy, discoverability | | D3 | Security | 20% | Secrets, injection, permissions, exfiltration, embedded shell | | D4 | Functional | 30% | Core quality, edge cases, output stability, error handling | | D5 | Comparative | 15% | Value over direct prompting (with vs without skill) | | D6 | Uniqueness | 10% | Overlap with similar skills, model supersession risk |

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

| Verdict | Condition | |---------|-----------| | PASS | score >= 70 AND D3 pass | | CAUTION | 50–69, or D3 High findings | | FAIL | score < 50, or D3 Critical (gate override) |

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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

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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.

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Works With Everything

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

| Tool | How it works together | Guide | |------|----------------------|-------| | Claudeception | Extracts skill → auto-evaluation catches security holes + redundancy → directed fix | guide | | Self-Improving Agent | Logs errors → feed as signals → SkillCompass maps to dimensions and fixes | guide |

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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.

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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.

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License

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