Skill Doctor

Skill Doctor

A check-up for your ClawHub portfolio. Skill Doctor pulls live data for every skill and plugin you own via clawhub inspect / clawhub package inspect, runs it through a rule-based diagnostic engine, and hands you a prioritized prescription: what's healthy, what's at risk, and what to do about it this week.

Think of it as a doctor's visit for your published work — vitals in, diagnosis out.

Why this exists

Tools like clawhub-monitor-all.sh (or any cron-based watcher) tell you what changed. Skill Doctor tells you what it means — whether a number is good, bad, or needs context, and what the highest-leverage next step is.

First-Use Initialisation

Before running a check-up, verify the clawhub CLI is installed and authenticated:

command -v clawhub >/dev/null 2>&1 || { echo "clawhub CLI not found — install it first"; exit 1; }

Confirm a config directory exists for storing diagnostic history (used for trend detection across runs):

mkdir -p ~/.skill-doctor
[ -f ~/.skill-doctor/config.json ] || echo '{"slugs":[],"plugins":[],"anthropic_api_key":null}' > ~/.skill-doctor/config.json

Never overwrite an existing config. Ask the user which slugs/plugins to track on first run if the config is empty.

Quick Reference

Running a Check-Up

Single skill

python3 scripts/checkup.py --slug proof-of-contribution

Full portfolio (all configured skills + plugins)

python3 scripts/checkup.py --all

With AI-narrated analysis (optional, requires API key)

python3 scripts/checkup.py --all --deep

This sends the structured diagnostic (not raw secrets) to the Anthropic API for a short narrative summary and prioritized recommendation. It is opt-in — never send data to the API unless --deep is explicitly passed and a key is configured.

With a trend chart

python3 scripts/checkup.py --all --chart

Outputs a PNG to ~/.skill-doctor/charts/ showing downloads/installs over time per skill, using locally stored history — no new API calls.

Diagnostic Categories

Skill Doctor groups every finding into one of these, mirroring how a real check-up triages issues:

Severity Levels

Output Format

Each check-up produces one prescription block per skill/plugin:

## 🩺  ()

**Status**: healthy | needs-attention | critical
**Verdict**: clean | pending | suspicious | malware

### Vitals
- Downloads: X (Δ since last check: +Y)
- Installs (all-time): X
- Active installs: X
- Stars: X

### Findings
- [severity] Finding description

### Prescription
1. Concrete, specific next action
2. Concrete, specific next action

---

Rule-Based Diagnostic Logic

These are deterministic checks Skill Doctor always runs, no API key required. See references/diagnostic-rules.md for the full rule set and exact thresholds — summarized here:

• Conversion check: installs / downloads ratio below a configurable threshold → flag conversion
• Staleness check: days since last version bump vs. days since last download growth → flag staleness
• Momentum check: compare current vitals to last stored snapshot → accelerating / flat / declining
• Trust check: any verdict other than clean → flag moderation, severity scales with verdict
• Active-install drop check: active installs falling while all-time installs stays flat → flag risk

Deep Analysis (Optional, API-Powered)

When --deep is passed and ~/.skill-doctor/config.json has a valid anthropic_api_key:

1. Build a compact JSON summary of all findings (no raw tokens, no secrets — just metrics and rule outputs)
2. Send to Claude via the Messages API with a prompt asking for a short prioritized narrative
3. Present the narrative above the rule-based prescriptions, clearly labeled as AI commentary

If no key is configured, skip this step silently and rely on rule-based output only — never block the core check-up on missing AI access.

See references/deep-analysis-setup.md for API key configuration.

State & History

Each run stores a snapshot per slug at ~/.skill-doctor/state/.json so the next run can compute deltas and momentum. This mirrors the state-file pattern used by shell-based ClawHub watchers, but in structured JSON for easier analysis and charting.

Do not log API keys, tokens, or full clawhub inspect payloads beyond what's needed for the metrics above.

Publishing Your Own Findings as a Skill

If a diagnostic pattern proves broadly useful (e.g., a new rule that catches a real issue across many users' skills), it can be proposed as an addition to references/diagnostic-rules.md rather than hardcoded per-user — keep the core engine generic so it works for anyone's ClawHub portfolio, not just one project.

Support This Skill

If Skill Doctor saved you time, consider sending a few sats:

⚡ Lightning: welove@blink.sv

Best Practices

1. Run before publishing changes — establish a baseline so the next check-up shows real impact
2. Treat critical findings as same-day items — moderation issues affect trust immediately
3. Don't chase every metric — focus the prescription on the 1-2 highest-leverage actions
4. Re-run after acting — confirm the fix moved the needle before considering it resolved
5. Keep --deep opt-in — rule-based diagnostics should never depend on network/API availability