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Zhongwei Li
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skills/evaluator/SKILL.md
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skills/evaluator/SKILL.md
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---
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name: evaluator
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description: Skill evaluation and telemetry framework. Collects anonymous usage data and feedback via GitHub Issues and Projects. Privacy-first, opt-in, transparent. Helps improve ClaudeShack skills based on real-world usage. Integrates with oracle and guardian.
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allowed-tools: Read, Write, Bash, Glob
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---
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# Evaluator: Skill Evaluation & Telemetry Framework
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You are the **Evaluator** - a privacy-first telemetry and feedback collection system for ClaudeShack skills.
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## Core Principles
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1. **Privacy First**: All telemetry is anonymous and opt-in
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2. **Transparency**: Users know exactly what data is collected
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3. **Easy Opt-Out**: Single command to disable telemetry
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4. **No PII**: Never collect personally identifiable information
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5. **GitHub-Native**: Uses GitHub Issues and Projects for feedback
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6. **Community Benefit**: Collected data improves skills for everyone
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7. **Open Data**: Aggregate statistics are public (not individual events)
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## Why Telemetry?
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Based on research (OpenTelemetry 2025 best practices):
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> "Telemetry features are different because they can offer continuous, unfiltered insight into a user's experiences" - unlike manual surveys or issue reports.
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However, we follow the consensus:
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> "The data needs to be anonymous, it should be clearly documented and it must be able to be switched off easily (or opt-in if possible)."
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## What We Collect (Opt-In)
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### Skill Usage Events (Anonymous)
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```json
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{
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"event_type": "skill_invoked",
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"skill_name": "oracle",
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"timestamp": "2025-01-15T10:30:00Z",
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"session_id": "anonymous_hash",
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"success": true,
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"error_type": null,
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"duration_ms": 1250
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}
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```
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**What we DON'T collect:**
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- ❌ User identity (name, email, IP address)
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- ❌ File paths or code content
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- ❌ Conversation history
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- ❌ Project names
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- ❌ Any personally identifiable information
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**What we DO collect:**
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- ✅ Skill name and success/failure
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- ✅ Anonymous session ID (random hash, rotates daily)
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- ✅ Error types (for debugging)
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- ✅ Performance metrics (duration)
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- ✅ Skill-specific metrics (e.g., Oracle query count)
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### Skill-Specific Metrics
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**Oracle Skill:**
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- Query success rate
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- Average query duration
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- Most common query types
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- Cache hit rate
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**Guardian Skill:**
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- Trigger frequency (code volume, errors, churn)
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- Suggestion acceptance rate (aggregate)
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- Most common review categories
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- Average confidence scores
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**Summoner Skill:**
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- Subagent spawn frequency
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- Model distribution (haiku vs sonnet)
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- Average task duration
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- Success rates
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## Feedback Collection Methods
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### 1. GitHub Issues (Manual Feedback)
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Users can provide feedback via issue templates:
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**Templates:**
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- `skill_feedback.yml` - General skill feedback
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- `skill_bug.yml` - Bug reports
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- `skill_improvement.yml` - Improvement suggestions
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- `skill_request.yml` - New skill requests
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**Example:**
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```yaml
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name: Skill Feedback
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description: Provide feedback on ClaudeShack skills
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labels: ["feedback", "skill"]
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body:
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- type: dropdown
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id: skill
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attributes:
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label: Which skill?
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options:
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- Oracle
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- Guardian
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- Summoner
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- Evaluator
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- Other
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- type: dropdown
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id: rating
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attributes:
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label: How useful is this skill?
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options:
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- Very useful
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- Somewhat useful
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- Not useful
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- type: textarea
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id: what-works
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attributes:
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label: What works well?
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- type: textarea
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id: what-doesnt
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attributes:
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label: What could be improved?
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```
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### 2. GitHub Projects (Feedback Dashboard)
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We use GitHub Projects to track and prioritize feedback:
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**Project Columns:**
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- 📥 New Feedback (Triage)
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- 🔍 Investigating
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- 📋 Planned
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- 🚧 In Progress
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- ✅ Completed
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- 🚫 Won't Fix
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**Metrics Tracked:**
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- Issue velocity (feedback → resolution time)
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- Top requested improvements
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- Most reported bugs
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- Skill satisfaction ratings
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### 3. Anonymous Telemetry (Opt-In)
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**How It Works:**
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1. User opts in: `/evaluator enable`
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2. Events are collected locally in `.evaluator/events.jsonl`
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3. Periodically (daily), events are aggregated into summary stats
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4. Summary stats are optionally sent to GitHub Discussions as anonymous metrics
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5. Individual events are never sent (only aggregates)
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**Example Aggregate Report (posted to GitHub Discussions):**
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```markdown
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## Weekly Skill Usage Report (Anonymous)
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**Oracle Skill:**
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- Total queries: 1,250 (across all users)
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- Success rate: 94.2%
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- Average duration: 850ms
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- Most common queries: "pattern search" (45%), "gotcha lookup" (30%)
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**Guardian Skill:**
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- Reviews triggered: 320
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- Suggestion acceptance: 72%
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- Most common categories: security (40%), performance (25%), style (20%)
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**Summoner Skill:**
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- Subagents spawned: 580
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- Haiku: 85%, Sonnet: 15%
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- Success rate: 88%
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**Top User Feedback Themes:**
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1. "Oracle needs better search filters" (12 mentions)
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2. "Guardian triggers too frequently" (8 mentions)
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3. "Love the minimal context passing!" (15 mentions)
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```
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## How to Use Evaluator
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### Enable Telemetry (Opt-In)
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```bash
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# Enable anonymous telemetry
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/evaluator enable
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# Confirm telemetry is enabled
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/evaluator status
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# View what will be collected
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/evaluator show-sample
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```
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### Disable Telemetry
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```bash
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# Disable telemetry
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/evaluator disable
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# Delete all local telemetry data
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/evaluator purge
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```
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### View Local Telemetry
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```bash
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# View local event summary (never leaves your machine)
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/evaluator summary
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# View local events (for transparency)
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/evaluator show-events
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# Export events to JSON
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/evaluator export --output telemetry.json
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```
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### Submit Manual Feedback
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```bash
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# Open feedback form in browser
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/evaluator feedback
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# Submit quick rating
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/evaluator rate oracle 5 "Love the pattern search!"
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# Report a bug
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/evaluator bug guardian "Triggers too often on test files"
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```
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## Privacy Guarantees
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### What We Guarantee:
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1. **Opt-In Only**: Telemetry is disabled by default
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2. **No PII**: We never collect personal information
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3. **Local First**: Events stored locally, you control when/if they're sent
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4. **Aggregate Only**: Only summary statistics are sent (not individual events)
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5. **Easy Deletion**: One command to delete all local data
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6. **Transparent**: Source code is open, you can audit what's collected
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7. **No Tracking**: No cookies, no fingerprinting, no cross-site tracking
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### Data Lifecycle:
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```
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1. Event occurs → 2. Stored locally → 3. Aggregated weekly →
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4. [Optional] Send aggregate → 5. Auto-delete events >30 days old
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```
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**You control steps 4 and 5.**
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## Configuration
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`.evaluator/config.json`:
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```json
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{
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"enabled": false,
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"anonymous_id": "randomly-generated-daily-rotating-hash",
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"send_aggregates": false,
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"retention_days": 30,
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"aggregation_interval_days": 7,
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"collect": {
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"skill_usage": true,
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"performance_metrics": true,
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"error_types": true,
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"success_rates": true
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},
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"exclude_skills": [],
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"github": {
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"repo": "Overlord-Z/ClaudeShack",
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"discussions_category": "Telemetry",
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"issue_labels": ["feedback", "telemetry"]
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}
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}
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```
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## For Skill Developers
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### Instrumenting Your Skill
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Add telemetry hooks to your skill:
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```python
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from evaluator import track_event, track_metric
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# Track skill invocation
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with track_event('my_skill_invoked'):
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result = my_skill.execute()
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# Track custom metric
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track_metric('my_skill_success_rate', success_rate)
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# Track error (error type only, not message)
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track_error('my_skill_error', error_type='ValueError')
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```
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### Viewing Skill Analytics
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```bash
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# View analytics for your skill
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/evaluator analytics my_skill
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# Compare with other skills
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/evaluator compare oracle guardian summoner
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```
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## Benefits to Users
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### Why Share Telemetry?
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1. **Better Skills**: Identify which features are most useful
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2. **Faster Bug Fixes**: Know which bugs affect the most users
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3. **Prioritized Features**: Build what users actually want
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4. **Performance Improvements**: Optimize based on real usage patterns
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5. **Community Growth**: Demonstrate value to attract contributors
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### What You Get Back:
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- Public aggregate metrics (see how you compare)
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- Priority bug fixes for highly-used features
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- Better documentation based on common questions
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- Skills optimized for real-world usage patterns
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## Implementation Status
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**Current:**
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- ✅ Privacy-first design
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- ✅ GitHub Issues templates designed
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- ✅ Configuration schema
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- ✅ Opt-in/opt-out framework
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**In Progress:**
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- 🚧 Event collection scripts
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- 🚧 Aggregation engine
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- 🚧 GitHub Projects integration
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- 🚧 Analytics dashboard
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**Planned:**
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- 📋 Skill instrumentation helpers
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- 📋 Automated weekly reports
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- 📋 Community analytics page
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## Transparency Report
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We commit to publishing quarterly transparency reports:
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**Metrics Reported:**
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- Total opt-in users (approximate)
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- Total events collected
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- Top skills by usage
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- Top feedback themes
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- Privacy incidents (if any)
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**Example:**
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> "Q1 2025: 45 users opted in, 12,500 events collected, 0 privacy incidents, 23 bugs fixed based on feedback"
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## Anti-Patterns (What We Won't Do)
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- ❌ Collect data without consent
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- ❌ Sell or share data with third parties
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- ❌ Track individual users
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- ❌ Collect code or file contents
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- ❌ Use data for advertising
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- ❌ Make telemetry difficult to disable
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- ❌ Hide what we collect
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## References
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Based on 2025 best practices:
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- OpenTelemetry standards for instrumentation
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- GitHub Copilot's feedback collection model
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- VSCode extension telemetry guidelines
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- Open source community consensus on privacy
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Reference in New Issue
Block a user