Add 71 new skills: Spark Intelligence, SupaRalph, PayloadCMS, Frontend-Design, Ralph, and Vibeship ecosystem integration
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skills/spark-intelligence/SKILL.md
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skills/spark-intelligence/SKILL.md
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# Spark Intelligence Skill for QwenClaw
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## Overview
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**Name:** spark-intelligence
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**Source:** https://github.com/vibeforge1111/vibeship-spark-intelligence
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**Website:** https://spark.vibeship.co
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**Spark Intelligence** is a **self-evolving AI companion** that transforms QwenClaw into a learning system that remembers, adapts, and improves continuously.
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---
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## What Spark Does
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Spark closes the intelligence loop for QwenClaw:
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```
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QwenClaw Session → Spark Captures Events → Pipeline Filters Noise →
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Quality Gate Scores Insights → Storage → Advisory Delivery →
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Pre-Tool Guidance → Outcomes Feed Back → System Evolves
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```
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### Key Capabilities
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| Capability | Description |
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|------------|-------------|
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| **Pre-Tool Advisory** | Surfaces warnings/notes BEFORE QwenClaw executes tools |
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| **Memory Capture** | Automatically captures important user preferences and patterns |
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| **Anti-Pattern Detection** | Identifies recurring mistakes (e.g., "edit without read") |
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| **Auto-Promotion** | Validated insights promote to CLAUDE.md, AGENTS.md, SOUL.md |
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| **EIDOS Loop** | Prediction → outcome → evaluation for continuous learning |
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| **Domain Chips** | Pluggable expertise modules for specialized domains |
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| **Obsidian Observatory** | 465+ auto-generated markdown pages with live queries |
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---
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## Installation
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### Prerequisites
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- Python 3.10+
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- pip
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- Git
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### Windows One-Command Install
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```powershell
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irm https://raw.githubusercontent.com/vibeforge1111/vibeship-spark-intelligence/main/install.ps1 | iex
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```
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### Mac/Linux One-Command Install
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```bash
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curl -fsSL https://raw.githubusercontent.com/vibeforge1111/vibeship-spark-intelligence/main/install.sh | bash
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```
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### Manual Install
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```bash
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git clone https://github.com/vibeforge1111/vibeship-spark-intelligence
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cd vibeship-spark-intelligence
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python -m venv .venv && source .venv/bin/activate # Mac/Linux
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# or .venv\Scripts\activate # Windows
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python -m pip install -e .[services]
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```
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### Verify Installation
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```bash
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python -m spark.cli health
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python -m spark.cli learnings
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python -m spark.cli up
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```
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---
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## Integration with QwenClaw
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### Step 1: Install Spark Intelligence
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Run the installation command above.
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### Step 2: Configure QwenClaw Session Hook
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Add to QwenClaw's session initialization:
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```javascript
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// In qwenclaw.js or session config
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const sparkConfig = {
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enabled: true,
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sessionId: `qwenclaw-${Date.now()}`,
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hooks: {
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preToolUse: true,
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postToolUse: true,
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userPrompt: true,
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},
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};
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```
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### Step 3: Enable Event Capture
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Spark captures QwenClaw events:
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```bash
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# Start Spark pipeline
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python -m spark.cli up
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# Start QwenClaw
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qwenclaw start
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```
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### Step 4: Generate Obsidian Observatory (Optional)
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```bash
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python scripts/generate_observatory.py --force --verbose
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```
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Vault location: `~/Documents/Obsidian Vault/Spark-Intelligence-Observatory`
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---
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## Advisory Authority Levels
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Spark provides pre-tool guidance with three authority levels:
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| Level | Score | Behavior |
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|-------|-------|----------|
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| **BLOCK** | 0.95+ | Prevents the action entirely |
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| **WARNING** | 0.80-0.95 | Prominent caution before action |
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| **NOTE** | 0.48-0.80 | Included in context for awareness |
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### Examples
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**BLOCK Example:**
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```
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⚠️ BLOCKED: Spark advisory
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This command will delete the production database.
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Last 3 executions resulted in data loss.
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Confidence: 0.97 | Validated: 12 times
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```
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**WARNING Example:**
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```
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⚠️ WARNING: Spark advisory
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You're editing this file without reading it first.
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This pattern failed 4 times in the last 24 hours.
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Consider: Read the file first, then edit.
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```
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**NOTE Example:**
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```
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ℹ️ NOTE: Spark advisory
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User prefers `--no-cache` flag for Docker builds.
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Captured from session #4521.
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```
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---
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## Memory Capture with Intelligence
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### Automatic Importance Scoring (0.0-1.0)
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| Score | Action | Example Triggers |
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|-------|--------|-----------------|
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| ≥0.65 | Auto-save | "remember this", quantitative data |
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| 0.55-0.65 | Suggest | "I prefer", design constraints |
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| <0.55 | Ignore | Generic statements, noise |
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### Signals That Boost Importance
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- **Causal language**: "because", "leads to" (+0.15-0.30)
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- **Quantitative data**: "reduced from 4.2s to 1.6s" (+0.30)
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- **Technical specificity**: real tools, libraries, patterns (+0.15-0.30)
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### Example Captures
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```
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User: "Remember: always use --no-cache when building Docker images"
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→ Spark: Captured (score: 0.82)
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→ Promoted to: CLAUDE.md
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User: "I prefer TypeScript over JavaScript for large projects"
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→ Spark: Captured (score: 0.68)
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→ Promoted to: AGENTS.md
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User: "The build time reduced from 4.2s to 1.6s after caching"
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→ Spark: Captured (score: 0.91)
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→ Promoted to: EIDOS pattern
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```
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---
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## Quality Pipeline
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Every observation passes through rigorous gates:
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```
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Event → Importance Scoring → Meta-Ralph Quality Gate →
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Cognitive Storage → Validation Loop → Promotion Decision
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```
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### Meta-Ralph Quality Scores (0-12)
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Scores on:
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- **Actionability** (can you act on it?)
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- **Novelty** (genuine insight vs. obvious)
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- **Reasoning** (explicit causal explanation)
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- **Specificity** (context-specific vs. generic)
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- **Outcome-Linked** (validated by results)
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### Promotion Criteria
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**Track 1 (Reliability):**
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- Reliability ≥80% AND validated ≥5 times
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**Track 2 (Confidence):**
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- Confidence ≥95% AND age ≥6 hours AND validated ≥5 times
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**Contradicted insights lose reliability automatically.**
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---
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## EIDOS Episodic Intelligence
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Extracts structured rules from experience:
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| Type | Description | Example |
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|------|-------------|---------|
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| **Heuristics** | General rules of thumb | "Always test before deploying" |
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| **Sharp Edges** | Things to watch out for | "API rate limits hit at 100 req/min" |
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| **Anti-Patterns** | What not to do | "Don't edit config without backup" |
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| **Playbooks** | Proven approaches | "Database migration checklist" |
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| **Policies** | Enforced constraints | "Must have tests for core modules" |
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---
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## Usage in QwenClaw
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### Basic Usage
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```bash
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# Start Spark pipeline
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python -m spark.cli up
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# Start QwenClaw (Spark captures automatically)
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qwenclaw start
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# Send task
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qwenclaw send "Refactor the authentication module"
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```
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### Check Spark Status
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```bash
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python -m spark.cli status
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python -m spark.cli learnings
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```
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### View Advisory History
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```bash
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python -m spark.cli advisories
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```
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### Promote Insights Manually
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```bash
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python -m spark.cli promote <insight-id>
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```
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---
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## Obsidian Observatory
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Spark auto-generates **465+ markdown pages** with live Dataview queries:
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### What's Included
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- **Pipeline Health** - 12-stage pipeline detail pages
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- **Cognitive Insights** - Stored insights with reliability scores
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- **EIDOS Episodes** - Pattern distillations
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- **Advisory Decisions** - Pre-tool guidance history
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- **Explorer Views** - Real-time data exploration
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- **Canvas View** - Spatial pipeline visualization
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### Auto-Sync
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Observatory syncs every **120 seconds** when pipeline is running.
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---
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## Measurable Outcomes
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### Advisory Source Effectiveness
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| Source | What It Provides | Effectiveness |
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|--------|-----------------|---------------|
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| Cognitive | Validated session insights | ~62% (dominant) |
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| Bank | User memory banks | ~10% |
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| EIDOS | Pattern distillations | ~5% |
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| Baseline | Static rules | ~5% |
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| Trigger | Event-specific rules | ~5% |
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| Semantic | BM25 + embedding retrieval | ~3% |
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### Timeline to Value
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| Time | What Happens |
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|------|--------------|
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| **Hour 1** | Spark starts capturing events |
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| **Hour 2-4** | Patterns emerge (tool effectiveness, error patterns) |
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| **Day 1-2** | Insights get promoted to project files |
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| **Week 1+** | Advisory goes live with pre-tool guidance |
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---
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## Integration Examples
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### Example 1: Preventing Recurring Errors
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```
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QwenClaw: About to run: npm install
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Spark: ⚠️ WARNING
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Last 3 times you ran `npm install` without --legacy-peer-deps,
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it failed with ERESOLVE errors.
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Suggestion: Use `npm install --legacy-peer-deps`
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Reliability: 0.94 | Validated: 8 times
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```
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### Example 2: Auto-Promoting Best Practices
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```
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User: "Remember: always run tests before committing"
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Spark: Captured (score: 0.78)
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→ After 5 successful validations:
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Promoted to CLAUDE.md:
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"## Testing Policy
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Always run tests before committing changes.
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Validated: 12 times | Reliability: 0.96"
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```
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### Example 3: Domain-Specific Expertise
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```
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Domain Chip: React Development
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Spark Advisory:
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ℹ️ NOTE
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In this project, useEffect dependencies are managed
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with eslint-plugin-react-hooks.
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Missing dependencies auto-fixed 23 times.
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Reliability: 0.89
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```
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---
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## Configuration
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### spark.config.yaml
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```yaml
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spark:
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enabled: true
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session_id: qwenclaw-${timestamp}
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hooks:
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pre_tool_use: true
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post_tool_use: true
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user_prompt: true
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advisory:
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enabled: true
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min_score: 0.48
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cooldown_seconds: 300
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memory:
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auto_capture: true
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min_importance: 0.55
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observatory:
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enabled: true
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sync_interval_seconds: 120
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```
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---
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## Best Practices
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### 1. Let Spark Learn Naturally
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Just use QwenClaw normally. Spark captures and learns in the background.
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### 2. Review Advisories
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Pay attention to pre-tool warnings. They're based on validated patterns.
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### 3. Provide Explicit Feedback
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Tell Spark what to remember:
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- "Remember: always use --force for this legacy package"
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- "I prefer yarn over npm in this project"
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### 4. Check Observatory
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Review the Obsidian vault to understand what Spark has learned.
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### 5. Promote High-Value Insights
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Manually promote insights that are immediately valuable.
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---
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## Skill Metadata
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```yaml
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name: spark-intelligence
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version: 1.0.0
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category: automation
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description: Self-evolving AI companion that captures, distills, and delivers
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actionable insights from QwenClaw sessions
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author: Vibeship (https://github.com/vibeforge1111/vibeship-spark-intelligence)
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license: MIT
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tags:
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- learning
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- memory
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- advisory
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- self-improving
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- local-first
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- obsidian
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```
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---
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## Resources
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- **Website:** https://spark.vibeship.co
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- **GitHub:** https://github.com/vibeforge1111/vibeship-spark-intelligence
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- **Onboarding:** `docs/SPARK_ONBOARDING_COMPLETE.md`
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- **Quickstart:** `docs/QUICKSTART.md`
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- **Obsidian Guide:** `docs/OBSIDIAN_OBSERVATORY_GUIDE.md`
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---
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**Spark Intelligence transforms QwenClaw from a stateless executor into a learning system!** 🧠✨
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Reference in New Issue
Block a user