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QwenClaw-with-Auth/skills/spark-intelligence/SKILL.md

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Spark Intelligence Skill for QwenClaw

Overview

Name: spark-intelligence
Source: https://github.com/vibeforge1111/vibeship-spark-intelligence
Website: https://spark.vibeship.co

Spark Intelligence is a self-evolving AI companion that transforms QwenClaw into a learning system that remembers, adapts, and improves continuously.

What Spark Does

Spark closes the intelligence loop for QwenClaw:

QwenClaw Session → Spark Captures Events → Pipeline Filters Noise → 
Quality Gate Scores Insights → Storage → Advisory Delivery → 
Pre-Tool Guidance → Outcomes Feed Back → System Evolves

Key Capabilities

Capability Description
Pre-Tool Advisory Surfaces warnings/notes BEFORE QwenClaw executes tools
Memory Capture Automatically captures important user preferences and patterns
Anti-Pattern Detection Identifies recurring mistakes (e.g., "edit without read")
Auto-Promotion Validated insights promote to CLAUDE.md, AGENTS.md, SOUL.md
EIDOS Loop Prediction → outcome → evaluation for continuous learning
Domain Chips Pluggable expertise modules for specialized domains
Obsidian Observatory 465+ auto-generated markdown pages with live queries

Installation

Prerequisites

  • Python 3.10+
  • pip
  • Git

Windows One-Command Install

irm https://raw.githubusercontent.com/vibeforge1111/vibeship-spark-intelligence/main/install.ps1 | iex

Mac/Linux One-Command Install

curl -fsSL https://raw.githubusercontent.com/vibeforge1111/vibeship-spark-intelligence/main/install.sh | bash

Manual Install

git clone https://github.com/vibeforge1111/vibeship-spark-intelligence
cd vibeship-spark-intelligence
python -m venv .venv && source .venv/bin/activate  # Mac/Linux
# or .venv\Scripts\activate  # Windows
python -m pip install -e .[services]

Verify Installation

python -m spark.cli health
python -m spark.cli learnings
python -m spark.cli up

Integration with QwenClaw

Step 1: Install Spark Intelligence

Run the installation command above.

Step 2: Configure QwenClaw Session Hook

Add to QwenClaw's session initialization:

// In qwenclaw.js or session config
const sparkConfig = {
  enabled: true,
  sessionId: `qwenclaw-${Date.now()}`,
  hooks: {
    preToolUse: true,
    postToolUse: true,
    userPrompt: true,
  },
};

Step 3: Enable Event Capture

Spark captures QwenClaw events:

# Start Spark pipeline
python -m spark.cli up

# Start QwenClaw
qwenclaw start

Step 4: Generate Obsidian Observatory (Optional)

python scripts/generate_observatory.py --force --verbose

Vault location: ~/Documents/Obsidian Vault/Spark-Intelligence-Observatory

Advisory Authority Levels

Spark provides pre-tool guidance with three authority levels:

Level Score Behavior
BLOCK 0.95+ Prevents the action entirely
WARNING 0.80-0.95 Prominent caution before action
NOTE 0.48-0.80 Included in context for awareness

Examples

BLOCK Example:

⚠️ BLOCKED: Spark advisory
This command will delete the production database.
Last 3 executions resulted in data loss.
Confidence: 0.97 | Validated: 12 times

WARNING Example:

⚠️ WARNING: Spark advisory
You're editing this file without reading it first.
This pattern failed 4 times in the last 24 hours.
Consider: Read the file first, then edit.

NOTE Example:

ℹ️ NOTE: Spark advisory
User prefers `--no-cache` flag for Docker builds.
Captured from session #4521.

Memory Capture with Intelligence

Automatic Importance Scoring (0.0-1.0)

Score Action Example Triggers
≥0.65 Auto-save "remember this", quantitative data
0.55-0.65 Suggest "I prefer", design constraints
<0.55 Ignore Generic statements, noise

Signals That Boost Importance

  • Causal language: "because", "leads to" (+0.15-0.30)
  • Quantitative data: "reduced from 4.2s to 1.6s" (+0.30)
  • Technical specificity: real tools, libraries, patterns (+0.15-0.30)

Example Captures

User: "Remember: always use --no-cache when building Docker images"
→ Spark: Captured (score: 0.82)
→ Promoted to: CLAUDE.md

User: "I prefer TypeScript over JavaScript for large projects"
→ Spark: Captured (score: 0.68)
→ Promoted to: AGENTS.md

User: "The build time reduced from 4.2s to 1.6s after caching"
→ Spark: Captured (score: 0.91)
→ Promoted to: EIDOS pattern

Quality Pipeline

Every observation passes through rigorous gates:

Event → Importance Scoring → Meta-Ralph Quality Gate → 
Cognitive Storage → Validation Loop → Promotion Decision

Meta-Ralph Quality Scores (0-12)

Scores on:

  • Actionability (can you act on it?)
  • Novelty (genuine insight vs. obvious)
  • Reasoning (explicit causal explanation)
  • Specificity (context-specific vs. generic)
  • Outcome-Linked (validated by results)

Promotion Criteria

Track 1 (Reliability):

  • Reliability ≥80% AND validated ≥5 times

Track 2 (Confidence):

  • Confidence ≥95% AND age ≥6 hours AND validated ≥5 times

Contradicted insights lose reliability automatically.

EIDOS Episodic Intelligence

Extracts structured rules from experience:

Type Description Example
Heuristics General rules of thumb "Always test before deploying"
Sharp Edges Things to watch out for "API rate limits hit at 100 req/min"
Anti-Patterns What not to do "Don't edit config without backup"
Playbooks Proven approaches "Database migration checklist"
Policies Enforced constraints "Must have tests for core modules"

Usage in QwenClaw

Basic Usage

# Start Spark pipeline
python -m spark.cli up

# Start QwenClaw (Spark captures automatically)
qwenclaw start

# Send task
qwenclaw send "Refactor the authentication module"

Check Spark Status

python -m spark.cli status
python -m spark.cli learnings

View Advisory History

python -m spark.cli advisories

Promote Insights Manually

python -m spark.cli promote <insight-id>

Obsidian Observatory

Spark auto-generates 465+ markdown pages with live Dataview queries:

What's Included

  • Pipeline Health - 12-stage pipeline detail pages
  • Cognitive Insights - Stored insights with reliability scores
  • EIDOS Episodes - Pattern distillations
  • Advisory Decisions - Pre-tool guidance history
  • Explorer Views - Real-time data exploration
  • Canvas View - Spatial pipeline visualization

Auto-Sync

Observatory syncs every 120 seconds when pipeline is running.

Measurable Outcomes

Advisory Source Effectiveness

Source What It Provides Effectiveness
Cognitive Validated session insights ~62% (dominant)
Bank User memory banks ~10%
EIDOS Pattern distillations ~5%
Baseline Static rules ~5%
Trigger Event-specific rules ~5%
Semantic BM25 + embedding retrieval ~3%

Timeline to Value

Time What Happens
Hour 1 Spark starts capturing events
Hour 2-4 Patterns emerge (tool effectiveness, error patterns)
Day 1-2 Insights get promoted to project files
Week 1+ Advisory goes live with pre-tool guidance

Integration Examples

Example 1: Preventing Recurring Errors

QwenClaw: About to run: npm install
Spark: ⚠️ WARNING
  Last 3 times you ran `npm install` without --legacy-peer-deps,
  it failed with ERESOLVE errors.
  Suggestion: Use `npm install --legacy-peer-deps`
  Reliability: 0.94 | Validated: 8 times

Example 2: Auto-Promoting Best Practices

User: "Remember: always run tests before committing"
Spark: Captured (score: 0.78)
→ After 5 successful validations:
  Promoted to CLAUDE.md:
  "## Testing Policy
   Always run tests before committing changes.
   Validated: 12 times | Reliability: 0.96"

Example 3: Domain-Specific Expertise

Domain Chip: React Development
Spark Advisory:
  ℹ️ NOTE
  In this project, useEffect dependencies are managed
  with eslint-plugin-react-hooks.
  Missing dependencies auto-fixed 23 times.
  Reliability: 0.89

Configuration

spark.config.yaml

spark:
  enabled: true
  session_id: qwenclaw-${timestamp}
  
  hooks:
    pre_tool_use: true
    post_tool_use: true
    user_prompt: true
    
  advisory:
    enabled: true
    min_score: 0.48
    cooldown_seconds: 300
    
  memory:
    auto_capture: true
    min_importance: 0.55
    
  observatory:
    enabled: true
    sync_interval_seconds: 120

Best Practices

1. Let Spark Learn Naturally

Just use QwenClaw normally. Spark captures and learns in the background.

2. Review Advisories

Pay attention to pre-tool warnings. They're based on validated patterns.

3. Provide Explicit Feedback

Tell Spark what to remember:

  • "Remember: always use --force for this legacy package"
  • "I prefer yarn over npm in this project"

4. Check Observatory

Review the Obsidian vault to understand what Spark has learned.

5. Promote High-Value Insights

Manually promote insights that are immediately valuable.

Skill Metadata

name: spark-intelligence
version: 1.0.0
category: automation
description: Self-evolving AI companion that captures, distills, and delivers 
             actionable insights from QwenClaw sessions
author: Vibeship (https://github.com/vibeforge1111/vibeship-spark-intelligence)
license: MIT
tags:
  - learning
  - memory
  - advisory
  - self-improving
  - local-first
  - obsidian

Resources

Spark Intelligence transforms QwenClaw from a stateless executor into a learning system! 🧠✨

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