Best use case
Playbook Learning System is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
**Version:** 1.0.0
Teams using Playbook Learning System should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/playbook/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How Playbook Learning System Compares
| Feature / Agent | Playbook Learning System | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
**Version:** 1.0.0
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Playbook Learning System
**Version:** 1.0.0
**Purpose:** Store and query successful CFN Loop patterns for continuous improvement
## Overview
The playbook system stores execution patterns from successful CFN Loops:
- Task patterns (type, description, keywords)
- Optimal agent configurations
- Iteration counts and convergence patterns
- Common feedback themes
- Success strategies
## Database Schema
**File:** `.claude/skills/playbook/playbook.db` (SQLite)
**Tables:**
### playbook_entries
```sql
CREATE TABLE playbook_entries (
id INTEGER PRIMARY KEY AUTOINCREMENT,
task_pattern TEXT NOT NULL, -- Task description or embedding
task_type TEXT NOT NULL, -- software-development, content-creation, etc.
task_keywords TEXT, -- Comma-separated keywords
loop3_agents TEXT NOT NULL, -- JSON array
loop2_agents TEXT NOT NULL, -- JSON array
loop4_agent TEXT DEFAULT 'product-owner',
iterations_required INTEGER,
final_confidence REAL,
final_consensus REAL,
gate_threshold REAL DEFAULT 0.75,
consensus_threshold REAL DEFAULT 0.90,
complexity TEXT, -- low, medium, high
estimated_iterations INTEGER,
actual_iterations INTEGER,
common_feedback TEXT, -- JSON array of recurring themes
success_strategy TEXT, -- JSON object
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
use_count INTEGER DEFAULT 1 -- How many times this pattern was reused
);
CREATE INDEX idx_task_type ON playbook_entries(task_type);
CREATE INDEX idx_task_pattern ON playbook_entries(task_pattern);
CREATE INDEX idx_final_confidence ON playbook_entries(final_confidence DESC);
CREATE INDEX idx_use_count ON playbook_entries(use_count DESC);
```
### agent_performance
```sql
CREATE TABLE agent_performance (
id INTEGER PRIMARY KEY AUTOINCREMENT,
agent_type TEXT NOT NULL,
task_type TEXT NOT NULL,
avg_confidence REAL,
execution_count INTEGER DEFAULT 1,
success_rate REAL,
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
UNIQUE(agent_type, task_type)
);
CREATE INDEX idx_agent_performance ON agent_performance(agent_type, task_type);
```
## Usage
### Query Playbook
```bash
SIMILAR_PATTERN=$(./.claude/skills/playbook/query-playbook.sh \
--task-type "software-development" \
--description "Implement OAuth2 authentication")
echo "$SIMILAR_PATTERN" | jq '.loop3_agents'
# ["backend-dev", "security-specialist"]
```
### Update Playbook
```bash
./.claude/skills/playbook/update-playbook.sh \
--task-id "$TASK_ID" \
--task-type "software-development" \
--description "Implement JWT authentication" \
--loop3-agents "backend-dev,security-specialist" \
--loop2-agents "reviewer,tester,security-auditor" \
--iterations 3 \
--final-confidence 0.92 \
--final-consensus 0.93
```
## Similarity Matching
**Algorithm:** Keyword overlap (Jaccard similarity)
```
Similarity = Intersection(keywords1, keywords2) / Union(keywords1, keywords2)
```
**Thresholds:**
- ≥ 0.6: Potentially similar
- ≥ 0.75: Similar (use this pattern)
- ≥ 0.90: Very similar (high confidence match)
## Auto-Update from Retrospectives
**Location:** `lib/auto-update/auto-update-playbook.sh`
Automatically update playbook with insights from sprint retrospectives.
### Auto-Update Features
- Parse retrospective JSON
- Update agent performance metrics
- Store successful strategies
- Increment pattern counters
- Maintain historical performance data
### Auto-Update Usage
```bash
./.claude/skills/cfn-playbook/lib/auto-update/auto-update-playbook.sh \
--retrospective-json "$RETROSPECTIVE_JSON" \
--task-id "$TASK_ID"
```
### Safety Mechanisms
- Validation of input data
- Backup of previous playbook version
- Logging of all modifications
- Ability to revert changes
## Integration
Used by:
- `.claude/agents/cfn-v3-coordinator.md` - Query for similar tasks
- Main Chat post-execution - Update playbook after PROCEED
- Loop 5 retrospective - Extract patterns and update playbook
- CodeSearch integration - Auto-update via semantic analysis
## Directory Structure
```
cfn-playbook/
├── SKILL.md # This file
├── playbook.db # SQLite database
├── init-playbook.sh # Initialize database
├── query-playbook.sh # Query patterns
├── update-playbook.sh # Manual updates
└── lib/
└── auto-update/
└── auto-update-playbook.sh # Retrospective auto-updates
```Related Skills
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!/bin/bash
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