**Version:** 1.0.0

MUST BE USED after every CFN Loop execution to capture successful patterns. Query before repeating any task type to retrieve prior learnings and avoid re-solving solved problems. Organizational learning from CFN Loop execution - workflow codification and playbooks.

Structured root-cause debugging with 5-phase protocol, 3-strike escalation, and scope lock. Use when diagnosing bugs, system failures, or unexpected behavior. Enforces investigation before fixes.

## Purpose

## Purpose

Human intervention detection and orchestration for CFN Loop. Use when automated processes need human oversight, when escalation is required, or when managing intervention workflows and approval gates.

GOAP-based planning bookend. Run BEFORE plan mode to model goal state and derive optimal action sequence. Run DURING implementation when 3-strike rule fires to replan from current world state.

Update CFN system expert agent with relevant git commits and project changes

Unified error handling, batching, and logging for CFN Loop. Use when you need to capture agent errors, batch multiple errors for processing, log structured error data, or categorize and recover from agent failures.

Converts natural language epic documents from markdown into structured JSON configurations for MDAP or CFN Loop execution. Use when you need to parse epic documents, validate epic structure, or generate execution configurations from planning documents.

Creates comprehensive epic definitions with sequential reviews from 10 key personas. Use when you need to analyze requirements from multiple perspectives and generate structured epic documentation with cost estimates and risk assessments.

Hybrid AISP epic creation with formal API contracts and natural language user content. Uses AISP (AI Symbolic Protocol) for type definitions and agent binding contracts while preserving human-readable descriptions.

Creates and manages file backups before edit operations in agent workflows.

MUST BE USED before merging any PR that adds 3+ new functions or 50+ lines. Run after implementation to identify DRY violations before they land. Code review for DRY violations, modularity improvements, and resumable pipeline opportunities. Outputs a JSON manifest for cfn-vote-implement.

Skill Promotion Workflow - Atomic promotion from staging to production

Automated skill deployment pipeline for CFN Loop integration

Skill deployment and promotion lifecycle management. Use when transitioning skills from APPROVED to DEPLOYED status, or promoting skills from staging to production with SLA enforcement.

Unified atomic ingestion of CFN dependency manifests (cli-mode, shared)

## Purpose

Task dependency extraction and context ingestion. Use when you need to parse task criteria, identify dependencies, generate execution order, or inject context for complex multi-step tasks.

Agent coordination patterns for task-mode multi-agent workflows. Use when planning how to spawn agents, sequence dependencies, or aggregate results in a single message. Covers chain, broadcast, mesh, and consensus collection without Redis.

Sync conversation history from .codex/sessions to .claude.json for context preservation

## Overview

Configuration management and environment sanitization. Use when you need to manage configuration files, validate environment variables, sanitize sensitive data, or update system settings across CFN Loop components.