cicd-intelligent-recovery
Loop 3 of the Three-Loop Integrated Development System. CI/CD automation with intelligent failure recovery, root cause analysis, and comprehensive quality validation. Receives implementation from Loop 2, feeds failure patterns back to Loop 1. Achieves 100% test success through automated repair and theater validation. v2.0.0 with explicit agent SOPs.
Installation
Follow the repository instructions for this skill and your agent. Review the files and dependencies before adding it to your workflow.
View source ↗GuidesSkill instructions
CI/CD Quality & Debugging Loop (Loop 3)
Purpose: Continuous integration with automated failure recovery and authentic quality validation.
SOP Workflow: Specification → Research → Planning → Execution → Knowledge
Output: 100% test success rate with authentic quality improvements and failure pattern analysis
Integration: This is Loop 3 of 3. Receives from parallel-swarm-implementation (Loop 2), feeds failure data back to research-driven-planning (Loop 1).
Version: 2.0.0 Optimization: Evidence-based prompting with explicit agent SOPs
When to Use This Skill
Activate this skill when:
- Have complete implementation from Loop 2 (parallel-swarm-implementation)
- Need CI/CD pipeline automation with intelligent recovery
- Require root cause analysis for test failures
- Want automated repair with connascence-aware fixes
- Need validation of authentic quality (no theater)
- Generating failure patterns for Loop 1 feedback
DO NOT use this skill for:
- Initial development (use Loop 2 first)
- Manual debugging without CI/CD integration
- Quality checks during development (use Loop 2 theater detection)
Input/Output Contracts
Input Requirements
input:
loop2_delivery_package:
location: .claude/.artifacts/loop2-delivery-package.json
schema:
implementation: object (complete codebase)
tests: object (test suite)
theater_baseline: object (theater metrics from Loop 2)
integration_points: array[string]
validation:
- Must exist and be valid JSON
- Must include theater_baseline for differential analysis
ci_cd_failures:
source: GitHub Actions workflow runs
format: JSON array of failure objects
required_fields: [file, line, column, testName, errorMessage, runId]
github_credentials:
required: gh CLI authenticated
check: gh auth status
Output Guarantees
output:
test_success_rate: 100% (guaranteed)
quality_validation:
theater_audit: PASSED (no false improvements)
sandbox_validation: 100% test pass
differential_analysis: improvement metrics
failure_patterns:
location: .claude/.artifacts/loop3-failure-patterns.json
feeds_to: Loop 1 (next iteration)
schema:
patterns: array[failure_pattern]
recommendations: object (planning/architecture/testing)
delivery_package:
location: .claude/.artifacts/loop3-delivery-package.json
contains:
- quality metrics (test success, failures fixed)
- analysis data (root causes, connascence context)
- validation results (theater, sandbox, differential)
- feedback for Loop 1
Prerequisites
Before starting Loop 3, ensure Loop 2 completion:
# Verify Loop 2 delivery package exists
test -f .claude/.artifacts/loop2-delivery-package.json && echo "✅ Ready" || echo "❌ Run parallel-swarm-implementation first"
# Load implementation data
npx claude-flow@alpha memory query "loop2_complete" --namespace "integration/loop2-to-loop3"
# Verify GitHub CLI authenticated
gh auth status || gh auth login
8-Step CI/CD Process Overview
Step 1: GitHub Hook Integration (Download CI/CD failure reports)
↓
Step 2: AI-Powered Analysis (Gemini + 7-agent synthesis with Byzantine consensus)
↓
Step 3: Root Cause Detection (Graph analysis + Raft consensus)
↓
Step 4: Intelligent Fixes (Program-of-thought: Plan → Execute → Validate → Approve)
↓
Step 5: Theater Detection Audit (6-agent Byzantine consensus validation)
↓
Step 6: Sandbox Validation (Isolated production-like testing)
↓
Step 7: Differential Analysis (Compare to baseline with metrics)
↓
Step 8: GitHub Feedback (Automated reporting and loop closure)
Step 1: GitHub Hook Integration
Objective: Download and process CI/CD pipeline failure reports from GitHub Actions.
Agent Coordination: Single orchestrator agent manages data collection.
Configure GitHub Hooks
# Install GitHub CLI if needed
which gh || brew install gh
# Authenticate
gh auth login
# Configure webhook listener
gh api repos/{owner}/{repo}/hooks \
-X POST \
-f name='web' \
-f active=true \
-f events='["check_run", "workflow_run"]' \
-f config[url]='http://localhost:3000/hooks/github' \
-f config[content_type]='application/json'
Download Failure Reports
# Get recent workflow runs
gh run list --repo {owner}/{repo} --limit 10 --json conclusion,databaseId \
| jq '.[] | select(.conclusion == "failure")' \
> .claude/.artifacts/failed-r
Examples
Read the usage examples in the source repository.
View source ↗Source history
- Indexed on
- 2026-01-20
- Index updated
- 2026-01-28
- Branch
- main
Index and sync dates describe this directory. View the source repository for code history.
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