cicd-intelligent-recovery

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-runs.json

# Download logs for each failure
cat .claude/.artifacts/failed-runs.json | jq -r '.databaseId' | while read RUN_ID; do
  gh run view $RUN_ID --log \
    > .claude/.artifacts/failure-logs-$RUN_ID.txt
done

Parse Failure Data

node <<'EOF'
const fs = require('fs');
const failures = [];

// Parse all failure logs
const logFiles = fs.readdirSync('.claude/.artifacts')
  .filter(f => f.startsWith('failure-logs-'));

logFiles.forEach(file => {
  const log = fs.readFileSync(`.claude/.artifacts/${file}`, 'utf8');

  // Extract structured failure data
  const failureMatches = log.matchAll(/FAIL (.+?):(\d+):(\d+)\n(.+?)\n(.+)/g);

  for (const match of failureMatches) {
    failures.push({
      file: match[1],
      line: parseInt(match[2]),
      column: parseInt(match[3]),
      testName: match[4],
      errorMessage: match[5],
      runId: file.match(/failure-logs-(\d+)/)[1]
    });
  }
});

fs.writeFileSync(
  '.claude/.artifacts/parsed-failures.json',
  JSON.stringify(failures, null, 2)
);

console.log(`✅ Parsed ${failures.length} failures`);
EOF

Validation Checkpoint:

• ✅ Failure data parsed and structured
• ✅ All required fields present (file, line, testName, errorMessage)

Step 2: AI-Powered Analysis

Objective: Use Gemini large-context analysis + 7 research agents with Byzantine consensus to examine each failure deeply.

Evidence-Based Techniques: Self-consistency, Byzantine consensus, program-of-thought

Phase 1: Gemini Large-Context Analysis

Leverage Gemini's 2M token window for full codebase analysis

# Analyze failures with full codebase context
/gemini:impact "Analyze CI/CD test failures:

FAILURE DATA:
$(cat .claude/.artifacts/parsed-failures.json)

CODEBASE CONTEXT:
Full repository (all files)

LOOP 2 IMPLEMENTATION:
$(cat .claude/.artifacts/loop2-delivery-package.json)

ANALYSIS OBJECTIVES:
1. Identify cross-file dependencies related to failures
2. Detect failure cascade patterns (root → secondary → tertiary)
3. Analyze what changed between working and failing states
4. Assess system-level architectural impact
5. Identify connascence patterns in failing code

OUTPUT FORMAT:
{
  dependency_graph: { nodes: [files], edges: [dependencies] },
  cascade_map: { root_failures: [], cascaded_failures: [] },
  change_analysis: { changed_files: [], change_impact: [] },
  architectural_impact: { affected_systems: [], coupling_issues: [] }
}"

# Store Gemini analysis
cat .claude/.artifacts/gemini-response.json \
  > .claude/.artifacts/gemini-analysis.json

Phase 2: Parallel Multi-Agent Deep Dive (Self-Consistency)

7 parallel agents for cross-validation and consensus

// PARALLEL ANALYSIS AGENTS - Evidence-Based Self-Consistency
[Single Message - Spawn All 7 Analysis Agents]:

  // Failure Pattern Research (Dual agents for cross-validation)
  Task("Failure Pattern Researcher 1",
    `Research similar failures in external sources:
    - GitHub issues for libraries we use
    - Stack Overflow questions with similar error messages
    - Documentation of known issues

    Failures to research: $(cat .claude/.artifacts/parsed-failures.json | jq -r '.[].errorMessage')

    For each failure:
    1. Find similar reported issues
    2. Document known solutions with evidence (links, code examples)
    3. Note confidence level (high/medium/low)

    Store findings: .cla