Run automated post-release workflow (eval baselines, dashboard, docs) for AILANG releases. Executes 46-benchmark suite (medium/hard/stretch) + full standard eval with validation and progress reporting. Use when user says "post-release tasks for vX.X.X" or "update dashboard". Fully autonomous with pre-flight checks.
Comprehensive development workflow specialist combining TDD, debugging, performance optimization, code review, PR review, and quality assurance into unified development workflows
Apply Bootstrapped AI Methodology Engineering (BAIME) to develop project-specific methodologies through systematic Observe-Codify-Automate cycles with dual-layer value functions (instance quality + methodology quality). Use when creating testing strategies, CI/CD pipelines, error handling patterns, observability systems, or any reusable development methodology. Provides structured framework with convergence criteria, agent coordination, and empirical validation. Validated in 8 experiments with 100% success rate, 4.9 avg iterations, 10-50x speedup vs ad-hoc. Works for testing, CI/CD, error recovery, dependency management, documentation systems, knowledge transfer, technical debt, cross-cutting concerns.
Comprehensive CI/CD pipeline methodology with quality gates, release automation, smoke testing, observability, and performance tracking. Use when setting up CI/CD from scratch, build time over 5 minutes, no automated quality gates, manual release process, lack of pipeline observability, or broken releases reaching production. Provides 5 quality gate categories (coverage threshold 75-80%, lint blocking, CHANGELOG validation, build verification, test pass rate), release automation with conventional commits and automatic CHANGELOG generation, 25 smoke tests across execution/consistency/structure categories, CI observability with metrics tracking and regression detection, performance optimization including native-only testing for Go cross-compilation. Validated in meta-cc with 91.7% pattern validation rate (11/12 patterns), 2.5-3.5x estimated speedup, GitHub Actions native with 70-80% transferability to GitLab CI and Jenkins.
Security-first dependency management methodology with batch remediation, policy-driven compliance, and automated enforcement. Use when security vulnerabilities exist in dependencies, dependency freshness low (outdated packages), license compliance needed, or systematic dependency management lacking. Provides security-first prioritization (critical vulnerabilities immediately, high within week, medium within month), batch remediation strategy (group compatible updates, test together, single PR), policy-driven compliance framework (security policies, freshness policies, license policies), and automation tools for vulnerability scanning, update detection, and compliance checking. Validated in meta-cc with 6x speedup (9 hours manual to 1.5 hours systematic), 3 iterations, 88% transferability across package managers (concepts universal, tools vary by ecosystem).
Organizational learning from CFN Loop execution - workflow codification and playbooks. Use when you need to store successful patterns, query past learnings, track edge cases and failures, or retrieve agent configurations and iteration strategies from previous tasks.
Review technical recommendations before presenting them to the user. Use BEFORE suggesting config changes, optimizations, refactors, or architecture decisions. Spawns a subagent to challenge assumptions and filter out generic advice that doesn't apply to the user's context.
CFN Loop coordination and orchestration - gate checks, validation, consensus, coordination patterns. Use when orchestrating multi-agent workflows, managing iteration cycles, or coordinating Loop 2/Loop 3 dependencies.
Dependency health analysis to detect outdated packages and unused dependencies.
Safe minor dependency updates with validation (lint/test/build) and automatic rollback on failure.
Agent process spawning with provider configuration and execution
Operational commands, environment configuration, and deployment for PolyFlup.
Rollback failed deployments, restore previous versions, and handle deployment emergencies. Use when deployments fail, bugs are discovered in production, or emergency recovery is needed.
CFN Loop workflow orchestration - three-loop structure management with gate checks and consensus. Use when coordinating Loop 3 implementers and Loop 2 validators, managing iteration cycles, or enforcing quality gates.
Unified atomic ingestion of CFN dependency manifests (trigger-dev, cli-mode, shared)
Orchestrate Docker container execution across parallel agent waves with memory-aware spawning
Manage feature manifests for code traceability. Use when creating new features, updating existing features, checking feature health, or exploring the feature-to-code relationship. Activates for manifest validation, feature creation, changelog updates, and traceability queries.
Wave-based parallel scheduling for DAG execution. Manages execution order, resource allocation, and parallelism constraints. Activate on 'schedule dag', 'execution waves', 'parallel scheduling', 'task queue', 'resource allocation'. NOT for building DAGs (use dag-graph-builder) or actual execution (use dag-parallel-executor).
Interactive claude-flow orchestration coach that guides users through swarm topology selection, agent deployment, memory configuration, and SPARC workflows. Use when learning claude-flow, choosing between swarm vs hive-mind, selecting agents, or optimizing multi-agent coordination. NEVER auto-executes - always displays recommendations and asks first.
Expert help with RCC (Repeatable, Contained Code): creating/running robots, robot.yaml + conda.yaml, holotree environment caching, rcc task testrun vs rcc run, dependency management (conda-forge + pip), Playwright/robotframework-browser install (rccPostInstall: rfbrowser init), Control Room (rcc cloud), troubleshooting enterprise networks/proxies, and performance profiling with --pprof/--timeline/--trace.
Orchestrates multi-step agent operations with durable execution, automatic retries, and recovery from failures. Use this for complex workflows that need to survive server restarts or coordinate multiple agents.
Use when conducting comprehensive code review for pull requests across multiple quality dimensions. Orchestrates 12-15 specialized reviewer agents across 4 phases using star topology coordination. Covers automated checks, parallel specialized reviews (quality, security, performance, architecture, documentation), integration analysis, and final merge recommendation in a 4-hour workflow.
Execute approved plans using subagent orchestration. Use after oberplan produces a plan and user approves. Maintains a persistent execution checklist file that tracks all actions. Dispatches agents for each phase, enforces checkpoints with code-review subagents, and tracks progress. Triggers on "execute the plan", "run the plan", "implement the plan", "start execution", "dispatch agents for plan", or when you have an approved plan ready for implementation.
