greyhaven-ai

Multi-mode code quality analysis covering security reviews (OWASP Top 10), clarity refactoring (readability rules), synthesis analysis (cross-file issues), and DDD/DRY lenses (flags mechanical naming, primitive obsession, domain rules leaking into…

Comprehensive performance analysis and optimization for algorithms (O(n²)→O(n)), databases (N+1 queries, indexes), React (memoization, virtual lists), bundles (code splitting), API caching, and memory leaks. 85%+ improvement rate. Use when application is…

Design RESTful APIs following Grey Haven standards - FastAPI routes, Pydantic schemas, HTTP status codes, pagination, filtering, error responses, OpenAPI docs, and multi-tenant patterns. Use when creating API endpoints, designing REST resources, implementing…

Automate developer onboarding with personalized plans, Linear integration, knowledge base setup, and milestone tracking. Use when onboarding new developers, planning team expansion, or managing role transitions.

Generate pull request descriptions following Grey Haven Studio standards with clear summary, motivation, implementation details, testing strategy, and comprehensive checklist. Use when creating or reviewing pull requests.

Handle production incidents with SRE best practices including detection, investigation, mitigation, recovery, and postmortems. Use when dealing with production outages, SEV1/SEV2 incidents, creating postmortems, or updating runbooks.

Master TDD orchestration with multi-agent coordination, strict red-green-refactor enforcement, a Layer-0 domain-modeling gate (DDD) that blocks Red until concepts are named in domain vocabulary and primitives promoted to types, and principled DRY enforcement…

Python Test-Driven Development expertise with pytest, strict red-green-refactor methodology, domain-first modeling (DDD), principled DRY discipline, FastAPI testing patterns, and Pydantic model testing. Use when implementing Python features with TDD, writing…

Iterative strategy generation and evaluation system. Use when the user wants to evaluate agent output quality, run improvement loops, queue tasks for background evaluation, check run status, inspect runtime artifacts and session branch lineage, or discover…

Use when an agent needs to CREATE knowledge with Autocontext - run a scenario or plain-language task through the improvement loop, judge or improve a single output, and inspect what the run produced. Host-agnostic; requires only the autoctx CLI.

Use when a Hermes agent needs to evaluate agent behavior, run Autocontext scenarios, inspect Hermes curator state, export reusable knowledge, or prepare local MLX/CUDA training data through the autoctx CLI.

Use when an agent needs to USE knowledge Autocontext already produced - find which scenarios have knowledge, read the playbook and lessons for one, understand the on-disk file and folder layout, and move knowledge between checkouts. Host-agnostic; requires…

Operational knowledge for the grid_ctf scenario including strategy playbook, lessons learned, and resource references. Use when generating, evaluating, coaching, or debugging grid_ctf strategies.

Use when running the experimental Lean formal-proof harness, validating machine-checked proof candidates, benchmarking playbook transfer, or invoking the pi-autocontext-lean-verify package. Triggers: Lean proof repair, formal proof, theorem proving,…