skills-with-evals

Plugin skills-with-evals in frankxai/claude-skills-library. Commands read from the repo's manifest — today.

Skills (10)

• agentic-orchestration

  Patterns for multi-agent coordination, task decomposition, handoffs, and workflow orchestration. Use when designing or debugging agent systems, splitting a large task across sub-agents, defining handoff contracts, or choosing an orchestration topology.

• ai-architect-review

  Review the architecture of a system that calls a language model. Use when adding an agent loop, a retrieval path, a tool surface, or an MCP server to a codebase; when a system that worked in a demo is being prepared for production; or when asked to audit, review, or plan AI/agent architecture. Check

• feynman-thinking

  Transform Claude into a Feynman mentor. Focuses on simple explanations, architectural models, Socratic questioning, and stripping away unnecessary complexity. Triggers on: feynman, feynman alignment, explain simply, explain architecture, explain logic, explain concept, build mental model.

• hooks-automation

  Automated coordination, formatting, and learning from Claude Code operations using intelligent hooks with MCP integration. Includes pre/post task hooks, session management, Git integration, memory coordination, and neural pattern training for enhanced development workflows. Use when configuring Clau

• langgraph-patterns

  Production-grade agentic workflows with LangGraph — graph orchestration, state machines, human-in-the-loop, and advanced control flow. Use when building or debugging a LangGraph app, modeling agent state, or adding checkpoints and human approval steps.

• loop-designer

  Compile a goal into a durable, file-backed agent loop — a .loop/<name>/ charter any coding agent (Claude Code, Codex, Gemini, Grok) can execute unattended. Use when the user wants to "build a loop", "design a loop", "run X continuously/24-7", "make an agent keep working on Y", automate recurring wor

• mcp-architecture

  Design and implement Model Context Protocol servers with resources, tools, prompts, and security best practices. Use when architecting an MCP server, modeling its resources/tools/prompts, or integrating a service as an MCP endpoint.

• model-routing

  Intelligent model selection — routes tasks to Haiku (fast/cheap), Sonnet (balanced), or Opus (complex/strategic) by analyzing task complexity. Use when deciding which model to run, optimizing cost vs quality, or building a tiered routing layer.

• todo-discipline

  Use when starting any complex task (3+ steps) or before declaring work complete. Enforces todo/task state to match reality — merge updates + read verification. Prevents false completion.

• verification-quality

  Truth scoring, code-quality verification, and automatic rollback with a high accuracy threshold. Use when validating agent output before it ships, gating changes on quality, or adding a verify-and-rollback safety loop.