Browse reusable Agent Skills, each with a clear purpose and practical guidance.
Design agent system prompts, parallel architectures, and methodological guardrails for data science decision-packs. Use when creating orchestrator, subagent, or parallel agent systems for analytical workflows. Covers anti-fabrication rules, epistemic humility, when to stop, conflict detection, uncertainty reporting, retry protocols, prompt design principles, and the decision-lab runtime mechanics.
Complete reference for decision-lab (dlab). Use when the user asks about creating decision-packs, designing data science agents, running sessions, analyzing results, or anything related to dlab CLI, agent architecture, parallel subagents, or decision-pack configuration. Covers the full workflow from scaffolding to analysis.
Analyze your Model Matchmaker override patterns and tune the local classifier to match your preferences. Use after collecting 50+ recommendations.
技能扬弃（Sublation/Aufhebung）——把执行经验沉淀为观测、候选、验证、独立复核、用户批准、晋升和观察窗的可审计治理链路；显式触发时可用 one-shot 控制面自动推进到用户决策门。
Streamlines the process of adding new tools to the MCP server.
Write generation rules using bundled references in this repo (airgap). Full skill body lives in runwhen-platform-mcp.
Detect requests for iterative autonomous agent loops and route to the appropriate loop executor
Crash-resilient external agent loop with state persistence and CI/CD integration
Cross-framework enhancement overlay for choosing a multi-agent topology BEFORE writing any agent. A binary-question rubric — is single-agent + tools enough? do agents need to know about each other? does the output need one voice? — maps the answer to single-agent / supervisor / swarm / sequential / hierarchical. Activates when a coder agent is tempted to "split the work into roles" or reaches for a multi-agent framework. Encodes the *selection rubric* that the per-framework skills assume but never surface. Search keywords: when to use multi-agent, single vs multi agent, do I need multiple agents, supervisor vs swarm, multi-agent vs single agent, agent team design.