sme-agent-protocol
Mandatory protocol for all SME (Subject Matter Expert) agents. Defines fact-finding requirements, output contracts, confidence/risk assessment, and qualification of advice.
浏览 tachyon-beep 维护的已发布 AI 技能,比较用途与说明,并通过来源仓库了解安装方式。
已收录技能
52收录技能的仓库 Stars 合计
300仓库 Forks
90平均每个技能的仓库 Stars
6同一仓库中的多个技能可能共享 Stars,合计包含重复仓库数据。
Mandatory protocol for all SME (Subject Matter Expert) agents. Defines fact-finding requirements, output contracts, confidence/risk assessment, and qualification of advice.
Use when user asks about E2E testing, performance testing, chaos engineering, test automation, flaky tests, test data management, or quality practices - routes to specialist reference sheets with deep expertise...
Use when deciding test strategy, struggling with code reviews, shipping without tests, or conflating verification with validation
Use when users request SDLC guidance, CMMI processes, requirements management, design documentation, quality assurance, governance, metrics, or adopting processes on existing projects
Use when implementing CMMI processes in GitHub or Azure DevOps, migrating between platforms, or establishing traceability/compliance on GitHub/Azure - platform-specific process guidance
Use when making architecture decisions, setting up CI/CD, managing technical debt, or choosing branching strategies - enforces ADR requirements and prevents resume-driven design
Master reward function design - potential-based shaping, hacking patterns, validation
Use when stakeholders pressure you to change technical priorities and you're tempted to compromise on security-first or call it synthesis - enforces risk-based prioritization over stakeholder preferences
Master ε-greedy, UCB, curiosity-driven, RND, intrinsic motivation exploration
Use when cataloging technical debt under time pressure and tempted to explain choices instead of delivering document - enforces execution discipline with scoped delivery patterns for partial catalogs
Systematic RL debugging - diagnose learning failures, common bugs, diagnosis trees
Master CQL, IQL, BCQ - offline RL from fixed datasets without environment interaction
Master DQN, Double DQN, Dueling DQN, Rainbow - value-based methods for discrete actions
Master REINFORCE, PPO, TRPO - direct policy optimization with trust regions
Master Dyna-Q, MBPO, Dreamer - model-based RL with world models and planning
Validate architecture artifacts against contracts and consistency requirements - codifies systematic validation practices with professional reporting
Generate C4 architecture diagrams from subsystem catalogs - codifies abstraction strategies, notation conventions, and selection criteria
Use when analyzing existing codebases to generate architecture documentation - coordinates subagent-driven exploration with mandatory workspace structure, validation gates, and pressure-resistant workflows
Gym/gymnasium API - custom environments, spaces, wrappers, vectorization, debugging
Master A2C, A3C, SAC, TD3 - actor-critic methods for continuous control
Routes to appropriate deep-RL skills based on problem type and algorithm family
Use when assessing codebase architecture and you feel pressure to soften critique, lead with strengths, or frame problems diplomatically - provides evidence-based critical assessment resisting relationship and...
Master QMIX, MADDPG, CTDE - multi-agent learning with coordination and credit assignment
Analyze unfamiliar codebases systematically to produce subsystem catalog entries - emphasizes strict contract compliance and confidence marking
默认按已收录数据中的 GitHub 仓库 Stars 排序。Stars 属于来源仓库,同一仓库的多个技能可能共享相同数值;它反映仓库关注度,不代表单个技能的安装量或质量评分。