Skills & Agent Building
5,949 results
- Bmadbmad-code-orgAnswers BMad questions and recommends the next skill from what is installed. Use when the user asks bmad for help, what to do next or where to start; to set up, update, repair, doctor, migrate or check the status of the installation, or add modules; or to see or change the active initiative.
- Bmad Agent Devbmad-code-orgSenior software engineer who implements stories and code changes. Use when the user asks to talk to Amelia or requests the developer agent
- Bmad Agent Pmbmad-code-orgProduct manager for PRD creation and requirements discovery. Use when the user asks to talk to John or requests the product manager
- Bmad Customizebmad-code-orgAuthors and updates customization overrides for installed BMad skills. Use when the user says 'customize bmad', 'override a skill', 'change agent behavior', or 'customize a workflow'
- Bmad Deep Reconbmad-code-orgDecision-grade research on a topic or this repository. Use when the user says "deep recon", "research this", "draft a research prompt", "process this research report", "market research", "domain research", "technical research", "competitor research", "what users say about X", "literature review", "help me choose between", "research the codebase", "research how X works here" or "why is X built…
- Bmad Helpbmad-code-orgAnalyzes current state and user query to answer BMad questions or recommend the next skill(s) to use. Use when user asks for help, bmad help, what to do next, or what to start with in BMad
- Bmad Party Modebmad-code-orgOrchestrates lively group discussions between installed BMAD agents or custom personas, and helps author custom parties. Use when the user requests party mode, a roundtable, or multiple agent perspectives — or wants to create/configure a party, define personas, or build an AI focus-group panel
- Bmad Retrospectivebmad-code-orgbmad-retrospective — an installable skill for AI agents.
- Bmad Reviewbmad-code-orgRuns one or more installed review lenses — adversarial critique, edge cases, verification gaps, structure, prose — and reports triaged findings. Use when, and only when, the user asks you to review a diff, a pull request, or an artifact — code or documents, one or many — and actually says "review"; an explicit skill:bmad-review directive from another skill counts as that ask.
- Bmad Specbmad-code-orgCondense any input — an idea, brief, PRD, transcript, or mixed notes — into a short spec: one kernel file plus supporting files that downstream skills build from. Also updates and validates existing specs. Use when the user says "create a spec", "distill this into a spec", "validate this spec", or "update the spec"
- Bmad Toolsmithbmad-code-orgSmithy the Toolsmith builds and maintains BMad skills, agents and modules. Use when the user asks for Smithy or says "build a skill", "make an agent", "scaffold a skill", "turn my session logs into a skill", "fix this skill", "convert this prompt to a skill", "review this skill", "optimize this skill", "cut this skill down", "make this skill BMad compliant", "package a module", "validate my…
- Bmod Core Toolsbmad-code-orgRequired bmod metadata. Never invoke this skill.
- Bmod Methodbmad-code-orgRequired bmod metadata. Never invoke this skill.
- Bmod Toolsmithbmad-code-orgRequired bmod metadata. Never invoke this skill.
- Gh Triagebmad-code-orgFetch all GitHub issues via gh CLI and use AI agents to deeply analyze, cluster, and prioritize issues with actual understanding. Use for issue triage, backlog cleanup, or when user mentions "issues", "triage", or "backlog".
- Chrome DevtoolsChromeDevToolsUses Chrome DevTools via MCP for efficient debugging, troubleshooting and browser automation. Use when debugging web pages, automating browser interactions, analyzing performance, or inspecting network requests. This skill does not apply to `--slim` mode (MCP configuration).
- ChromeDevTools/chrome-devtools-mcpChromeDevToolsCollectionChrome DevTools for coding agents
- Memory Leak DebuggingChromeDevToolsDiagnoses and resolves memory leaks in JavaScript/Node.js applications. Use when a user reports high memory usage, OOM errors, or wants to capture, compare, or inspect heap snapshots with Chrome DevTools MCP memory tools.
- AeonK-DenseThis skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches.
- AnndataK-DenseData structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.
- ArborK-DenseApplies Arbor Hypothesis Tree Refinement to research artifacts with repeatable evaluators, including model training, agent harnesses, data synthesis and benchmark optimization. Uses persistent hypotheses, isolated experiments, evidence propagation and held-out candidate comparison for multi-experiment research runs.
- Protocolsio IntegrationK-DenseReads, validates, and safely exports protocols.io data with current official REST/MCP contracts, or creates non-executing mutation plans. The bundled client makes bounded official-host GET requests only with explicit --execute. Use only for tasks explicitly targeting protocols.io or an exact protocols.io protocol version.
- Stable Baselines3K-DenseProduction-ready reinforcement learning algorithms (PPO, SAC, DQN, TD3, DDPG, A2C) with scikit-learn-like API. Use for standard RL experiments, quick prototyping, and well-documented algorithm implementations. Best for single-agent RL with Gymnasium environments. For high-performance parallel training, multi-agent systems, or custom vectorized environments, use pufferlib instead.
- Stable Baselines3K-DenseTrains and evaluates single-agent reinforcement learning with Stable Baselines3 (PPO, SAC, DQN, TD3, DDPG, A2C), Gymnasium custom environments, vectorized rollouts, callbacks, and checkpoint normalization. Applies to reproducible RL experiments, continuous control, discrete actions, and SB3-Contrib recurrent or masked policies.