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Found 9,752 Skills
Route generative media requests before any creative planning or provider execution. Use this when the user asks to generate, modify, dub, animate, or assemble image, video, audio, workflow, or analysis-derived media and the first decision is which generation controller should own the job.
Wallarm integration. Manage data, records, and automate workflows. Use when the user wants to interact with Wallarm data.
Whatfix integration. Manage data, records, and automate workflows. Use when the user wants to interact with Whatfix data.
Railway integration. Manage data, records, and automate workflows. Use when the user wants to interact with Railway data.
TextAnywhere integration. Manage data, records, and automate workflows. Use when the user wants to interact with TextAnywhere data.
Observe the user's screen via screenpipe, detect repeated research workflows, match them against existing academic-skills, and draft new skills (or composition recipes that chain existing ones) for the patterns not yet covered. Use when the user asks to analyze their recent work and propose skills based on what they actually do. Requires the screenpipe daemon (https://github.com/screenpipe/screenpipe) running locally on port 3030 — the skill has no other data source and will refuse to run if screenpipe is unreachable. All detection runs locally; only redacted cluster summaries reach the LLM.
Optional AI SDLC architecture workflow. Use when an AI assistant needs to define system boundaries, components, interfaces, architectural constraints, alternatives, decisions, tradeoffs, risks, or validation for a feature and produce routed human and machine artifacts linked to requirements and durable decisions. Supports `--quick-flow` for focused design and `--full-flow` for strict decision, risk, and validation coverage.
AI SDLC controlled change-workspace and specification-delta workflow. Use when an AI assistant needs to create or validate an isolated proposal workspace, author and validate requirement deltas, preview canonical changes, or apply and archive an explicitly approved change with rollback evidence. Supports `--quick-flow` for assumption-driven drafts and `--full-flow` for strict owner, target, evidence, and authority checks.
Use when PRFAQ, BRD, PRD, product brief, workflow, or equivalent initiative artifacts exist and you need to review them for planning gaps, unclear scope, weak priorities, missing actors, and backlog-blocking ambiguity before decomposing work. Supports `--quick-flow` for fast assumption-driven execution and `--full-flow` for question-driven verified execution. Explicit full or end-to-end spec refinement requests continue through the existing 18-stage refinement cascade.
AI SDLC package trust and privacy-preserving local metrics workflow. Use when an AI assistant needs to verify package origin, file integrity, harness compatibility, declared capabilities, provenance evidence, or generate reproducible aggregate run, retry, budget, coverage, and freshness metrics without collecting source, prompts, commands, or diffs. Supports `--quick-flow` and `--full-flow`.
AI SDLC reusable quality-lens workflow. Use when an AI assistant needs to challenge a requirement, design, plan, test strategy, change, or delivery artifact through pre-mortem, adversarial, edge-case, stakeholder-conflict, reversibility, abuse-case, operational-failure, or assumption lenses and finalize evidence-backed findings with ownership and traceability. Supports `--quick-flow` for selected high-value lenses and `--full-flow` for the complete applicable registry.
Designs, builds, and deploys AI agents or multi-agent systems on Google Cloud. Provides an interactive workflow to gather requirements, recommend a tailored architecture, and generate deployment instructions. Use when designing or implementing agentic systems on Google Cloud. Don't use for general Google Cloud solution architecture (use google-cloud-solution-architecture instead) or for narrow tasks targeting a single product without agent context.