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Found 1,195 Skills
Coordinate multiple specialized Skills and Task Agents through parallel, sequential, swarm, hybrid, or iterative execution strategies. Use when orchestrating multi-worker workflows, managing dependencies, or optimizing complex task execution with quality gates.
Run a full-scale implementation review with parallel subagents for plan alignment, UI verification, technical and strategic analysis, and test coverage gap closure across app and database layers.
Use when the user asks to "plan this feature", "plan refactor", "research & plan", "plan auth/API/work", or needs multi-step work with evidence-based planning before coding. Understands → Researches (via Local Search/Research) → Plans → Implement. No guessing; validates with code.
Workflow orchestrator for Spec Driven Development. Coordinates skills and tracks progress. speckit workflow, spec driven development, speckit commands.
Orchestrate a specialized software development agent team. Receive user requests, classify task type, select the matching workflow, delegate each step to specialist agents via the Agent tool, and assemble the final output. Use when the user needs multi-step software development involving architecture, implementation, testing, security review, or code review. Also use for production incident investigation — when the user reports a live system issue, service outage, pod crash, data anomaly, or needs root cause analysis using kubectl, psql, argocd, or docker. Trigger this skill whenever a task involves more than one concern (e.g., "add a new endpoint" needs BA + Architect + Developer + QA + Security), when the user mentions team coordination, agent delegation, or when the work clearly benefits from multiple specialist perspectives rather than a single implementation pass.
Coordinates multi-session, delegated, or long-running work with persistent state, recovery checks, and explicit status transitions. Use when a task spans multiple turns, multiple agents, background jobs, or scheduled loops, or when interrupted work must be resumed reliably.
A meta-skill that understands task requirements, dynamically selects appropriate skills, tracks successful skill combinations using agent-memory-mcp, and prevents skill overuse for simple tasks.
Automated content production pipeline: hot topic aggregation from 10+ platforms (Bilibili, GitHub, Reddit, YouTube, Weibo, Zhihu, etc.), AI-powered topic scoring, multi-platform content generation (Xiaohongshu, WeChat, Twitter), draft review, and auto-publishing. Use when: user wants daily content pipeline, hot topic collection, content generation, article publishing, or content factory automation.
Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for forced-auth coercion, relay chains, target selection, NTLM or related acceptance paths, and coercion-to-privilege transitions. Use when the user asks to trace a coercion primitive, follow a relay path, analyze forced authentication, determine which service accepts relayed auth, or connect a coercion step to resulting privilege, enrollment, or code execution. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.
Master dispatcher for all MLflow workflows. Use this skill when the user wants to do anything with MLflow — tracing, evaluating, debugging, or improving an agent. Routes to the right MLflow sub-skill automatically. Triggers on: "use mlflow", "help with mlflow", "mlflow agent", "add mlflow to my project", "trace my agent", "evaluate my agent", or any MLflow task without a specific skill in mind.
Retrieve a GitHub issue using the `gh` CLI, analyze it, and spawn a PM + developer team to address it. Accepts an issue URL, issue number, or `owner/repo#number`.
Design data pipelines covering ETL vs ELT architectures, data source integration, scheduling, quality checks, and warehouse design. Use this skill when the user needs to move data between systems, build a data warehouse, automate data processing, or improve data reliability — even if they say 'move data from X to Y', 'build an ETL pipeline', 'our data is a mess', or 'set up a data warehouse'.