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Found 1,269 Skills
Orchestrate a configurable, multi-member CLI planning council (Codex, Claude Code, Gemini, OpenCode, or custom) to produce independent implementation plans, anonymize and randomize them, then judge and merge into one final plan. Use when you need a robust, bias-resistant planning workflow, structured JSON outputs, retries, and failure handling across multiple CLI agents.
Only to be triggered by explicit /parallel-task-spark commands.
Only to be triggered by explicit super-swarm-spark commands.
Automate application deployment to cloud platforms and servers. Use when setting up CI/CD pipelines, deploying to Docker/Kubernetes, or configuring cloud infrastructure. Handles GitHub Actions, Docker, Kubernetes, AWS, Vercel, and deployment best practices.
Orchestrates BMAD workflows for structured AI-driven development. Routes work across Analysis, Planning, Solutioning, and Implementation phases.
JEO — 통합 AI 에이전트 오케스트레이션 스킬. ralph+plannotator로 계획 수립, team/bmad로 실행, agent-browser로 브라우저 동작 검증, 작업 완료 후 worktree 자동 정리. Claude, Codex, Gemini CLI, OpenCode 모두 지원. 설치: ralph, omc, omx, ohmg, bmad, plannotator, agent-browser.
Ultimate multi-agent framework for Google Antigravity. Orchestrates specialized domain agents (PM, Frontend, Backend, Mobile, QA, Debug) via Serena Memory.
README-first AI repo reproduction orchestrator. Use when the user wants an end-to-end minimal trustworthy reproduction flow that reads the repo, selects the smallest documented inference or evaluation target, coordinates the intake, setup, execution, and optional paper-gap sub-skills, enforces conservative patch rules, and writes the standardized `repro_outputs/` bundle. Do not use for paper summary, generic environment setup, isolated repo scanning, standalone command execution, or broad research assistance outside repository-grounded reproduction.
Run a model-diverse subagent council to investigate the same problem from multiple perspectives, compare findings, and produce a final recommendation. Use this skill whenever the user asks for a council, second opinions, multiple agents/models to evaluate one question, parallel investigation, red-team/blue-team comparison, or help deciding between competing technical approaches.
Run an autonomous, spec-driven development "saga" for medium-to-large features using an orchestrator agent and a fleet of worker subagents. Use this skill whenever the user invokes /saga, asks to autonomously build a sizable feature end-to-end with minimal human intervention, wants a comprehensive spec broken into milestones and tasks with airtight validation criteria before parallelized implementation, or wants an orchestrator to delegate implementation to worker agents while preserving its own context window. Trigger on phrases like "run a saga", "autonomously implement this feature", "spec it out then build it with subagents", "orchestrate this big feature end-to-end", or "build this with workers and validate each step". Also use this skill when asked to continue, resume, or pick up an existing saga from its saga directory (e.g. under ~/.sagas).
When the user wants to set up a recurring, self-running marketing workflow — a repeatable loop an AI agent runs on a cadence (weekly, daily, on a trigger) rather than a one-off task. Also use when the user mentions 'marketing loop,' 'recurring marketing workflow,' 'automate my marketing,' 'marketing on autopilot,' 'weekly marketing review,' 'ad fatigue check,' 'content refresh loop,' 'churn watch,' 'ranking drop alert,' 'always-on marketing,' 'marketing automation workflow,' or 'run this every week.' Use this to pick, adapt, and schedule an ongoing marketing loop that orchestrates the other marketing skills. For one-off marketing ideas, see marketing-ideas. For the experimentation loop specifically, see ab-testing.
Delegate noisy investigation to one or more subagents so the orchestrator's context stays clean, then work from the distilled answer. Use this skill whenever answering a question would require reading many files, long logs, large diffs, or wide codebase surveys — i.e. when producing the answer generates far more noise than the answer itself. Use it for "how does X work", "where is Y used", "what's the root cause of Z", "summarize this PR/log" style questions, and reach for it liberally before reading a pile of files inline.