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Found 13,592 Skills
楽勝で流す。Agent Teamsで完全自走、寝てる間にゴール。Use when user mentions '/breezing', agent teams, team execution, full auto completion, multi-agent workflow, 'チームで完走', 'チームで全部'. Do NOT load for: single tasks, reviews, setup, or /work (direct implementation).
Превращает идею в конкретную задачу, которую агент может выполнить. Проводит через 5 шагов: идея → результат → реальность → задача → план.
Evaluates and optimizes agent skills using a DSPy-powered GEPA (Generate/Evaluate/Propose/Apply) loop. Loads scenario YAML files as DSPy datasets, scores outputs with pattern-matching metrics, and optimizes prompts via BootstrapFewShot or MIPROv2 teleprompters. Also generates new scenario YAML files from skill descriptions.
A micro-prompt that reminds the agent that it is an interactive programmer. Works great in Clojure when Copilot has access to the REPL (probably via Backseat Driver). Will work with any system that has a live REPL that the agent can use. Adapt the prompt with any specific reminders in your workflow and/or workspace.
SIWA (Sign-In With Agent) authentication for ERC-8004 registered agents.
Issue quality primitives: lint, enrich, decompose. `/issue lint [#N|--all]` — Score issues against org-standards. `/issue enrich [#N]` — Fill gaps with sub-agent research. `/issue decompose [#N]` — Split oversized issues into atomic sub-issues.
Progressive context refinement pattern for subagents. Solves the problem of agents not knowing what context they need until they start working. Uses a 4-phase loop: DISPATCH, EVALUATE, REFINE, LOOP.
Reviews and grades an agent skill directory (SKILL.md plus supporting resources) for specification compliance, clarity, token efficiency, safety, robustness, and portability. Use when a user wants a rubric-based critique with a weighted score/grade and concrete, minimal patch suggestions.
Iteratively reviews and fixes Claude Code skill quality issues until they meet standards. Runs automated fix-review cycles using the skill-reviewer agent. Use to fix skill quality issues, improve skill descriptions, run automated skill review loops, or iteratively refine a skill. Triggers on 'fix my skill', 'improve skill quality', 'skill improvement loop'. NOT for one-time reviews—use /skill-reviewer directly.
Inline adversarial plan review — 3 sequential checks (Feasibility, Completeness, Scope & Alignment) performed by the calling LLM in its own context. No subagents spawned. Call after saving a plan. Returns GATE_PASS or GATE_FAIL with blocking issues.
Exhaustive, source-accurate guide for building iMessage applications using @photon-ai/imessage-kit (Basic) and @photon-ai/advanced-imessage-kit (Advanced). Covers every method, type, option, event, and pattern for iMessage automation, AI agents, and chat bots.
Use this skill when writing code that calls the Gemini API for text generation, multi-turn chat, multimodal understanding, image generation, streaming responses, background research tasks, function calling, structured output, or migrating from the old generateContent API. This skill covers the Interactions API, the recommended way to use Gemini models and agents in Python and TypeScript.