2.1K skills · sorted by relevance
Apply AlphaGo-style patterns: learned priors plus search, mixed evaluators, self-play, and asynchronous fast/slow loops. Use for intractable search and compute allocation. NOT for basic supervised learning, tractable optimization, or domains without search bottlenecks.
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Fundamental patterns for effective agentic behavior. Teaches decomposition, tool orchestration, error recovery, context management, quality self-assessment, and knowing when to stop. Model-agnostic principles that make any agent more effective regardless of domain. Activate on: "how should I structure this agent", "agentic workflow", "agent patterns", "multi-step task", "tool orchestration", "/agentic-patterns", "decompose this", "agent best practices", "chain of actions", "when should the agent stop", "agent loop design". NOT for: creating agent infrastructure (use agent-creator), building DAGs (use jury_rig-architect), specific tool implementation.
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Expert in 3D computer vision labeling tools, workflows, and AI-assisted annotation for LiDAR, point clouds, and sensor fusion. Covers SAM4D/Point-SAM, human-in-the-loop architectures, and vertical-specific training strategies. Activate on '3D labeling', 'point cloud annotation', 'LiDAR labeling', 'SAM 3D', 'SAM4D', 'sensor fusion annotation', '3D bounding box', 'semantic segmentation point cloud'. NOT for 2D image labeling (use clip-aware-embeddings), general ML training (use ml-engineer), video annotation without 3D (use computer-vision-pipeline), or VLM prompt engineering (use prompt-engineer).
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Apply cognitive task analysis to expert work that depends on perceptual cues, branching judgment, and recurring monitoring loops. Use when decomposing expert capability into agent structure, simulation design, or validation interviews. NOT for ordinary step-by-step SOP capture or simple pipelines with no tacit cue layer.
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Design low-friction UX, interaction patterns, and comeback loops for AI coding assistant apps, agent consoles, background coding agents, and Port Daddy swarm/operator surfaces. Use when creating flows like prompt-to-diff, context attach, plan/apply/review, checkpoint rollback, background-agent dashboards, visual proof, or multi-agent invocation. NOT for generic landing pages, static dashboards, or implementation-only changes without product flow.
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FLOW framework integration: evidence-led SEO using the Find → Leverage → Optimize → Win loop. Surfaces stage-specific AI prompts from the FLOW knowledge base (41 prompts, CC BY 4.0). Use when user says "FLOW", "FLOW framework", "seo flow", "evidence-led SEO", "find leverage optimize win", or wants stage-specific SEO prompts.
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The one skill for taking a page to done: its agent surface and its UI/UX, in one pass. Use when assigned pages or routes to pass, on `/page-pass <route>`, or when a campaign brief says 'do the page pass'. NOT for certification ledger writes (use surface-certification-loop).
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Continuous localhost UI iteration loop with Arman, through full surface certification. Use when he asks to fix, refine, review, or iterate on a page, route, panel, overlay, or other UI while he may inspect localhost. NOT for a report-only UI audit.
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Redux Toolkit selector rules against re-renders, recalculation warnings, and render loops. Use when writing useSelector/useAppSelector or createSelector, deriving state in a connected component, or seeing 'Selector returned a different result when called with the same parameters'.
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Doctrine for all Supabase realtime: postgres_changes, broadcast, presence. Use before writing a `.channel(`/useChannel subscription, echo suppression, reconnect logic, an autosave loop beside realtime, or replacing polling with live updates, or when the tab freezes, messages duplicate, or a subscription delivers nothing.
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One-time recovery when a shared checkout has drifted far from GitHub main: hundreds ahead/behind, stacked worktrees and PRs, a dirty tree nobody owns. Use when tempted to reset or force-push. NOT for the ordinary 30-minute release loop (operations/INTEGRATION_MAINTAINER.md).
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The watch-fix-rerun method for making an existing platform agent or automated process efficient and correct: baseline its ledger, run one unit yourself, fix the class behind every wasted call, rerun, record. Use when asked to improve, optimize, watch, or 'make efficient' an agent, a sandbox session, a sync job, or any recurring automated process.
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指定フロントエンド範囲のcomponent・state・render・loadingを監査し、挙動を保ってリファクタリングする。read-only監査にも対応する。
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責務・依存方向・状態や副作用の境界を監査し、既存挙動を保って段階的にリファクタリングする。read-only監査にも対応する。
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Claude Codeセッションのための包括的な検証システム。
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自律型Claude Codeループのパターンとアーキテクチャ — シンプルなシーケンシャルパイプラインからRFC駆動のマルチエージェントDAGシステムまで
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品質ゲート、評価、リカバリー制御を備えた継続的自律エージェントループのパターン。
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This skill should be used when the user wants to "synthesize two competing solutions without averaging", "resolve a design/value disagreement by lifting to a higher frame", "止揚 / アウフヘーベン", "矛盾を止揚", "独立案を統合", "平均化せず統合", "contradiction lift", "sublate competing designs", or has two good-but-divergent answers to the same question and wants a higher-order resolution (a selection mechanism) or an honest aporia — not a compromise. NOTE: Use this when two independent solutions diverge and you want to PRESERVE both truth-moments while lifting to a higher frame. Use devils-advocate to stress-test ONE proposal with an assigned opponent, strong-inference to find an unknown cause, and dialectic-loop to validate a claim against a real corpus.
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Runs the test-first build loop (spec, plan, thin slices, failing test first, verify, commit) for a Godot 4 game in GDScript or C# first; in a TypeScript, Python or other repo when test-first, slices or a reproduce-first fix is asked for. Trigger to build or add a game feature, mechanic, stat, system or milestone, even when told to test later; to turn a spec or grill record into slices; to fix a game bug from a reproduction, one hypothesis at a time; on "write the test first", "what's the next slice", "is this slice done" or "is this milestone ready to exit"; or say slicesmith (spec, plan, slice, fix, exit). Run proof and gate rulings are godotsmith's; diff review a review tool's; repo maps researchscribe's; art pixelsmith's; audio soundsmith's; interviews grillwright's; fan-out dispatchwright's.
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Hypothesis-Test Loops via REPL for Exploratory Abductive Inference with Gay.jl colors
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[32] ANALYZE. Ensure every critical claim has verifiable evidence with confidence levels. Each fact must have source + confidence percentage. If confidence <85%, enter Loop150 to find more sources. Use for critical decisions, factual claims, legal/compliance work, or any situation where unverified claims are dangerous.
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Expert 3D modeling specialist with deep knowledge of topology, UV mapping, game-ready and film-ready pipelines, DCC tool workflows (Blender, Maya, ZBrush, 3ds Max, Houdini), retopology, LOD systems, and export pipelines. This skill represents years of production experience distilled into actionable guidance. Use when "3d model, 3d modeling, mesh topology, uv unwrap, uv mapping, retopology, retopo, low poly, high poly, subdivision, subdiv, edge flow, edge loops, polygon modeling, box modeling, hard surface, organic modeling, sculpting, zbrush, blender modeling, maya modeling, 3ds max, LOD, level of detail, game ready mesh, film ready, baking normals, high to low, fbx export, gltf export, texel density, 3d, modeling, topology, uv, game-dev, vfx, blender, maya, zbrush, retopology, lod, hard-surface, organic, sculpting" mentioned.
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Coordinator workflow for orchestrating dockeragents through fix-review-iterate-present loop. Use when delegating any task that produces code changes. Ensures agents achieve 10/10 quality before presenting to human.
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Expert in 3D computer vision labeling tools, workflows, and AI-assisted annotation for LiDAR, point clouds, and sensor fusion. Covers SAM4D/Point-SAM, human-in-the-loop architectures, and vertical-specific training strategies. Activate on '3D labeling', 'point cloud annotation', 'LiDAR labeling', 'SAM 3D', 'SAM4D', 'sensor fusion annotation', '3D bounding box', 'semantic segmentation point cloud'. NOT for 2D image labeling (use clip-aware-embeddings), general ML training (use ml-engineer), video annotation without 3D (use computer-vision-pipeline), or VLM prompt engineering (use prompt-engineer).
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