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概要と使いどころ
Expert patterns for Godot 3D physics (Jolt/PhysX), including Ragdolls, PhysicalBones, Joint3D constraints, RayCasting optimizations, and collision layers. Use for rigid body simulations, character physics, or complex interactions. Trigger keywords: RigidBody3D, PhysicalBone3D, Jolt, Ragdoll, Skeleton3D, Joint3D, PinJoint3D, HingeJoint3D, Generic6DOFJoint3D, RayCast3D, PhysicsDirectSpaceState3D.
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Expert blueprint for educational games including gamification loops (learn/apply/feedback/adapt), progress tracking (student profiles, mastery %), adaptive difficulty (target 70% success rate), spaced repetition, curriculum trees (prerequisite system), and visual feedback (confetti, XP bars). Use for learning apps, training simulations, or edutainment. Trigger keywords: educational_game, gamification, adaptive_difficulty, spaced_repetition, student_profile, curriculum_tree, mastery_tracking.
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Anime/illustration text-to-image (ANIMA 1.0, ~2B Cosmos DiT). Use for anime, manga, illustrated characters; accepts Danbooru tags + natural language; runs/trains on <6GB VRAM; includes anime inpainting via Anima-LLLite ControlNet
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Automated coordination, formatting, and learning from Claude Code operations using intelligent hooks with MCP integration. Includes pre/post task hooks, session management, Git integration, memory coordination, and neural pattern training for enhanced development workflows.
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Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
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Write architecture fitness functions — deterministic tests that enforce a project's hard rules (module boundaries, offline guarantees, content contracts) from inside its own test target. Use when a constraint lives only in prose (CLAUDE.md, code review) and should become un-arguable.
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Enforce complexity and size limits on Swift code with SwiftLint threshold rules — cyclomatic complexity, function/type/file length, parameter count — adopted via a violations baseline so existing code never blocks. Use when agent-written code needs deterministic size constraints.
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Use when the user wants to optimize configurable system parameters against a measurable scalar objective, especially for model training, inference, quantitative strategies, reinforcement learning, scientific workflows, or other expensive black-box evaluations where reading the project can improve trial selection.
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This package provides a workflow for the use of EaSIeR tool, developed to assess patients' likelihood to respond to ICB therapies providing just the patients' RNA-seq data as input. We integrate RNA-seq data with different types of prior knowledge to extract quantitative descriptors of the tumor microenvironment from several points of view, including composition of the immune repertoire, and activity of intra- and extra-cellular communications. Then, we use multi-task machine learning trained in
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This package provides a client for the Bioconductor ExperimentHub web resource. ExperimentHub provides a central location where curated data from experiments, publications or training courses can be accessed. Each resource has associated me
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Draft STRATEGY.md from a goal - read the operator's brief (goal, repo, links) plus the repo README and memory, then write a tight north-star/priorities/audience/constraints strategy.
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Validate and constrain untrusted input at the boundary. Use on any handler that accepts external data.
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Don't over-equip an agent with tools/MCP servers. More tools = more ways to fail and a higher cognitive load. Use when wiring tools or an agent underperforms.
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Evidence-first development — surround the implementation with an executable spec and a gauntlet of constraints (tests, types, coverage, mutation) so line-by-line review becomes optional. Use when the user explicitly asks for high-assurance or evidence-first work ("reliable", "TDD", "prove it works", "I won't read the code"), or when the change touches high-stakes domains (money, auth, data loss, concurrency, public API). For routine changes where the user just wants normal tests, write good tests directly instead of invoking this loop.
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Flip core assumptions to reveal hidden constraints and alternative approaches - "what if the opposite were true?"
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The consumer panel. Spins up a swarm of buyer sub-agents (default 100, --quick for 20) trained on the user's target customer, each with a different income, age, buying behaviour and objection, shows every one the same pitch, and runs the business through 100 buyer scenarios to see who buys, who doesn't, and why. Tallies the answers into buy rates by segment, the reasons people pass in their own words, what would flip a no, and price answers for founder-pricing. Use when the user says "would people buy this", "test it on customers", "run the consumer panel", "simulate buyers", "who is my customer", or "validate demand".
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Deutsche Bahn train schedules, journey planning, and departures across Germany and into neighboring countries (Austria, Switzerland, Netherlands, France, Belgium). Use for ICE/IC/regional rail planning and airport ground transport (FRA, MUC).
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Search trains, buses, and ferries in Norway (Entur), Sweden (ResRobot), and Denmark (Rejseplanen). Intra-Scandinavia ground transport with schedules and Danish fare pricing.
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Transform ordinary snapshots—including daily moments, portraits, objects, pets, food, interiors, streets, architecture, landscapes, travel, and humanistic scenes—into the restrained Shijing Paper Zine visual style. Use when the user wants any real photo turned into a spacious paper-editorial composition with an irregular photographic fragment, simplified source-derived drawing, one structural print color, tactile torn-paper texture, minimal micro-copy, a consistent multi-image series, or a source-to-poster comparison.
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Use when hitting layout errors (RenderFlex overflow, unbounded constraints, RenderBox not laid out), scroll errors, or setState-during-build errors.
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Agent safety boundaries - input validation, output filtering, scope constraints, and document creation policy
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Use when a leader assigns work to a marketing team member or runs a cross-functional meeting — a task brief with concrete deliverables, a deadline with time and timezone, a definition of done, constraints, and check-in points, plus an async-first meeting agenda and action-item template. Trigger on 'assign a task', 'brief for my team', 'team brief', 'definition of done', 'meeting agenda template', 'my team keeps delivering the wrong thing'. Also use when the user says nobody knows who owns what. Not for — the campaign brief the whole team executes from, see `02-campaign-brief-global`; briefing an external agency or freelancer, see `67-agency-vendor-brief-global`; evaluating performance, see `65-team-performance-review-global`.
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Phase 2 of the prd-taskmaster pipeline: spec generation and task parsing. Loads a template (comprehensive|minimal), fills it with DISCOVER-phase constraints and answers, validates the spec (placeholders_found, grade thresholds), parses the PRD into tasks via task-master, runs TaskMaster's native complexity analysis, and expands every task into verifiable subtasks. Autonomous-safe. Declares GENERATE complete so HANDOFF can follow.
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Phase 1 of the prd-taskmaster pipeline: brainstorm-driven discovery. Delegates to superpowers:brainstorming in Interactive Mode (one adaptive question at a time), or self-brainstorms in Autonomous Mode when no user is present. Intercepts before the brainstorming chain hands off to writing-plans — this skill owns the exit. Extracts constraints, calibrates scale (Solo / Team / Enterprise), and advances the pipeline to GENERATE.
日本語の概要は準備中です。原文の説明を表示しています。