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概要と使いどころ
Design a variation theory task using contrast, separation, and fusion to teach critical features of a concept. Use when students confuse similar concepts or fail to discern key distinctions.
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。
Design a Montessori three-period lesson for introducing concepts through concrete materials and naming. Use when teaching vocabulary, classifications, or concepts through hands-on materials.
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。
Develop pedagogical content knowledge for a topic by surfacing common misconceptions and effective representations. Use when preparing to teach unfamiliar content or improving teaching of hard topics.
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。
Generate a Socratic questioning sequence that develops conceptual understanding through guided inquiry. Use when facilitating philosophical discussions, concept exploration, or critical examination.
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。
Design an error analysis protocol to diagnose the root cause of student mistakes and misconceptions. Use when error patterns appear in student work and targeted feedback is needed.
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。
Verify mini-program implementations and fixes with risk-calibrated evidence across static checks, unit tests, integration tests, state matrices, simulators, real devices, cloud environments, and release artifacts. Use when users ask to test, validate, accept, regression-check, quality-check, confirm readiness, or determine whether a mini-program feature is actually complete. Binds results to a source and build fingerprint, records commands and observable evidence, separates passed, failed, blocked, and not-run layers, prioritizes the next highest-information check, and never converts local success into device, cloud, release, or formal acceptance claims.
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。
Convert a vague mini-program idea, feature request, or partially documented product into an evidence-calibrated product specification with target users, core problem, version-one scope, main and exception flows, page responsibilities, state matrices, and testable acceptance criteria. Use when users ask to define an MVP, clarify requirements, organize product flows, specify page behavior, resolve ambiguous product states, or prepare a stable handoff before architecture or implementation. Separates facts, user decisions, assumptions, unknowns, and future ideas; never invents product logic merely to complete a screen or technical plan.
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。
Use when you need a bite-sized, TDD-driven implementation plan but do NOT have a brainstorm-beagle spec to plan against. quick-plan reconstructs intent from the current conversation, fans out domain-expert exploration subagents across the codebase, and synthesizes the same plan format write-plan produces — without requiring `.beagle/concepts/<slug>/spec.md`. Triggers on: "quick plan", "plan this out", "plan what we just discussed", "turn this into an implementation plan", "plan this without a spec", "I don't have a spec, just plan it", "write-plan but no spec". Make sure to use this skill whenever the user wants an implementation or TDD plan and there is no spec to plan against — even if they just say "plan it" after discussing a feature. Writes to `.beagle/plans/<slug>/plan.md`. If a finalized spec already exists at `.beagle/concepts/<slug>/spec.md`, prefer write-plan. Does NOT brainstorm specs, write code, or execute the plan — produces the plan document (and an optional handoff prompt) only.
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。
Use when you have a finalized brainstorm-beagle spec at `.beagle/concepts/<slug>/spec.md` and need a bite-sized, TDD-driven implementation plan before any code is written. Triggers on: "write a plan", "plan this spec", "turn the spec into a plan", "now plan the implementation", "write-plan". Reads the spec, designs the file structure, decomposes work into 2-5 minute TDD steps with exact paths and commands, self-reviews against the spec, gets user approval, then writes to `.beagle/concepts/<slug>/plan.md` and offers to generate an execution handoff prompt via the subagent-prompt skill. Does NOT brainstorm specs, write code, or execute the plan — produces the plan document (and an optional handoff prompt) only.
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。
Explanation documentation patterns for understanding-oriented content - conceptual guides that explain why things work the way they do. Use when writing an explanation doc, conceptual guide, understanding/background doc, design rationale, or architecture explanation, or when asked how/why something works. Builds on the docs-style core writing principles.
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。
Act as a product-manager copilot that turns evidence into product decisions and artifacts: backlogs and prioritization, PRDs and user stories, metrics and OKRs, experiments, launch plans, feedback triage, and pricing/packaging, kept in a per-product workspace. Use whenever the user wants to decide what to build, spec it, or ship it: "write a PRD for…", "prioritize our backlog", "what should we build next", "plan OKRs", "define our north star metric", "design an A/B test", "plan the launch", "triage this customer feedback", "platform or feature?", in any language ("viết PRD", "ưu tiên tính năng nào", "quản lý sản phẩm", "プロダクト管理", "产品经理"), even when they never say "product manager". Also use when the user returns to a product workspace this skill created (a directory holding `product.md`, `state.md`, and `decisions.md`). Not for deep user discovery (design-thinking), desk market facts (market-researcher), or company-level bets (strategy-board).
Facilitate a full design-thinking engagement (Empathize, Define, Ideate, Prototype, Test) grounded in real user evidence. Use whenever the user wants to understand users deeply and design a solution for them: "run design thinking", "understand our users", "design user interviews", "synthesize these interview notes", "build personas", "write How-Might-We questions", "prototype this concept", "design a usability test", "our users are churning and we don't know why", in any language ("tư duy thiết kế", "nghiên cứu người dùng", "phỏng vấn khách hàng", "デザイン思考", "设计思维"), even when they never say "design thinking". Also use when the user drops raw interview notes, transcripts, or survey exports and wants insights, personas, or product decisions. Not for "is there a market for X" desk questions (market-researcher).
Use when asked to create a logo, design a brand mark, generate a logo concept, or produce any logo asset. Examples: "create a logo for X", "design a brand mark", "make me a logo", "generate logo concepts", "logo for our product".
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。
Hacker News post crafter — given a product, feature, or story produces a ready-to-post HN submission (title ≤80 chars, no marketing), honest body text with technical depth, no outbound links, predicted reception analysis, and comment-response templates for likely pushback. Use when asked to "write an HN post", "craft a Show HN", "prepare our Hacker News launch", or "help me post on HN".
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。
Create reviewable modular plant concept layouts from land shape, unit requirements and exclusions. Use for plant design, layout improvement, プラント設計, ユニット配置 and 設計JSON.
Vocabulary learning strategies and retention science for any language -- frequency-based word selection, spaced repetition systems (Ebbinghaus forgetting curve, Leitner system, SM-2 algorithm), cognate exploitation, word family networks, context-based learning, collocations, depth of word knowledge (form, meaning, use), reading progression from controlled to authentic texts, and productive vs. receptive vocabulary thresholds. Use when building vocabulary plans, optimizing review schedules, selecting learning materials, or diagnosing vocabulary gaps.
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。
MLOps and the production ML lifecycle -- model packaging and serving, CI/CD for ML, experiment tracking, model registries, reproducibility, production monitoring for data and concept drift, retraining pipelines, A/B and shadow deployment, and rollback. Covers batch vs online/real-time inference, REST endpoints, feature stores, data and version pinning, deterministic pipelines, performance-decay detection, and retraining triggers. Use when deploying models to production, serving predictions, monitoring for data or concept drift, setting up ML CI/CD, tracking experiments, managing a model registry, or planning retraining, shadow rollout, and rollback.
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。
Core programming concepts from variables through recursion. Covers data types (integers, floats, strings, booleans, arrays, objects), variables and scope (lexical, dynamic, block, function, global), control flow (conditionals, loops, pattern matching), functions (parameters, return values, closures, higher-order functions), recursion (base cases, call stack, tail recursion, mutual recursion), type systems (static vs dynamic, strong vs weak, type inference, generics), and error handling (exceptions, Result types, defensive programming). Use when teaching, reviewing, or diagnosing issues with fundamental programming constructs.
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。
Intercept and debug Claude Code API requests using mitmproxy. Inspect system prompts, tool definitions, token usage, and streaming responses. Triggers: cc-trace, mitmproxy, API interception, Claude Code debugging, traffic inspection.
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。
CatColab Ologs (Ontology Logs) - category-theoretic knowledge representation where objects are concepts and morphisms are functional relationships. Foundation for database schemas and conceptual modeling.
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。
This skill should be used when agents generate placeholder tokens like "pseudo-", "mock-", "temporary", "TODO", "demo-", or similar incompleteness markers. Detects substitution patterns in agent OUTPUT and triggers mandatory user interview instead of accepting incomplete work. Activates automatically on any output containing forbidden tokens.
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。
Execute structured acceptance checks and record their observed results for later comparison. Use to capture a pre-change baseline or a post-change verification state without treating expected nonzero baseline results as execution failure.
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。
This skill should be used when the user asks to "add docstrings", "write error handling", "use naming conventions", "handle exceptions", or when writing new Python code for StreamFlow that requires docstring format, naming conventions, exception handling, or async cleanup patterns.
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。
Skeptical analysis and critique of claims, plans, and code without taking anyone at face value. Use when the user says "don't trust me", wants adversarial review, red-teaming, assumption checking, or verification that generated or proposed code is actually correct. Triggers on skepticism, critique everything, verify claims, challenge assumptions, red team, devil's advocate, prove it, smell test.
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。