Analyze logs and source from a completed repository example run. Never execute or control examples.
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概要と使いどころ
Analyze logs and source from a completed repository example run. Never execute or control examples.
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。
Expo's official example projects - the expo/examples repo of ~70 `with-*` integrations (Stripe, Clerk, Supabase, OpenAI, maps, Reanimated, SQLite, Skia, NativeWind, and more). Use when integrating a third-party library or service into an existing Expo app and you want the canonical, version-matched pattern to adapt, or when scaffolding a new project from one with `npx create-expo --example`.
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。
Cultural adaptation for translated content. Run AFTER blog-translate completes. Adjusts brand examples, CTAs, legal references, and formality for the target market (German, French, Japanese, Spanish, etc.). Deep cultural adaptation of translated blog posts. Goes beyond translation to swap brand examples, adapt CTAs, substitute legal references, localize statistic sources where possible, and adjust formality (Sie/du, tu/vous, formal/informal). Built-in profiles for DACH, Francophone, Hispanic, and Japanese markets, plus a custom-locale template. Makes content feel locally authored, not translated. Use when user says "localize blog", "blog localize", "cultural adaptation", "adapt for Germany", "adapt for France", "lokalisieren", "localiser", "adaptar".
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。
Teaches how to reason from a transaction protocol’s rules to a working model. Use when analyzing a concurrency-control paper, spec, or algorithm to identify state, metadata, invariants, operation rules, examples, counterexamples, guarantees, false aborts, unsafe commits, or tradeoffs before inspecting concrete traces.
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。
Build the context an AI needs to do a task well — the background, constraints, examples, and format it can't guess — so you get a great result on the first try instead of a generic one you have to keep correcting. Use when asked why does AI give me generic answers, how do I give AI better context, my AI results are mediocre, or how do I get it right the first time. Produces the specific context this task needs (who/what/constraints/examples/format), a reusable primer you can paste ahead of the request, the difference between a starved prompt and a well-briefed one, and what to leave out — turning vague back-and-forth into a strong first result.
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 worked example fading sequence from fully worked examples through to independent practice. Use when teaching procedures, algorithms, or multi-step processes to novice learners.
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 self-explanation prompts that deepen understanding of worked examples, texts, or diagrams. Use when students read material passively without engaging with underlying 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。
Provide Phoenix, LiveView, Ecto, OTP, or Oban examples. Use when asked for sample code, a walkthrough, a proper…
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。
Provide Phoenix, LiveView, Ecto, OTP, or Oban examples. Use when asked for sample code, a walkthrough, a proper implementation, or expected workflow output. Pair with domain skills. NOT for debugging, direct changes, best-practice advice, or audits.
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。
Provide Phoenix, LiveView, Ecto, OTP, or Oban examples. Use when asked for sample code, a walkthrough, a proper implementation, or expected workflow output. Pair with domain skills. NOT for debugging, direct changes, best-practice advice, or audits.
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。
The compile-readiness gate for prompt auto-optimization. Decide whether you have earned the right to run an optimizer (DSPy MIPROv2 / GEPA / BootstrapFewShot) before spending compute. Two preconditions only — a real metric, and enough examples for the optimizer you picked. Garbage metric in, garbage prompt out. Pick the optimizer by data scale; GEPA inverts the scale assumption (~10 examples + textual feedback).
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。
Research documentation, find examples, explain APIs and libraries - Use when you need to look up official documentation, find code examples, understand library APIs, or research best practices.
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。
Build and maintain Core UI with the ReUI registry and authenticated ReUI MCP. Use for new pages/sections/features, ReUI components/examples/Pro blocks, @reui installs, REUI_LICENSE_KEY setup, and ReUI API or documentation research. Preserve exact base-maia/Base UI, packages/ui ownership, existing shared tables, and Core tokens; use live APIs and worked examples.
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 concrete counterexamples when formal verification, assertions, or specifications fail. Use this skill when debugging failed proofs, understanding why verification fails, creating minimal reproducing examples, analyzing assertion violations, investigating invariant breaks, or diagnosing specification mismatches. Produces concrete input values, execution traces, and state information that demonstrate the 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。
Debug proof failures using counterexamples from Nitpick (Isabelle) or QuickChick (Coq) to identify specification errors, missing preconditions, and proof strategy issues. Use when: (1) A proof attempt fails and you need to understand why, (2) Counterexamples are generated by Nitpick or QuickChick, (3) Specifications may be incorrect or incomplete, (4) Theorems need validation before proving, (5) Missing preconditions or lemmas need identification, or (6) Proof failures need explanation and correction suggestions. Supports both Isabelle/HOL and Coq equally.
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。
TestDriver examples skill
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。
Internal — for Boundless team members only. Generate release notes for a new Boundless version. Fetches PR details, explores code changes, and produces prover-facing documentation with config examples, migration guides, and worked examples. Use when the user wants to write release notes, changelog entries, or upgrade guides.
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。
Cultural adaptation for translated content. Run AFTER blog-translate completes. Adjusts brand examples, CTAs, legal references, and formality for the target market (German, French, Japanese, Spanish, etc.). Deep cultural adaptation of translated blog posts. Goes beyond translation to swap brand examples, adapt CTAs, substitute legal references, localize statistic sources where possible, and adjust formality (Sie/du, tu/vous, formal/informal). Built-in profiles for DACH, Francophone, Hispanic, and Japanese markets, plus a custom-locale template. Makes content feel locally authored, not translated. Use when user says "localize blog", "blog localize", "cultural adaptation", "adapt for Germany", "adapt for France", "lokalisieren", "localiser", "adaptar".
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 worked example fading sequence from fully worked examples through to independent practice. Use when teaching procedures, algorithms, or multi-step processes to novice learners.
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 self-explanation prompts that deepen understanding of worked examples, texts, or diagrams. Use when students read material passively without engaging with underlying 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。
Hook recipes and working examples — plugin hooks, frontmatter hooks in skills/agents/commands, prompt-based LLM hooks, and complete code examples in Python and Node.js. Use when building hook scripts, integrating hooks into plugins, implementing prompt-based hooks, or looking for hook configuration 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。
This skill should be used when the user asks to "add an example", "write a tutorial", "document a new workflow example", or when creating a new worked example under `docs/source/examples/`.
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。
High-precision semantic search and content retrieval via Exa API. Use when: (1) Deep research requiring semantic understanding, (2) Code documentation and examples lookup, (3) Company/professional research, (4) AI-powered comprehensive research tasks, (5) URL content extraction with structured output. Triggers: "research", "find papers", "code examples", "company info", "LinkedIn profiles", "deep analysis". Differentiator: Exa excels at semantic/neural search while grok-search is better for real-time news and general web content.
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 this skill when working with Twilio communication APIs for SMS/MMS messaging, voice calls, phone number management, TwiML, webhook integration, two-way SMS conversations, bulk sending, or production deployment of telephony features. Includes official Twilio patterns, production code examples from Twilio-Aldea (provider-agnostic webhooks, signature validation, TwiML responses), and comprehensive TypeScript examples.
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。