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概要と使いどころ
Inspect live Python class instances in a running process — find all instances of a class, examine their attributes/state, or invoke methods on them. Also supports forcing GC. Use this when you need to probe object-level runtime state beyond what watch/getglobal can provide.
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Read GitHub Actions CI for the current branch or open PR, triage failing checks, reproduce locally, and fix scoped issues until green. Use whenever the user mentions CI failures, red checks, failed pipeline, GitHub Actions, merge blocked, fix the build, babysit CI, gh run failed, ruff/ty errors, failing pytest, example run failed, or asks to check CI on this branch — even if they only paste a log snippet. Also use after implementing a feature when tests, lint, or typecheck might have broken.
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Meta-skill for creating new stitch-kit skills. Enforces naming conventions, SKILL.md structure, examples format, and the Design-First SOP. Use when adding new framework support, a new domain-specific prompt architect, or any new capability to the stitch-kit plugin.
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fast-check patterns for JS/TS — 6 canonical property categories with worked examples targeting agent-studio utilities (path normalization, safe-json, glob-to-regex, routing logic)
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Generates comprehensive documentation from code, APIs, and specifications. Creates API documentation, developer guides, architecture docs, and user manuals with examples and tutorials.
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 near the end of EZ_math_model intake when external/user-corpus contains user-provided papers, notes, PDFs, datasets, or examples that should become a local AGENTS.md reference index.
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Initialise a project for knowledge base use. Appends the [Knowledge Base] section to the project's CLAUDE.md, creates the library/ directory structure, optionally seeds with three example library files from the Agentic SDLC research, and reports next steps. Run once after installing sdlc-knowledge-base.
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Trains the HUMAN user's critical thinking through four modes: drill (argument-analysis items with a single defensible answer, judge stance), scene (Socratic exploration of synthetic scenes or user-supplied material, no verdicts on interpretations; includes a configure track — design the information request and verification plan for a decision before any analysis), expedition (guided audit of impossible-tier problems from verified packs), and detective (a runtime-generated multi-layer case worked as an escape room, guide-and-judge stance). Use when the user wants to practice critical thinking, analyze arguments, hunt assumptions, examine bias, train reasoning, or decide what information a decision needs first. Triggers: critical thinking practice, train my thinking, drill, scene, byom, configure, information plan, what would I need to know, spot manipulation tactics, scam literacy, 批判思考練習, 話術辨識, detective, 查案, 破案, 偵探.
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。
Used when the user asks to "create slides", "make a presentation", "build a deck", "design slides", "generate a pitch deck", "create a conference talk", "make a keynote", "presentation design", "slide layout", "slide template", "presentation structure", or mentions slides, presentations, decks, or talks in any context. Also triggers when the user wants to plan the content and visual direction of a presentation before generating it. This is the entry-point orchestrator that drives the full slide creation workflow across both HTML and PPTX formats. Examples: "I need to create slides for my conference talk about AI adoption", "can you turn this markdown into a presentation?", "I have a product demo tomorrow and need visual materials", "convert my existing PowerPoint to interactive HTML slides".
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 Telnyx communication APIs for SMS/MMS messaging, voice calls, phone number management, messaging profiles, webhook integration, two-way SMS conversations, bulk sending, message scheduling, or production deployment of telephony features. v2.0 includes official Telnyx documentation, production patterns from Twilio-Aldea (Ed25519 signature validation, provider-agnostic webhooks, idempotency), and TypeScript code 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。
Deploy and manage Netlify projects using Next.js with serverless functions, environment variables, and continuous deployment. Use this skill when working with Netlify deployments, configuring netlify.toml, managing Netlify Functions, debugging webhooks, setting environment variables, or troubleshooting deployment issues for Next.js applications on Netlify. Essential for Twilio-Aldea and similar serverless SMS/telephony projects. v2.0 includes official Netlify documentation, production patterns from Twilio-Aldea (webhook timeout solutions, background functions), and TypeScript code 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。
Research UI component patterns across the component.gallery corpus — 60 canonical component types, ~95 production design systems, 2,600+ examples — plus an AI-era overlay (prompt inputs, tool-call cards, streaming status, citations) and composite patterns (command palette, app shell, agent console). Compare implementations, find alternative component names, ground frontend decisions in real-world precedent. Pairs with minoan-frontend-design for research-then-build workflows. Triggers on component patterns, how do others implement X, design system research, component comparison, AI chat components, agent UI 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。
Use when an output artifact, template, schema, example, or handoff field must stay aligned across commands, skills, docs, and validators.
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。
Apply the fail-early (fail-fast) pattern to detect and report errors at the earliest possible point. Covers input validation with guard clauses, meaningful error messages, assertion functions, and anti-patterns that silently swallow failures. Primary examples in R with general/polyglot guidance. Use when writing functions that accept external input, adding input validation before CRAN submission, refactoring code that silently produces wrong results, reviewing PRs for error-handling quality, or hardening internal APIs against invalid arguments.
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Add an external probabilistic classifier to a decision path without letting it take the path over. Covers finding the externally-graded rows you are already logging, measuring the confidence separation between the overrides it got right and the ones it got wrong, choosing an operating point your data licenses, failing open at the call site, and proving the whole arrangement offline with no API key. Applies to any service returning a score you can order — TypeSafe AI's System One models (Jev) are the worked example. Most of the method transfers to a local model or a second heuristic, with the parts that do not called out where they arise. Use when a rule-based path is wrong often enough to hurt, when someone proposes replacing a heuristic with a model, when a confidence threshold needs a defensible value, or when an oracle already in production has never been graded.
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。
Scaffold a new R package with complete structure including DESCRIPTION, NAMESPACE, testthat, roxygen2, renv, Git, GitHub Actions CI, and development configuration files (.Rprofile, .Renviron.example, CLAUDE.md). Follows usethis conventions and tidyverse style. Use when starting a new R package from scratch, converting loose R scripts into a structured package, or setting up a package skeleton for collaborative development.
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Use when examining ELF binaries with readelf, objdump, nm, or ldd: dependencies, symbols, sections, relocations, build IDs, or hardening, or when enabling RELRO, PIE, stack canaries, FORTIFY_SOURCE, CET, CFI, or seccomp filters, or checking a binary with checksec. Not for modifying binaries: use binutils. Not for runtime sanitizer builds: use sanitizers.
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Use when skill or knowledge activation is unreliable, or setup reaches activation reliability. Not for remote, credential, publish, deploy, or irreversible changes.
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 the user asks for a council, second opinions, or parallel investigation. Not for cross-examination of existing proposals: use cross-critique.
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。
Distill books, documents, folders, or URLs into compact, practical Agent Skills. Use when source material should become either one reusable skill package or a split set of focused skills, each with a concise SKILL.md plus detailed references and examples.
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Solve competition math problems (IMO, Putnam, USAMO, AIME) with adversarial verification that catches the errors self-verification misses. Activates when asked to 'solve this IMO problem', 'prove this olympiad inequality', 'verify this competition proof', 'find a counterexample', 'is this proof correct', or for any problem with 'IMO', 'Putnam', 'USAMO', 'olympiad', or 'competition math' in it. Uses pure reasoning (no tools) — then a fresh-context adversarial verifier attacks the proof using specific failure patterns, not generic 'check logic'. Outputs calibrated confidence — will say 'no confident solution' rather than bluff. If LaTeX is available, produces a clean PDF after verification passes.
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Migrate workloads from Google Cloud Platform to AWS — including AI and agentic workloads regardless of cloud provider. Triggers on: migrate from GCP, GCP to AWS, move off Google Cloud, migrate Terraform to AWS, migrate Cloud SQL to RDS, migrate GKE to EKS, migrate Cloud Run to Fargate, Google Cloud migration, migrate from OpenAI to Bedrock, move off OpenAI, switch from ChatGPT API to AWS, migrate from Gemini to Bedrock, migrate LangChain to Bedrock, migrate LangGraph to AWS, migrate agentic workloads to AWS, move AI workloads to AWS, migrate my AI app to AWS. Runs a 6-phase process: discover GCP resources from Terraform files, app code, or billing exports, clarify migration requirements, design AWS architecture, estimate costs, generate migration artifacts, and collect optional feedback. Clarify must finish before Design, Estimate, or Generate. Includes AI provider migration guidance (for example, OpenAI to Amazon Bedrock) by selecting closest-fit Bedrock model families for required modality, latency/quality targets, context windows, and cost constraints. Model mapping is compatibility-guided, not 1:1 parity; validate prompts, tool-calling behavior, and eval metrics before cutover. Do not use for: Azure or on-premises migrations to AWS, AWS-to-GCP reverse migration, general AWS architecture advice without migration intent, GCP-to-GCP refactoring, or multi-cloud deployments that do not involve migrating off GCP.
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。
Distill books, documents, folders, or URLs into compact, practical Agent Skills. Use when source material should become either one reusable skill package or a split set of focused skills, each with a concise SKILL.md plus detailed references and 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。
当用户直接要求进行数据集压缩测评，或在量化调优过程中需要使用 AISBench 评测工具输出数据集精度等场景时使用。 本 Skill 负责：使用 AISBench 数据集压缩代码、用 RBF Kernel Herding 算法生成压缩后的数据子集（coreset），并按用户目标交付两种产物之一： ① 接入量化调优主流程：产出独立的 coreset 数据集配置（不覆盖原始数据），回传「全集 config_name + coreset config_name」给编排层，供 evaluation.yaml 切换； ② 仅数据集压缩测评（不做量化调优）：基于 example 模板生成「全集测试脚本」和「子集测试脚本」两份 shell 脚本。 暂时支持 AIME 2025 和 GPQA，其他数据集需要再结合 aisbench tools目录下 herding_coreset_selector 源码进行分析。
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。