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Transforms validated /linear-walkthrough artifacts into one presentation-ready deck outline per major codebase component. Use when the user asks to create a presentation, prepare a walkthrough deck, build an onboarding deck, produce an architecture review deck, or summarize a codebase for a technical audience. Requires walkthrough directory output -- reads unified walkthrough, per-section files, validation reports, coverage maps, entry points, and open questions -- then orchestrates four parallel agent phases to produce slide outlines with speaker notes, evidence references, and suggested visuals.

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Validate and iterate on the SDLC Layer Separation Architecture implementation across the check categories defined in its Evaluation Checklist — cross-references, doc completeness, knowledge-explorer layer filters, research entry metadata, integration points, and plan consistency. Produces a structured findings report and optionally applies safe fixes. Use when validating a first-pass implementation, before claiming layer work complete, auditing layer docs or schema, or running --dry-run to preview findings without changes.

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Integrates patterns from external sources (URLs or local files) into local skills, agents, and plugins. Use when comparing external agent definitions against local equivalents, extracting best practices from frameworks like GSD or BMAD-METHOD, enhancing local skills with external workflow patterns, or adding interoperability with external tool ecosystems. Runs a 3-phase workflow — parallel candidate mapping, contextual enhancement, and validation — with source attribution and backlog tracking for deferred enhancements.

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Advises on setting up LLM-based text classification as a validated measurement design. Covers codebook construction, learning-regime and model choice, prompt construction, validation against human coding with kappa and F1, hybrid human-LLM review, downstream measurement error, and reporting, plus a short resumable batch pattern with a rule-based baseline for repeated free text against a closed codebook. Use when the user wants to classify, code, or label text at scale. Category discovery goes to topic-modeling.

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Advises on setting up and validating topic models for survey and experimental text. Covers the discovery-versus-measurement question, choosing STM, LDA, or BERTopic, preprocessing consequences, prevalence and content formulas, topic-count selection across coherence, exclusivity and FREX, held-out likelihood, and residuals, validation of topics against human judgment, robustness, and DA-RT-compliant reporting. Use when the user has open-ended responses or another corpus and asks what topics are present, how many topics to use, about STM, searchK, coherence, or FREX, or wants topic prevalence compared across treatment arms or countries. Fixed-codebook coding goes to text-classification.

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。

Advises on setting up and validating topic models for survey and experimental text: discovery vs. measurement, STM/LDA/BERTopic choice, preprocessing, covariate formulas, choosing K across coherence, FREX, held-out likelihood, and residuals, validating topics against human coding, robustness, and reporting. Coding against a fixed codebook goes to text-classification.

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。

Advises on setting up and validating LLM text classification: codebook, learning regime and model choice, prompts, validation against human coding (kappa, F1), hybrid review, measurement error, and reporting, plus a short resumable batch pattern with a rule-based baseline. Use to classify, code, or label text at scale; category discovery goes to topic-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。

Design, create, or validate saved and transient Vapi voice assistants. Use for new phone or web agents, production system prompts and first messages, saved-versus-transient architecture, model/voice/transcriber selection, multilingual compatibility, existing tool attachment, native call-control tools, assistant hooks, and Create Assistant API validation errors.

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。

Review documentation for quality, clarity, SEO, and technical correctness. Optimized for Docus/Nuxt Content but works with any Markdown documentation. Use when asked to: "review docs", "check documentation", "audit docs", "validate documentation", "improve docs quality", "analyze documentation", "check my docs", "review my documentation pages", "validate MDC syntax", "check for SEO issues", "analyze doc structure". Provides actionable recommendations categorized by priority (Critical, Important, Nice-to-have).

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This skill should be used when the user asks to "create a hook", "add a PreToolUse/PostToolUse/Stop hook", "validate tool use", "implement prompt-based hooks", "use ${CLAUDE_PLUGIN_ROOT}", "set up event-driven automation", "block dangerous commands", or mentions hook events (PreToolUse, PostToolUse, Stop, SubagentStop, SessionStart, SessionEnd, UserPromptSubmit, PreCompact, Notification). Provides comprehensive guidance for creating and implementing Claude Code plugin hooks with focus on advanced prompt-based hooks API.

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Microsoft.Extensions.Options patterns including IValidateOptions, strongly-typed settings, validation on startup, and the Options pattern for clean configuration management.

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Cross-verify Claude-generated plans and code using OpenAI Codex and Google Gemini CLI. Provides code review, plan validation, and comparative analysis. Use when needing second opinions on Claude's code or plans, validating technical decisions, or seeking consensus from multiple AI models.

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。

Branch skill for building and improving agents. Use when creating new agents, adapting marketplace agents, validating agent structure, writing system prompts, or improving existing agents. Triggers: 'create agent', 'improve agent', 'validate agent', 'fix agent', 'agent frontmatter', 'system prompt', 'adapt agent', 'customize agent', 'agent examples', 'agent tools'.

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Validate agent YAML frontmatter and configuration. Use before committing agent changes or in CI.

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Pre-commit and pre-merge validation checks. Use before committing changes or declaring work complete to ensure all quality gates pass.

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Use when asked to audit or harden security in this repo — e.g. "security audit", "auditoría de seguridad", "revisa la seguridad", "is this secure", "hardening", "vulnerabilities". Audits code, config, workflows, and secrets exposure. Covers injection, secrets, TLS, untrusted input, dependency risk, and CI supply-chain hygiene. Runs when the user asks about security, not just for code reviews.

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Validate and improve GitHub Well-Architected Framework (WAF) documentation under the content/ folder for architectural correctness, decision quality, explicit trade-offs, and actionable guidance. Use this when reviewing pull requests, markdown files, or draft content in content/ to ensure recommendations are technically sound, context-aware, and guide readers toward correct architecture and design decisions.

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。

TypeScript best practices: strict mode, ESLint rules, file naming, imports, type safety, Zod validation, error handling, and testing with Vitest. Use when writing TypeScript code, reviewing for code quality, fixing lint errors, creating types/interfaces, writing tests, or adding validation.

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。

Odoo-aware idea validation for deciding whether a module, addon, app, or product should exist before implementation. Use when the user wants to validate an Odoo module idea, check whether a solution already exists, assess usefulness or buyer demand, narrow a wedge, or decide whether an addon is worth building.

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 downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for JWT, JWS, and JWE validation paths, header parsing, key selection, claim acceptance, audience and issuer checks, role derivation, and token-to-identity confusion bugs. Use when the user asks to inspect JWT headers or claims, key lookup, `kid` handling, `alg` confusion, audience or issuer validation, role claims, or explain how a token becomes accepted identity or privilege. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.

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实现表单 / 校验 / 提交 / 错误处理时使用。覆盖 React Hook Form + Zod / TanStack Form / Formik / VeeValidate / Formily / Angular Forms。融合受控/非受控 + Schema 校验 + 异步校验 + 防重复提交。

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Uses htmltest to crawl generated documentation or static site output, detect broken internal and external links, and return a link-focused validation report before deploy. This is a narrow docs QA skill for agents validating already-built HTML, not a generic site generator or crawler listing.

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。

Connect agents and IDE assistants to Shopify docs, API schemas, code validation, and store-management workflows through Shopify's official toolkit.

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。

Validates Ansible playbooks in check mode using ansible-playbook --check --diff and the Ansible Python API. Detects idempotency issues, undefined variables, and unreachable hosts before production runs.

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。