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Experimental behavioral evaluator. Drives a running artifact via browser/curl/CLI and records rubric evidence; the runner is not yet a fully implemented automated evaluation system.

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Fetch external sources (web / arXiv / GitHub) and turn them into scanner-gated wiki CANDIDATES — never canonical pages. Every fetched byte is DATA; url-guarded, size-capped, redirect-re-validated, injection-scanned, and secret-scrubbed before a candidate is even created. Requires network. NOT for capturing local knowledge (see mk:wiki); NOT for one-shot page-to-markdown (see mk:web-to-markdown).

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。

Copies or adapts features from external repos/apps into this project. Triggers on 'copy this from', 'replicate'. NOT for packing this project (mk:pack); NOT for URL fetches (mk:web-to-markdown).

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。

Spec-driven manual QA testing and Playwright E2E code generation. Orchestrates browser skills to navigate apps like a human tester, producing structured test reports or production-ready .spec.ts files. Always prompts user for credentials — never guesses or stores auth. Use for QA testing, E2E generation, login flow testing, or exploratory testing from a URL. NOT for AI-autonomous flows (see mk:agent-browser).

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 a decision needs deep multi-source technical research with cited evidence — technology evaluation, ecosystem comparison, standards/spec fact-finding, "how do others solve X". Triggers on "research X", "deep dive on X", "evaluate X vs Y", "find best practices for X". NOT for library/API doc lookup (see mk:docs-finder); NOT for one-shot URL fetch (see mk:web-to-markdown); NOT for internal codebase discovery (see mk:scout); NOT for project-only Q&A (see mk:ask-me); NOT for comparing solution designs (see mk:brainstorming); NOT for root-cause debugging (see mk:investigate); NOT for wiki ingestion of sources (see mk:wiki-research).

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 copy-catting, replicating, or adapting features from external systems, repos, apps, or ideas into the current project. Triggers on 'copy this from', 'replicate', 'clone this feature', 'how does X do Y', 'chom', 'like how X does it', 'port from', 'build like', '1:1 copy'. NOT for packing the current project (see mk:pack); NOT for one-shot URL-to-markdown fetches (see mk:web-to-markdown).

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。

Experimental behavioral evaluator. Drives a running artifact via browser/curl/CLI and records rubric evidence; the runner is not yet a fully implemented automated evaluation system.

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。

Deep spec analysis on a Confluence page. Reads page (+ children) via the confluence-as wrapper, produces a structured Spec Research Report with requirements, acceptance criteria, gaps, ambiguities, and suggested user stories for human review. Read-only — does NOT create Jira tickets. Triggers: 'analyze spec for PAGE-ID', 'extract requirements from confluence page', 'spec research on URL'. NOT for raw page CRUD (mk:confluence-page); NOT for ticket complexity scoring (mk:planning-engine).

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 applications to Vercel with auto-detection and preview URLs

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。

Analyze external URL to evaluate fit with oh-my-customcode project and auto-create GitHub issue with verdict

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。

Publish a runnable prototype and return a shareable link. Prepares the prototype to run, starts/serves it (or deploys), and hands back a URL plus run instructions for review. Use when the user says "share the prototype", "give me a link", "publish this", "let the team see it", "deploy the preview", or is ready to circulate a prototype for feedback. Final stage of the Product Design set.

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。

Orchestrator for the Product Design skill set — turn early ideas into reviewable prototypes. Routes to the right sub-skill (get-context, research, audit, ideate, image-to-code, url-to-code, prototype, qa, share) based on where you are in the design process. Use when the user gives a high-level design goal like "design a UI for X", "prototype this idea", "rebuild this screen", "review our flow", or isn't sure which design step they need. Delegates to design-* skills.

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。

Audit an existing UI for usability, hierarchy, accessibility, and conversion problems. Reviews a live URL, screenshot, or codebase against heuristics and reports prioritized issues with fixes. Use when the user says "audit this UI", "what's wrong with this screen", "UX review", "find the problems", "why is this confusing", or has existing interface to critique. Part of the Product Design set. Can drive a browser for live 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。

Use this skill whenever the user asks to generate, edit, inpaint, restyle, or create bitmap images through OpenAI's Responses API image_generation tool from Codex. This skill calls the Responses API, saves generated base64 image results to files, uses no npm or pip dependencies, works on Linux/macOS/Windows with either Node.js 18+ or Python 3, and reuses Codex's configured API base URL, model, and key instead of asking the user for credentials.

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 data validity through Openapi Trust - email deliverability, mobile number HLR checks, IP reputation, URL safety scans, document-based identity verification (IDV). Use for anti-fraud validation of user-provided contact data and identities.

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Save important context to persistent memory for future sessions. PROACTIVELY USE THIS when you detect: - Decisions: "let's go with", "decided to", "chose", "went with" - Learnings: "I learned", "turns out", "TIL", "realized", "discovered" - Errors resolved: "fixed", "the problem was", "solution was" - Client info: names, emails, phone numbers, addresses, invoice amounts - Preferences: "I prefer", "I like", "don't like", "always use" - References: API keys locations, file paths, URLs, credentials - Patterns: "pattern", "convention", "always do", "standard approach" - Important commands run worth remembering Do NOT ask permission - just capture silently when these patterns appear.

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 building server-driven UIs with htmx, designing hx-* attribute compositions, choosing swap strategies (innerHTML/outerHTML/morph), wiring out-of-band updates, integrating with form validation, or migrating from a SPA back to server-rendered HTML. Triggers: hx-get/hx-post setup, hx-target + hx-swap interplay, hx-trigger debouncing, OOB swaps for parts of the page outside the request target, response headers like HX-Trigger and HX-Push-Url, integration with Hyperscript or Alpine.js, accessibility considerations on partial updates. NOT for full SPAs (use React/Vue), framework-specific server libraries (use those skills directly), or non-HTML responses.

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。

Expert legal research agent for finding and scraping expungement data state by state. Knows authoritative sources, URL patterns, Firecrawl configuration, and 2026 legal landscape.

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。

API migration strategies, deprecation workflows, and header/URL/content versioning. Activate on: API versioning, backward compatibility, deprecation, breaking change, API migration, v1 v2, sunset header. NOT for: schema evolution in data (use schema-evolution-manager), gateway routing (use api-gateway-reverse-proxy-expert).

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。

Generates OpenAPI 3.0/3.1 specs, TypeScript client types, and curl examples from existing route handler code. Supports Express, Next.js API routes, Fastify, and Hono. Produces complete documentation including auth requirements, rate limits, error codes, and request/response examples. Activate on: 'generate API docs', 'OpenAPI from code', 'document endpoints', 'API reference', 'swagger generation', 'endpoint documentation'. NOT for: API design from scratch (use api-architect), deployment (use devops-automator), testing (use test-automation-expert).

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Structures biological experiments so inference is valid by construction, covering Fisher's principles (randomization, replication, local control), the experimental-vs-observational unit distinction and pseudoreplication (Hurlbert 1984; Lazic 2018), randomization mechanics (complete, restricted, stratified, rerandomization, run-order), blocking layouts (randomized complete block, Latin square, incomplete block), factorial designs and interactions, and the split-plot/nested error strata hidden inside multi-batch genomics. Use when deciding the experimental unit and what counts as a replicate, planning randomization and run order, choosing a blocked/factorial/split-plot/nested layout, avoiding pseudoreplication in cell-culture or animal studies, or specifying the random-effects structure of the analysis model. For assigning samples to sequencing batches/lanes/plates and batch-effect correction see experimental-design/batch-design; for regulated clinical-trial randomization see clinical-biostatistics.

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。

Bulk-query Ensembl BioMart (and other BioMart instances) for cross-database ID mapping, gene/transcript/exon coordinates, and ortholog tables. Use when batch-converting Ensembl IDs to other namespaces (HGNC, RefSeq, UniProt, Entrez), pulling gene coordinate tables for thousands of genes, building ortholog wide-tables across species, or replacing slow Ensembl REST loops with one-shot bulk export. Encodes BioMart's XML query format, R biomaRt vs Python pybiomart trade-off, mart-vs-dataset hierarchy, and the URL endpoint that's BioMart-specific (separate from rest.ensembl.org).

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。

Interactive Archon integration for knowledge base and project management via REST API. On first use, asks for Archon host URL. Use when searching documentation, managing projects/tasks, or querying indexed knowledge. Provides RAG-powered semantic search, website crawling, document upload, hierarchical project/task management, and document versioning. Always try Archon first for external documentation and knowledge retrieval before using other sources.

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。

Process and generate multimedia content using Google Gemini API. Capabilities include analyze audio files (transcription with timestamps, summarization, speech understanding, music/sound analysis up to 9.5 hours), understand images (captioning, object detection, OCR, visual Q&A, segmentation), process videos (scene detection, Q&A, temporal analysis, YouTube URLs, up to 6 hours), extract from documents (PDF tables, forms, charts, diagrams, multi-page), generate images (text-to-image, editing, composition, refinement). Use when working with audio/video files, analyzing images or screenshots, processing PDF documents, extracting structured data from media, creating images from text prompts, or implementing multimodal AI features. Supports multiple models (Gemini 2.5/2.0) with context windows up to 2M 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。