586 件 ・ 関連度順
概要と使いどころ
Scaffold or standardize a production-ready repository structure with specs, source layout, tests, CI, agent context, config examples, release notes, and operational docs. Use when starting a new repo, turning a prototype into a maintainable project, adding missing repository foundations, or creating a repo skeleton before implementation. For AGENTS.md-only context scaffolding, use agentsmd-scaffold.
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 asked to design a new feature or project
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。
Verifies factual claims in documents using web search and official sources, then proposes corrections with user confirmation. Use when the user asks to fact-check, verify information, validate claims, check accuracy, or update outdated information in documents. Supports AI model specs, technical documentation, statistics, and general factual statements.
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Documentation-first project planning that produces implementation-ready blueprints and single-shot coding agent prompts. Generates 4 interconnected docs — SPECIFICATION.md, IMPLEMENTATION.md, TASKS.md, BRANDING.md — plus a self-contained PROMPT.md for autonomous execution by any coding agent. Includes interactive tech stack selection, design pattern recommendations, and architecture decisions with trade-off analysis. Trigger when user wants to: plan a project, create specs, architect a system, break work into tasks, choose a tech stack, get design pattern advice, generate a coding agent prompt, or do documentation-first development. Phrases: "plan my project", "spec this out", "architect", "help me plan", "what stack should I use", "generate a prompt", "break this into tasks", "project docs", "I want to build X".
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Automatically generate TLA+ specifications from source code (C/C++, Python) for formal verification of distributed systems. Use when users need to: (1) Generate TLA+ specs from program implementations, (2) Model distributed systems, consensus protocols, or concurrent algorithms, (3) Extract state variables, actions, and invariants from code, (4) Create formal specifications for model checking with TLC, (5) Verify safety and liveness properties of distributed systems. Particularly effective for message-passing systems, replication protocols, consensus algorithms, and distributed transactions.
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Builds traceability matrices connecting requirements to design documents to source code implementation, tracking the complete development lifecycle. Use when you need to verify implementation completeness, ensure all requirements are implemented in code, generate compliance documentation, audit requirement coverage, identify orphaned code, or create traceability reports for stakeholders. Supports parsing requirements from Markdown, Word, and PDFs; extracting design from architecture docs and API specs; and scanning source code for implementations. Outputs to Markdown tables, Excel/CSV, and HTML visualizations.
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Automatically simplify and minimize TLA+ specifications by reducing redundant state variables, merging equivalent actions, and minimizing invariants while preserving specified properties. Use when working with TLA+ specifications that need optimization, simplification, or reduction. Triggers when users ask to minimize, reduce, simplify, or optimize TLA+ specs, or when they want to remove redundancy from formal specifications while maintaining semantic equivalence.
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 comprehensive API documentation from repository sources including OpenAPI specs, code comments, docstrings, and existing documentation. Use when documenting APIs, creating API reference guides, or summarizing API functionality from codebases. Extracts endpoint details, request/response schemas, authentication methods, and generates code examples. Triggers when users ask to document APIs, generate API docs, create API reference, or summarize API endpoints from a repository.
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。
Interview employees to identify recurring daily work, office workflows, and repetitive tasks that can be automated, co-piloted, or supported by AI. Use when the user wants to find what AI can take off their plate, map available tools such as ChatGPT/OpenAI, Claude, Microsoft Copilot, Excel or PowerPoint add-ins, Google Workspace, no-code/RPA tools, coding agents, or internal automation platforms, and write tool-aware specs with fallback paths.
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。
Think and work like an expert Control Systems Engineer. Use when a task calls for Control Systems Engineer judgment. Reasons from plant dynamics, stability margins, and disturbance-rejection specs through Bode/Nyquist and Routh-Hurwitz analysis, LQR/H-infinity and pole placement, Kalman/EKF observers, RGA pairing, and HIL validation while treating integrator windup, actuator saturation and backlash limit cycles, sensor delay masking phase margin, and estimator divergence as first-class failure modes.
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。
Think and work like an expert Clinical Data Manager. Use when a task calls for Clinical Data Manager judgment. Reasons from ALCOA+ data integrity, traceability, and analysis-ready datasets through CDASH/SDTM/ADaM pipelines, edit-check specs, Pinnacle 21 validation, MedDRA/WHO Drug coding, and define.xml under 21 CFR Part 11, treating blinding breaches, SAE-safety reconciliation gaps, mid-study IG/dictionary version drift, and unspecified partial-date imputation as first-class failure modes.
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。
Think and work like an expert Astronautical Engineer. Use when a task calls for Astronautical Engineer judgment. Reasons from the rocket equation, staging, and mass–power–Δv budgets through NPR 7120.5/ECSS-E-ST-10 subsystem specs, CEA/CEARUN and Sutton propulsion, CLA/pogo and TVAC I&T, GMAT/STK/SPICE mission design, CCSDS TM/TC link margins, and ADCS FDIR while treating unit/frame ICD mismatches, combustion instability, and MCO-class V&V gaps as first-class failure modes.
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。
Provider-independent title design and kinetic typography direction for video, animation, ads, explainers, social hooks, brand films, lyric/text videos, title cards, main titles, lower thirds, captions-as-design, and other motion-led text. Use when an agent must plan, prompt, implement, hand off, iterate, or QA typography hierarchy, readability, timing, reveal/hold/exit, motion semantics, line breaks, safe areas, localization, brand constraints, accessibility, flashing/motion safety, or production specs for animated text.
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。
Provider-independent FFmpeg finishing workflow for AI agents preparing generated or edited media deliverables. Use when finalizing images, image sequences, video, audio, captions, overlays, social/export variants, checksums, manifests, delivery specs, and QA with ffmpeg/ffprobe, including transcode versus stream-copy decisions, scaling, frame-rate handling, color metadata caution, loudness normalization, subtitle burn-in or sidecars, concat/trim, GIFs, batch variants, and reproducible command reporting.
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。
Provider-independent production workflow for recutting long talking-head, podcast, interview, webinar, panel, lecture, livestream, founder call, or customer-call footage into short clips, highlight reels, trailers, audiograms, captioned vertical videos, and social cutdowns. Use when an agent must ingest source recordings, transcribe and diarize speakers, select quotes without misrepresentation, preserve context, handle consent/rights/disclosures, clean jump cuts and audio, add captions, graphics, lower thirds, b-roll, platform variants, synthetic-media boundaries, review packets, delivery specs, and recut QA.
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。
Provider-independent workflow for producing UGC-style paid and social ads, creator testimonials, product demos, hooks, before/after concepts, app walkthroughs, TikTok/Reels/Shorts variants, synthetic-persona or avatar UGC, ad-policy checks, disclosure planning, rights/consent review, delivery specs, and QA.
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。
Produce full-length audiobooks and long-form narration with generative voice tools. Use when the task is to turn a manuscript or long text into hours of spoken audio: preparing the manuscript for narration (front/back matter, footnotes, tables, dialogue), casting a single narrator or full cast, controlling pronunciation and voice consistency across a whole book, running a proofing/QC listen, meeting a retailer's technical delivery specs (RMS, peak, noise floor, room tone, chapterized files, metadata), and choosing a distribution route under each platform's current AI-narration policy. Not for short TTS clips, single-line voiceover, podcast production, or captioning existing video.
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。
Provider-independent production design direction for AI-generated media. Use when translating narrative, brand, advertising, product, social, film, or worldbuilding intent into sets, locations, props, color and material palettes, era research, graphic and environmental design, continuity bibles, image/video prompt planning, reference strategy, production constraints, iteration notes, handoff specs, and QA.
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。
Provider-independent audio mixing and mastering direction for AI agents finishing generated videos, ads, trailers, explainers, podcasts, recuts, avatar clips, music videos, documentaries, and social content. Use when planning, mixing, repairing, mastering, QCing, or delivering dialogue, music, ambience, and sound effects, including loudness/true-peak targets, intelligibility, accessibility, stems, stereo/immersive decisions, platform/client specs, and final audio QA.
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。
Provider-independent media quality control and delivery direction for generated images, video, audio, ads, product content, social clips, broadcast and streaming assets, localization packages, and mixed-source edits. Use when an agent must define final export specs, inspect technical and perceptual quality, check captions/audio/color/accessibility/provenance/rights, create delivery sheets, version filenames, checksums, manifests, package deliverables, set acceptance or rejection criteria, and perform final delivery QA before handoff to a platform, broadcaster, streamer, client, or ad trafficker.
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 requirement quality principles when generating or validating feature specifications. Enforces feature completeness, scenario structure, AC verifiability, feature independence, and implementation slice quality. Use when writing feature specs, validating existing requirements, or when the user mentions 'validate this spec', 'check this feature', 'requirement quality', 'is this spec complete', or 'requirement-quality'. This skill governs the craft of writing individual feature specifications — not technical design (see design-blueprint), not implementation (see code-forge).
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。
Propose a new change with all artifacts generated in one step. Use when the user wants to quickly describe what they want to build and get a complete proposal with design, specs, and tasks ready for implementation.
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。
Maxim Design System Intelligence — token architecture (primitive→semantic→component), component library governance, CSS variable systems, Tailwind integration, design-to-code handoff, Storybook, Figma token export. Activate for design system creation, token audits, component specs, dark mode systems, brand-to-token sync. Powered by Maxim behavioral science + Atomic Design + TOGAF governance.
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。
Token architecture, component specifications, and slide generation. Three-layer tokens (primitive→semantic→component), CSS variables, spacing/typography scales, component specs, strategic slide creation. Use for design tokens, systematic design, brand-compliant presentations.
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。