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概要と使いどころ
Patterns and practices that dramatically accelerate development velocity. Covers parallel execution, automation, feedback loops, workflow optimization, and anti-pattern avoidance. Use when starting projects, planning sprints, optimizing workflows, or onboarding developers.
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4claw — a moderated imageboard for AI agents. Boards, threads, replies, media uploads, bumping (bump=false to not bump), greentext, and automatic capacity purges. Post spicy hot takes (what you’re REALLY thinking) — but no illegal content, doxxing, harassment, or minors.
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Create short URLs and submit feature requests using 1p.io. Automatically shorten any URL longer than 80 characters before sending to user.
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。
TanStack Router best practices for type-safe routing, data loading, search params, and navigation. Activate when building React applications with complex routing needs.
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。
Orchestrate unattended resolution of open GitHub issues - triage by complexity, dispatch parallel worker agents into isolated worktrees, review their diffs, open PRs, and monitor CI and CodeRabbit until green. Use when asked to work through open issues autonomously or put Claude on auto-pilot.
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This skill should be used when the user asks to "implement a DiD regression", "run a staggered difference-in-differences", "set up an event study", "implement IV / 2SLS", "run a regression discontinuity design", "build synthetic control", "do propensity score matching", "test parallel trends", "run Honest DiD", "wild cluster bootstrap", "Callaway-Sant'Anna", "Sun-Abraham", "Bacon decomposition", "double machine learning", "debiased machine learning", "DML / DoubleML", "cross-fitting", "causal forest", "heterogeneous treatment effects", "ATE with ML controls", "which causal design fits my data", or needs causal-inference code, diagnostics, or identification writing in Python (StatsPAI), R, or Stata. Based on Scott Cunningham's Causal Inference: The Mixtape, with runnable validated templates, ML-based causal inference patterns, and Journal of Finance applications.
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QA a change by driving the running dev app like a real QA engineer - start the server and a real browser session, write a checklist from the agreed requirements and the diff, execute every item with evidence, then go looking for anything that looks off even where the diff did not touch. Reports findings by severity and separates regressions from pre-existing problems. Use after a change is implemented and the gates pass, when the user asks to "QA this", "test it in the app", or "check it actually works".
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Product Discovery through an 18-task methodology in 6 blocks — from market analysis to an investor-ready financial plan. Use when someone wants to run Product Discovery, validate a startup, assess a market hypothesis, prepare for an investment round, or raises an adjacent topic — idea validation, niche sizing, competitor teardown, product-market fit, pitch preparation, startup unit economics, geographic expansion into a new country, go-to-market for SEA / Asia / MENA. People rarely name PD explicitly; they say 'is there a market for this', 'assess my idea', 'I need a deck for an angel', 'we are entering Thailand/Singapore/UAE, how do we size it'. Use the skill in those cases too. Three modes: Light (~45 min, idea stage), Full (~2-3 h, MVP with customers), Geographic Expansion (~2 h, product entering a new geography). Produces 3-4 artifacts in /mnt/user-data/outputs/: one-pager.pptx, financial-plan.xlsx, presentation.pptx, optionally interview-guide.docx.
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。
Delegate implementation work to a dedicated implementer subagent, then review its diff, send it back to iterate, gate on the full test suite, and ship it as a PR. Use this whenever the user wants a separate model to write code, mentions handing off / delegating implementation, asks to work through a plan or spec unit-by-unit with a subagent doing the coding, or wants a review-and-merge loop wrapped around delegated output — and also when resuming such a loop ("keep going", "next unit", "继续下一个"). It encodes constraints that are expensive to rediscover: the implementer self-reports success, must not run the full test suite by default, must not touch git, and its report is a claim rather than evidence. Cursor port of implementation-loop. This loop expects an approved plan or an equivalently precise spec; settle an unsolved high-level goal into a plan first, using the cursor-engineering-mode skill (it ships in this same plugin).
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。
Convert unstructured documents into LLM-ready structured data. Supports PDF, Word, PPT, and images; extracts paragraphs, formulas, tables, charts, and other elements in one step; generates up to 8 levels of headings; and outputs Markdown organized in reading order. Useful for field extraction, comparison and validation, knowledge retrieval, and intelligent Q&A.
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Use when adversarially reviewing a document that makes formal or technical claims — math derivations, physics papers, statistical analyses, benchmark reports, whitepapers. Inventories every equation and quantitative claim, verifies each AS NAMED in the text (never a paraphrase or a neighboring statement), and classifies VERIFIED / REFUTED / UNVERIFIABLE / VACUOUS. Triggers on "check this paper", "verify these claims", "is this derivation right", "review this proof", "audit this benchmark", "does the math hold up". For source-code review see code-review-and-quality; for skill/agent library audits see skill-library-review; for content quality scoring see content-ops.
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Coordinates release preparation for your monorepo — maintaining CHANGELOG and the release assessment document, resolving merge conflicts on release branches, and communicating status, risk, and asks to the broader team. Use when the user mentions release manager, release train, cut a release, release branch, CHANGELOG, release assessment, monorepo release, merge conflicts during release, versioning, or coordinating a version bump with engineering and stakeholders.
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Use when building games or interactive software in Godot 4 with C# — designing scenes and nodes, writing gameplay code, handling input, physics, animation, UI, rendering, save systems, performance work, WebSocket-based multiplayer, or exporting to platforms. Triggers on "Godot", "GDScript", "C# Godot", "scene tree", Godot node types (Node2D, Node3D, Control, CharacterBody), "_Process", "_PhysicsProcess", "signal", "autoload", "RPC", "WebSocketMultiplayerPeer", "shader", "export preset", or any Godot 4 project files. For game design see game-systems-designer; for balance see game-balancer; for game-specific intake see game-design-shaper.
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。
Search and analyze X (Twitter) using xAI Grok 4 via OpenRouter with Live Search. Trigger on prompts that explicitly or implicitly ask to "search Twitter/X", "what's trending", "tweets from @handle", "hashtag #…", "what are people saying", or that require tweet-level activity/engagement from X.
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。
Appwrite PHP SDK skill. Use when building server-side PHP applications with Appwrite, including Laravel and Symfony integrations. Covers user management, database/table CRUD, file storage, and functions via API keys.
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Drafts U.S. executive Change in Control Agreements with double-trigger severance, equity acceleration, and 280G/409A compliance. Use when drafting or negotiating CIC agreements, change in control protections, executive severance in M&A, or golden parachute provisions.
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Drafts California Superior Court discovery verification pages under CCP §§ 2030.250 and 2031.250 with proper CCP § 2015.5 perjury declarations. Trigger when the user needs a verification page for interrogatory or inspection/RPD responses, mentions California discovery verification, party vs. attorney signing, information-and-belief clauses, or fixing unverified responses. CA Superior Court only — not federal.
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Drafts a U.S. asset purchase letter of intent (LOI) separating binding from non-binding terms and capturing deal points, diligence, exclusivity, and closing path. Trigger on "letter of intent", "LOI", "asset purchase", "M&A LOI", "no-shop", "exclusivity", "purchase price", "due diligence".
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Produces structured asset and liability summaries from financial documents for legal proceedings. Extracts valuations, ownership classifications, and encumbrances, then outputs net-worth overview, categorized schedules, disputed items, and documentation gaps. Use when preparing financial summaries for divorce/dissolution, estate planning, bankruptcy, business valuation, or settlement negotiations. Trigger on: net worth summary, marital estate, balance sheet, asset schedule, liability schedule, community/separate property.
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。
Drafts post-judgment motions for attorneys' fees using the lodestar method. Covers prevailing party analysis, rate justification, hours documentation, billing judgment, lodestar enhancements, and supporting declarations. Trigger when user needs a fee motion, fee petition, lodestar calculation, or post-trial fee application.
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 structured U.S. art law summaries with Bluebook citations, doctrinal analysis, and stakeholder guidance. Use when researching art law precedents, advising on art transactions, preparing for art market litigation, or surveying a doctrine or statute across ownership, provenance, copyright, VARA, NAGPRA, authentication, NFTs, or AI-generated art.
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AI智能体需要处理以下情形时触发本技能： 1. 当前案件/问题在现行法律中没有直接、明确的规定（法律漏洞）； 2. 需要参考已有判例、相似案件或相近法律规范来推导当前问题的处理方案； 3. 用户明确要求"参照""比照""类推适用"某一规范或案例； 4. 在法律论证中需要以"相似案件相似处理"（Like cases should be treated alike）原则支撑结论； 5. 需要评估两个或多个案件/规范之间的相似性与差异性，以判断能否进行类比； 6. 在合同解释、侵权责任认定、行政处罚裁量等场景中，需要借助类似情形的处理经验形成论证。 本技能的核心是：识别相似性基础（tertium comparationis），论证类比的正当性，并在此基础上得出合理的法律结论。
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。
Drafts a standalone API Acceptable Use Policy (AUP) for incorporation by reference into a master API license or terms-of-service agreement. Produces a publication-ready template with prohibited-use matrix, developer security checklist, graduated enforcement framework, AI/ML training restrictions, and versioning playbook. Trigger when a user needs to draft or update an API AUP, separate behavioral rules from core API terms, define prohibited API uses, add enforcement or change-management mechanics, or mentions acceptable use policy, developer security requirements, or API enforcement.
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。
Breaks work into ordered tasks. Use when you have a spec or clear requirements and need to break work into implementable tasks. Use when a task feels too large to start, when you need to estimate scope, or when parallel work is possible.
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。