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概要と使いどころ
Helps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable skill.
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。
Doctrine for drafting and keeping working-group docs under `docs/wg/**` — RFC/RFD specs and findings/research/glossary. A WG doc is a language-agnostic, code-agnostic study of a domain: it argues *why* and defines *what*, never *how in our code*. Use when writing or editing anything under `docs/wg/`, an RFC/RFD, a spec, a design note, a glossary, or research findings — including "write up the design", "document the spec", or "capture what we learned". Not for plans/TODOs (untracked `*.plan.md`), user docs, or SDK API refs — use `docs` to route those.
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。
Find 10x product opportunities and high-leverage improvements. Use when user wants strategic product thinking, mentions '10x', wants to find high-impact features, or says 'what would make this 10x better', 'product strategy', or 'what should we build next'.
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。
FLOW framework integration for bloggers. Evidence-led content workflow using the Find, Optimize, Win loop with stage-specific AI prompts from the FLOW knowledge base (30 blog-applicable prompts, CC BY 4.0). Use when user says "FLOW", "FLOW framework", "blog flow", "evidence-led blogging", "find optimize win", or wants stage-specific blog prompts.
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。
FLOW framework integration for bloggers. Evidence-led content workflow using the Find, Optimize, Win loop with stage-specific AI prompts from the FLOW knowledge base (30 blog-applicable prompts, adapted from FLOW under CC BY 4.0). Use when user says "FLOW framework", "blog flow", "/blog flow", "evidence-led blogging", or "find optimize win", or asks for stage-specific blog prompts. Not for generic prompt writing.
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。
Plans, runs, and synthesizes customer research — interviews, surveys, win-loss analysis, and message testing — into findings that change decisions. Use this when the customer is being guessed at, before positioning or a major launch, when churn or conversion has an unexplained cause, or when a team is arguing from anecdotes. Also use to turn raw interview notes into usable findings.
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。
Runs the ongoing relationship with accounts after the sale — segmenting coverage against account value, building a health score that predicts rather than describes, running reviews customers find worth attending, forecasting renewals honestly, and finding expansion that follows usage instead of quota. Use this to design a customer success motion, decide who gets a named contact, work out why renewals surprise you, or fix a health score everyone ignores.
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。
Builds the help center, in-product guidance, and knowledge base that let customers resolve problems without contacting anyone — content, findability, maintenance, and deflection measurement. Use this to build or fix a help center, reduce support volume, write documentation for customers, improve findability, or decide what deserves a help article versus a product fix.
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。
Find desktop UI elements using UIAutomation (fast, accurate) or vision recognition (fallback). When you need to locate buttons/menus/icons, get element positions before clicking, or verify UI state. For browser webpage elements, use browser_* tools instead.
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。
Automatically find references for academic paper Markdown files. Reads full paper text, identifies each knowledge point requiring citation (epidemiological data, mechanism descriptions, existing research conclusions, etc.), searches PubMed for 3-5 most relevant articles per kn...
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。
Extends a mechanistic or association-level biomedical finding into a staged validation pathway that moves from descriptive evidence toward stronger functional support, mechanistic specificity, and clinical relevance. Use this skill when a user has a pathway, biomarker, cell-state, target, mechanism, or association finding and needs to decide what should be validated next, in what order, and which evidence layers are necessary versus optional. Do not default to maximal validation stacks. Build a structured validation ladder with a primary route, stronger upgrade route, and optional extensions.
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。
Identifies real, evidence-audited, topic-specific research gaps in medical research by first retrieving and verifying literature from trusted sources, then mapping the current evidence landscape, rejecting pseudo-gaps, and converting only medium/high-confidence gaps into study-ready research opportunities. Always require real literature retrieval before formal gap claims. Never fabricate references, metadata, or findings.
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。
Designs cell-based and animal-based validation plans that translate computational, omics, biomarker, genetic, or clinical findings into experimentally testable validation routes. Always use this skill whenever a user wants to move from an in silico, statistical, or clinical association finding toward wet-lab validation using cell systems, organoid-like systems, xenograft or genetically relevant animal models. It should define the exact claim to test, separate mechanism-testing from association-support and translational-support goals, choose the best-fit model family, specify perturbation strategy, readouts, controls, sequencing of experiments, and four workload configurations (Lite / Standard / Advanced / Publication+) with one recommended primary plan. Never fabricate model availability, reagent availability, species relevance, assay feasibility, phenotype penetrance, expected effect sizes, validation success, or literature references.
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。
Matches your study to appropriate journals based on topic, design, and evidence strength. Use when deciding where to submit a manuscript, comparing journal options by impact factor vs scope fit vs method tolerance, or finding a realistic submission target after a rejection. Also triggers on "where should I submit this paper", "which journal is best for my study", "find journals for my manuscript", "is this a good fit for [journal]", or "I need a journal with IF around 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。
Rewrites technical research content into a structured lay summary that cross-disciplinary teams can quickly understand and act on. Use when the user wants to explain research to colleagues outside their specialty — clinicians, wet-lab scientists, bioinformaticians, product managers, or leadership. Trigger on: "lay summary", "explain my research to the team", "non-technical summary", "cross-disciplinary summary", "translate my findings", "align our team on the study", or any request to communicate research goals, findings, or next steps to a mixed or non-specialist audience. Part of the AIPOCH Academic Writing skill hub. Sits midstream: after research content is clarified, before downstream deliverables like slide decks or graphical abstracts.
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 domains, compare prices, find promo codes, get purchase recommendations. Use when user wants to buy a domain, check domain prices, find domain deals, compare registrars, or search for .ai/.com domains.
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。
This skill should be used when the user asks to "find skills in the wild", "assimilate popular workflows", "discover SKILL.md files in repos", "research external skills", "find workflow patterns", "survey the skill landscape", "what skills exist out there", or wants to investigate public repositories for extractable processes, babysitter plugins, and reusable procedural insights. Searches GitHub for SKILL.md files, classifies repos by archetype, and maintains structured research under docs/reference-repos/.
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。
Systematically explore and test a mobile app on iOS/Android with agent-device to find bugs, UX issues, and other problems. Use when asked to "dogfood", "QA", "exploratory test", "find issues", "bug hunt", or "test this app" on mobile. Produces a structured report with reproducible evidence: screenshots, optional repro videos, and detailed steps for every issue.
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。
Comprehensive adversarial audit of a theory, proof, math/econ paper, codebase, or set of claims — decompose into components, fan out independent skeptics that must return CONCRETE defects, adjudicate every finding with a separate judge, fix all confirmed defects, then re-verify. Use when correctness must be bulletproof and single-pass or round-by-round review is too slow and too shallow. Invoke for "audit this rigorously", "find ALL the bugs/gaps", "make this rock solid", "converge faster on correctness".
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。
Keeps a project's to-do list and memory in GitHub issues — files each finding (from a review, an audit, or the user) as its own issue without duplicates, lists the open issues that bear on the current work, and closes an issue with a comment recording what was wrong, what was tried, what worked, and how it was fixed. Use when review findings or a list of problems should become tracked work (a paper's misleading claims, a codebase's bugs, improvements for later), when starting or resuming work and deciding what to do next, or when a fix has landed and its issue should be closed with a record. Needs the GitHub CLI; warns before posting to a public 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。
Go and find the reels that are actually working right now in the user's niche, rank them by how far each beat its own account, name the hook formula each one used, and turn that into a swipe file they can shoot from. Use when the user says "find viral videos", "what's working right now", "what are people posting in my niche", "reverse engineer this account", "build me a swipe file", "why is this reel doing numbers", or asks what to make next and has no evidence to answer with.
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。
Add AI navigation in Unity 6.3 LTS: bake a NavMesh with the AI Navigation package (NavMeshSurface), move agents with NavMeshAgent.SetDestination, and handle dynamic obstacles. Use when setting up pathfinding, making an enemy chase the player, baking navigation, or when the user mentions NavMesh, NavMeshAgent, NavMeshSurface, NavMeshObstacle, or Unity pathfinding.
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 NPC and enemy decision-making with finite state machines, behavior trees, steering behaviors, and A* pathfinding — engine-neutral algorithms that pair with the detected engine's navigation API. Use when building enemy AI, an FSM or behavior tree, steering/flocking, or pathfinding, or when the user mentions state machine, behavior tree, blackboard, A*, navmesh, seek, or patrol/chase.
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。
Find what is actually working in the user's niche on YouTube and rank it by how far each video beat its own channel, then name the formula. Use for "what's working right now", "find viral videos in my niche", "why did this blow up", competitor research, or a swipe file.
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。