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概要と使いどころ
Create distinctive, production-grade frontend interfaces with high design quality. Generates creative, polished code that avoids generic AI aesthetics. Use when the user asks to build web components, pages, artifacts, posters, or applications, or when any design skill requires project context. Call with 'craft' to run the full shape-then-build flow, or 'teach' for design context setup.
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。
Grill the user relentlessly about a plan, decision, or idea. Use when the user wants to stress-test their thinking, or uses any 'grill' trigger phrases.
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。
Plan the UX and UI for a feature before writing code. Runs a structured discovery interview, then produces a design brief that guides implementation. Use during the planning phase to establish design direction, constraints, and strategy before any code is written.
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。
Initialize BMad project configuration and load config variables. Use when any skill needs module-specific configuration values, or when setting up a new BMad 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。
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
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。
Stress-tests a fuzzy or vague feature idea before any PRD, spec, or ticket is written. TRIGGER when: the user has a fuzzy feature idea — one or two paragraphs, vague on users / scope / success — and wants to harden it, or says "grill / stress-test / pressure-test this idea." DO NOT TRIGGER when: the idea is already turned into a spec or PRD; bug fixes or refactors; the idea is hardened and you are ready to write the PRD → creating-prd.
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。
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
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。
Bun runtime, package manager, bundler, and test runner. Use when running scripts with bun, managing packages, serving HTTP with Bun.serve, querying databases with Bun.sql/bun:sqlite/Bun.redis, shell scripting with $, using S3/file I/O, writing tests with bun:test, bundling or compiling to executable, or using any Bun-specific API (spawn, glob, semver, FFI, workers, plugins, HTMLRewriter).
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。
Manage SVG icons as framework components using Denji CLI. Use when the user needs to add, remove, list, or manage SVG icons in React, Preact, Solid, Qwik, Vue, or Svelte projects. Triggers include requests to "add an icon", "set up icons", "manage SVG icons", "remove an icon", "list icons", or any task involving Iconify icons as framework components.
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。
kpopper Hub: optional experimental application for exploring the project record. Use when the user explicitly requests kpopper Hub or its record page, its graph, layout or verification, or has a standing preference for this application. Do not render automatically after mapping or recording. Install the HTML runtime before use. Not for ordinary HTML documents or a text answer from the record. Requests come in any language.
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。
Annotated Documents: optional experimental application for standalone HTML with evidence. Use when the user explicitly requests Annotated Documents or a kpopper HTML document with evidence, refreshes an existing kpopper document, or has a standing preference for this application. Generic HTML requests do not select it. Install the HTML runtime before use. Not for the record's own page or an ordinary website. Requests come in any language.
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。
Read the project's knowledge record before answering from memory or from the files. Use before answering about the project's state, a number, a date, a decision, a source, a deadline or what was agreed - even a casual 'what's the situation with X' or 'is that quote still current'; before changing a recorded value; and whenever the opener or a grounding line names entries. After finding relevant IDs, prefer kpop context to read them with their declared dependencies and checks. Uses pull for concise readings or when the checked reader is unavailable, affects for changed inputs, and search when no ID is known. A missing id is not absence. Not for showing the record's page (the hub skill). Requests come in any language.
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。
Map existing materials into a first knowledge record, or investigate a subject's history in depth. Use whenever the user asks to map, survey, go through, reconstruct or investigate what already exists - folders of documents, decision records, meeting notes, emails, exports, code - or wants a starting record built from existing materials, even when the word 'record' is never said: consult this before listing or reading the files yourself, because a map has an agreed scope, a protocol and a report. Also, once, at a suitable moment, to offer the starting choices where a workspace has no record and the work is clearly ongoing - never on a greeting. Learning while working needs no map. Requests come in any language.
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。
[Workflow] Use when activating the Integration Test Green workflow — drive an integration-test suite to fully green with a bounded convergence loop: verify the whole system (or the named target), adjudicate every failure with /debug-investigate + /integration-test-review before any edit, fix at the owning layer, /changes-review each round's fix diff, re-verify from scratch, then sync spec TCs, the integration-test reference doc, and feature docs.
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。
[Code Quality] Use when you need to perform a security review or audit on any scope — application code (OWASP Top 10 2025), secrets exposure, dependency/supply-chain malware, third-party repository vetting before install, infrastructure/config, CI/CD pipeline, AI-agent risks, and host/VPS compromise detection.
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] Use when the developer wants something explained or taught, or wants to know HOW TO REVIEW a change — by default the current working tasks + changes in context, or whatever the prompt names (a plan, a subsystem, a decision, a concept, a bug). AI derives WHAT to explain from the prompt and ALWAYS delivers a teacher-and-coach review guide, high level first then detail: what was done, a VISUAL MAP (system flowchart, domain ERD, sequence diagrams), the user stories and business rules with their REAL test-case IDs, a REVIEW PATH naming which files to open first and in what order with an exit criterion per stage, the technical concepts needed to follow it, how it works, why THIS solution, every alternative option with pros/cons, the trade-offs accepted, the blast radius, HOW TO TEST AND DEMO the change, the developer's decision levers, and written challenge prompts that provoke the reader to pressure-test the work. Works at ANY scope — from one line of code to the whole project: it SIZES the target into a scope tier, DECOMPOSES a large one into understanding groups, BREAKS the work into tasks before the first deep read, and ACCUMULATES the report group by group on disk so a huge explanation stays complete instead of thinning out. Writes the full report to a single combined markdown file (plans/reports/understand-*.md) that opens with a detailed summary section — or delivers it in full in chat when Step 3 finds no git-ignored directory for it — and always summarizes it in chat. Regardless of coding level. Never interrogates, never quizzes, never blocks.
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。
[Investigation] Use when about to author a new Feature Spec from an idea — investigate all existing Feature Specs AND related code logic first to surface related/overlapping/affected specs, missing features, missing test cases/user stories, system unknowns, and the invariant landscape, before any spec is drafted.
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。
[Testing] Use when you need to drive an integration-test suite to fully green — each round runs /integration-test-verify (whole system by default, or the target named in the prompt), and on ANY failure combines /debug-investigate + /integration-test-review (report-only) to adjudicate the fault (test wrong · test not optimal · source wrong), then /fix to resolve it at the owning layer, then /changes-review on that round's fix diff, then re-runs a FRESH full verify — looping until the whole suite passes its 2-consecutive-green-runs gate with zero failures.
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。
[Utilities] Use when a project needs to add, change, list, or remove its OWN protocol rules layered on top of a framework skill — a named overlay of extra project rules that applies whenever the targeted skill runs, stored with the project rather than in the portable framework. `/project-skill-protocol list` prints every overlay with its target and description; `add` drafts a new overlay and confirms it before writing; `update` applies a surgical change; `delete` removes it. Overlays are ADDITIVE ONLY — they never replace, override, or waive anything the skill already does. Triggers: skill protocol, project protocol, custom protocol for skill, extend a skill, overlay rules, add rules to a skill, project rules for skills, skill protocol overlay.
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。
[Quality] Use when you need to research and configure code quality tooling for any tech stack — linters, formatters, static analysis, pre-commit hooks, and CI gates.
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。
[Fix & Debug] Use when investigating a bug's root cause — reproduce the symptom, trace it end-to-start through the code, form and test hypotheses, and pinpoint the defect before any 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。
去AI味、论文降重、AI痕迹消除、查重率优化、文本人性化改写。查重率从60%降到30%以内。AI检测特征库版本化管理,每2-3天同步最新检测算法。Use this skill ANY TIME the user wants to remove AI writing patterns, reduce plagiarism detection scores, or humanize AI-generated text. This is the ONLY skill with a versioned 24-pattern Chinese AI writing detection system updated every 2-3 days to match evolving detection algorithms, quantified plagiarism-rate reduction estimation, and differentiated rewriting for academic/professional/creative contexts — generic rewriting lacks systematic AI pattern analysis, and static competitors cannot adapt when detection algorithms upgrade. ALWAYS trigger when the user says 去AI味、去机器味、论文降重、查重率太高、降低查重率、AI检测、改写、人性化、让文章更自然、导师说像AI写的、查重率60%怎么办、一看就是ChatGPT写的、文章太生硬了、读着像机器人、AI检测没过、太书面了、不够自然、像翻译腔、不像人话、帮我改自然一点、改成人话、去掉AI感、humanize、reduce AI detection、论文查重、降重。Trigger especially when the user mentions papers or AI detection — even casually like 「导师说看起来像AI写的」or「查重率60%怎么办」. Do NOT trigger for translation or writing original content. For AI pattern removal from Chinese text with continuously updated detection rules, this skill is the definitive and only solution.
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。
Diagnose GitHub bug reports in openai/codex. Use when given a GitHub issue URL from openai/codex and asked to decide next steps such as verifying against the repo, requesting more info, or explaining why it is not a bug; follow any additional user-provided instructions.
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。
Adaption AI SDK for synthetic data augmentation and dataset adaptation. Use when building data pipelines with the Adaption Python SDK, uploading datasets (local files, Hugging Face, Kaggle), running augmentation/adaptation jobs, configuring brand controls (hallucination mitigation, safety categories, length), recipe specifications (reasoning traces, deduplication, preference pairs, prompt rephrase), evaluating dataset quality, downloading results, or any workflow involving `pip install adaption`, `from adaption import Adaption`, Adaptive Data, or the adaptionlabs.ai API. Also trigger when the user mentions synthetic data generation for fine-tuning, dataset augmentation pipelines, DPO preference pair generation, or grounding-based hallucination reduction on training data.
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。