1万 件 ・ 関連度順
概要と使いどころ
Claude-native review of code for quality, structure, naming, and maintainability (no external AI). Use when reviewing a file or diff for code quality before committing. Trigger on "review this code", "code review", "is this well-written". For security, use /security-verify; for companion-AI review, use /review.
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 an existing codebase in parallel via 7 specialized mapper subagents. Outputs structured findings to docs/codebase/ for persistent cross-session context. Use when onboarding to a brownfield project or when reactive on-demand file reading is too slow.
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。
Searches for Memorie van Toelichting (explanatory memoranda) for a Dutch law and generates Gherkin test scenarios from legislature-intended examples. Use this skill proactively when: user wants MvT-derived BDD scenarios, mentions "memorie van toelichting", "MvT", "parlementaire stukken", "rekenvoorbeelden", or "kamerstukken" in the context of Dutch law. Activate automatically when a new law YAML file is created and the user discusses testing or validation.
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。
Maakt regelrecht-scenario's vindbaar en hun norm-ketens leesbaar/geassert. Aan de invoerkant — vertaal een platte lijst leaf-parameters naar benoemde casus-assen + een herbruikbare persona-bibliotheek, met een reverse-lookup casus-matrix. Aan de uitvoerkant — maak de norm-keten zichtbaar via keten-checkpoints (assert elke knoop op het kritieke pad, niet alleen de endpoint) en golden-trace-snapshots + branch-coverage. Gebruik dit bij het schrijven of opschonen van scenario-/feature-files, het terugvinden of ontwerpen van een specifieke casus in een grote variabelenlijst, het reviewen van engine-traces, of het regressie-bestendig maken van cross-law-ketens zodat een ketenfout niet stil achter een correcte endpoint verdwijnt. Dossier-agnostisch; de regelrecht-methode is de vaste taal. Haakt aan op regelrecht-stelselanalyse (engine-tests / meta-check) en regelrecht-audit-products (testcase-scenario's).
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。
Detects version drift between machine-readable law YAML files and the binding geldende wettekst on wetten.overheid.nl at the YAML's claimed valid_from. Catches the silent failure where authored content lags behind ingevoerde wijzigingen — a YAML dated 2025-01-01 still carrying pre-Stb.-X text. Activate as Step 0 of every regelrecht-stelselanalyse micro-cycle, before any YAML edit. Strict by design: no bypass, no normalization beyond whitespace within a single paragraph; the geldende wettekst is leading and is mirrored verbatim, including errata. Use proactively when starting a stelselanalyse cycle, before harvest/migrate/extend, or when user mentions "drift", "versie", "geldende tekst", or worries that a YAML may be stale relative to wetten.overheid.nl.
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。
Two-layer memory system with Dream-managed knowledge files.
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。
Summarize or extract text/transcripts from URLs, podcasts, and local files (great fallback for “transcribe this YouTube/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。
Interview the user to surface experience that is real but undocumented, mapped against a specific role's requirements, then update the profile. Use when a fit assessment or an adversarial review flags a section as thin.
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。
Explain what slushpile is, what each skill does, what order to run them in, where the workspace files live, and how to set up a voice agent. Use when the user asks how slushpile works, what to run next, or how to fix a workspace problem.
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。
An adversarial job search that gets better as it goes. Set up a job-search workspace, search a careers board and score roles against the realistic applicant pool, build a resume and cover letter, review them with seven agents that return a verdict per submission channel, then write what the review finds back into the profile. Invoke with /slushpile and name what you want to do.
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。
Set up a slushpile workspace. Ingests an existing resume or LinkedIn export, interviews the user to fill the gaps, then writes profile.md, preferences.yaml, and stories.md. Run once per workspace, before any other slushpile 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。
Search a named company's careers board, or resolve a natural-language query into a company list and search each. Extract job descriptions, score pool-anchored and channel-conditional fit against the user's profile, run a contrarian gate before tiers are finalized, and create role folders with assessments. Updates the job search tracker.
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。
Optimizes Atlassian Forge apps to reduce platform consumption and avoid unnecessary costs using Atlassian's "Optimise Forge platform costs" guidance. Use when the user asks to optimize Forge app costs, reduce Forge invocations, lower GB-seconds, reduce storage or log usage, tune memory, replace polling, improve scheduled triggers, reduce KVS writes, move work to the frontend, use bridge APIs, batch API calls, add caching, or evaluate Forge Remote trade-offs. By default, perform an audit first and offer to make the recommended changes after presenting the audit. Only modify files immediately when the user explicitly asks the agent to implement or apply optimizations.
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。
Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn/ui). Use for complex artifacts requiring state management, routing, or shadcn/ui components - not for simple single-file HTML/JSX artifacts.
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。
Presentation creation, editing, and analysis. When Claude needs to work with presentations (.pptx files) for: (1) Creating new presentations, (2) Modifying or editing content, (3) Working with layouts, (4) Adding comments or speaker notes, or any other presentation tasks
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 document creation, editing, and analysis with support for tracked changes, comments, formatting preservation, and text extraction. When Claude needs to work with professional documents (.docx files) for: (1) Creating new documents, (2) Modifying or editing content, (3) Working with tracked changes, (4) Adding comments, or any other document tasks
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。
Build competitive intelligence briefs following the CI cycle — Plan, Collect, Analyze, Disseminate. Profile competitors, create feature comparison matrices, analyze pricing, build positioning maps, and identify win/loss patterns. Use when preparing for product launches, sales enablement, or strategic reviews.
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。
Compare policies, procedures, onboarding files, or operating evidence against a named compliance framework such as AML/KYC, privacy, internal controls, or audit readiness. Use when a team needs a gap matrix, control checklist, remediation priorities, or evidence request list. Do not use when no target framework is specified or when formal legal or regulatory sign-off is required.
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。
Upgrade Prisma Next in your app. Bumps every `@prisma-next/*` dependency from the version pinned in the lockfile to the requested target (or npm `latest`), applies any required code-translation steps from the per-transition upgrade instructions, validates with the project's own typecheck + tests, and commits each minor step on its own. Use when the user asks to "upgrade Prisma Next", "bump Prisma Next", "move to Prisma Next X.Y", or asks an agent to deal with an `@prisma-next/*` minor bump in their app.
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。
Review computational protein or gene function predictions and fill PredictionReview YAML files using the COR/CNN/LSP/UNC/PLI/NPI/REP biological-validity taxonomy. Use for ProtNLM, DeepECTF, BioReason/GO-GPT, InterPro2GO, PANTHER/IBA, CLEAN, GloEC, MAPred, ProteinInfer, or other predicted EC/GO annotations, including audits of the evidence and reasoning in existing prediction reviews.
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 agent when you need to systematically review existing GO annotations for a gene and make curation decisions based on literature evidence and functional analysis. This agent should be called after seeding the SPECIES/GENE/GENE-ai-review.yaml file, which seeds each annotation with `action: PENDING`; these should all be manually reviewed.
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 agent when you need to create a pathway summary for a gene after completing the main gene review. This agent should be invoked specifically to generate GENE-pathway.md files that summarize the gene's role in biological pathways with proper citations and mermaid diagrams.
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 agent when you need to synthesize and distill the core functions of a gene from multiple information sources (textual summaries, reviewed annotations, bioinformatics analysis) and populate the `core_functions` section of a gene review YAML file. This agent should be called after completing the gene research, annotation review, and bioinformatics analysis phases of a gene review 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。
Task Breakdown Command. Breaks a feature into discrete, dependency-ordered implementation tasks from a PRD and tech spec. Creates a task list and individual task files with no approval prompt. When run inside the `/start-job` pipeline, return control to that orchestrator so it can invoke the implementation phase. When invoked standalone, stop after generating tasks and prompting for the next step. Use when the user says "break this down into tasks", "create tasks", "task breakdown", or after the tech spec is complete and the user wants to plan 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。