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概要と使いどころ
TanStack Form best practices for type-safe form management, validation, field composition, and submission handling in React. Use when building forms with complex validation, integrating schema libraries (Zod/Valibot/ArkType), composing reusable form components, managing array/dynamic fields, or integrating with meta-frameworks (TanStack Start, Next.js, Remix).
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。
Head-to-Head Candidate Comparison — two candidates side-by-side across skills, experience, culture fit, salary expectations, references, hire-or-no-hire recommendation
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。
Batch Resume Screening — score and rank candidates 0-100 against job requirements, flag red flags (job hopping, gaps, skill mismatches), output Pass/Phone Screen/Skip recommendation per candidate
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。
Deep Single-Candidate Scoring — evaluate one candidate across 5 dimensions (skills match, experience relevance, culture fit signals, growth potential, red flags) with final 0-100 score and hire/no-hire signal
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 AI output validation systems with guardrails. TRIGGERS - Use when user needs help with ai-output-validator related 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。
Validate acceptance criteria and feature completion. Use when checking if features pass, validating test results, verifying acceptance criteria, or determining feature completion status.
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。
Auto-activates when coordinating sequential validation specialists with workflow management, dependency tracking, and overall validation status monitoring.
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。
Orchestrates the full law interpretation pipeline: MvT research, machine_readable generation with validation/BDD testing, and reverse validation. Invokes /law-mvt-research, /law-generate, and /law-reverse-validate sequentially. Use this skill proactively when: user wants to make a law executable, add machine_readable sections, or mentions "interpret" in the context of a Dutch law YAML file. Activate automatically when user opens a corpus YAML file without machine_readable sections and wants to make it executable.
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。
Genereert uit een (semi-)gevalideerd regelrecht-corpus een geloofwaardige service-PoC (burger- + behandelaar-portaal, of een headless beslis-service) en zet die in als validatie-instrument met uitvoeringsexperts — om tastbaar te maken wat nodig is om een wet rechtvaardig in de praktijk te brengen, gezien vanuit de betrokkene. Gebruik dit als workshop-fase ná de logica-/scenariovalidatie (audit-products) om de dienstverlening-kant te valideren: welke service impliceert de wet, waar zit menselijk oordeel, welke last ligt bij de burger, welke termijnen, waar is de wet hard of onduidelijk. Dossier-agnostisch; de regelrecht-methode (hooks, legal_character, untranslatables, source/implements, type_spec, receipts) is de vaste taal. Voor de logica-/scenariovalidatie zelf: zie de zusterskill regelrecht-audit-products.
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。
Execute dynamic instrumentation utilizing Frida to inject custom JavaScript into running iOS applications (IPAs) on jailbroken devices. Hook native functions, bypass SSL Pinning, bypass Jailbreak Detection, and manipulate in-memory data at runtime.
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 validating and serializing Python data with Pydantic v2 - models, field and model validators, constrained and special types, TypeAdapter, discriminated unions, BaseSettings, or FastAPI integration
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。
VeeValidate v4 patterns - useForm, useField, defineField, useFieldArray, schema validation with Composition API
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。
Phase 2B of the Gaia evidence verification pipeline. Verifies benchmark-result rows and benchmark-source candidates against registry/benchmark-sources.json before adversarial audit, link validation, catalog promotion, or ingestion. Use when running /ev-pipeline, checking benchmark-result evidence, reviewing benchmark-source candidates, or preparing benchmark rows for /gaia-ingest-batch.
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。
Skippable Phase 0 of the Gaia evidence verification pipeline. Use only on declared need for higher-quality Stage-2 evidence: a promotion candidate, or a skill that gaia-meta-sweep flagged as under-evidenced. Given a skill id, source repo/context, and needed evidence types (benchmark-result, arxiv, peer-review, social-signal), it runs Firecrawl searches, scrapes top hits to markdown, and appends discovered sources into Phase 1 inputs. Missing benchmark evidence is recorded as benchmark-source candidates first, not direct scoring rows. Trigger phrases: "discover evidence", "find new evidence", "Phase 0", "run ev-discovery", "search for benchmarks/papers", "under-evidenced", "promotion candidate needs stronger proof". This is the ONLY ev-* skill that searches the web for NEW evidence; other ev-* skills aggregate or verify known sources. Requires Firecrawl to be usable (`firecrawl --status` authenticated OR FIRECRAWL_API_KEY set); skips gracefully when neither holds.
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 whether an LLM classifier's validation-gate failure is GOLD-BOUND before spending on prompt revision or model changes. Use when: (1) a scoring pipeline over-predicts a label (precision low, recall high) and a prompt clarification is proposed to tighten it, (2) a pilot/validation gate fails and the fix candidates are prompt edits, (3) inter-rater agreement on the weak label was already low (κ < ~0.6). Core check: if gold POSITIVES share the exact feature the revision would exclude, no prompt can pass a gold-scored gate — recall craters while precision barely moves. Also documents the verified surgical-pilot design (single-section diff, tune/holdout split, pre-registered gate, perturbation check on untouched sections).
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 running existing project validation commands and fixing failures after code changes, including lint, type-check, unit/integration test, build, CI, or local script failures. Do not use when the task is to design or author new component/E2E tests; Chinese triggers include validation, check failures, fix failures, CI 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。
Validate OpenClaw release candidates through OCM using the built OpenClaw artifact, a broad reusable scenario matrix, copied existing-user state, clean new-user state, concise pass/fail reporting, and focused failure notes. Use when asked to test OpenClaw origin/main, release readiness, upgrade behavior, plugins, services, channels, doctor recovery, packaging, or broad release risk.
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。
Discover and characterize regulatory elements (enhancers, promoters, silencers, insulators, super-enhancers) using ENCODE data and the cCRE catalog. Use when the user wants to find candidate regulatory elements, identify active enhancers in a tissue, map promoter states, classify chromatin states with ChromHMM, identify super-enhancers with ROSE, understand the functional validation hierarchy (CRISPR > MPRA > reporter), or characterize non-coding genomic regions. Covers the full workflow from ENCODE cCRE lookup through chromatin state segmentation to functional validation and enhancer-gene linkage. Use this skill for ANY regulatory element discovery, classification, or characterization task.
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。
Validate skills against Anthropic best practices for frontmatter, structure, content, file organization, hooks, MCP, and security (62 rules in 8 categories). Use when creating new skills, updating existing skills, before publishing skills, reviewing skill quality, or when user mentions "validate skill", "check skill", "skill best practices", "skill review", or "lint 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。
Validate plugin content quality before release. Run skill-validator across plugins, verify marketplace.json completeness, check plugin.json version consistency, and detect broken documentation cross-references. Use when preparing a release, before running release.sh, or when user mentions "release check", "pre-release", "plugin-release-checker", or "release 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。
Manage Volcengine's AI-native BaaS platform (Supabase edition / AIDAP / 火山引擎 AI 原生 BaaS 平台 Supabase 版) — a Volcengine-operated distribution that differs from official Supabase. Use when the user asks to create, inspect, or manage Volcengine Supabase or AIDAP resources — workspaces, branches, computes, SQL queries, schema changes, Auth, Realtime, Edge Functions, Storage buckets, frontend static-site hosting (Pages), API keys, connection info, or TypeScript type generation — or when a Volcengine deployment selects AIDAP as its database provider. Also trigger on Chinese prompts mentioning "火山引擎 Supabase" or "火山 Supabase". Operations run through the byted-supabase-cli command-line tool (installed via `npm i -g @byted-supabase/cli`; this is NOT the official `supabase` CLI). Do NOT use it for general database discussions, non-Supabase services (RDS MySQL, Redis), or pure client-side coding unrelated to Supabase backend management.
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。
Construct a compelling job offer. Use when the user says "write an offer letter", "help me make an offer", "how should I structure this offer", "what should we include in the offer", "how do I explain the equity", "the candidate is negotiating", or wants to convert a candidate from interested to accepted - even if they don't explicitly say "offer letter". Also use when a recruiter needs to explain comp philosophy or total compensation to a candidate.
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。
Structure and run a post-interview candidate debrief. Use when the user says "run the debrief", "how do we decide on this candidate", "let's calibrate", "we need to make a hire/no-hire call", "prep the debrief meeting", or wants to structure the decision-making process after interviews are complete - even if they don't explicitly say "debrief". Also use when a recruiter wants to prevent groupthink or HiPPO effect (highest paid person's opinion) from dominating the decision.
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。
Turn Caveman's exact report-only repository observations into an operator-chosen optimization candidate with a paired baseline/candidate evaluation. Use when the user asks to inspect an optimization observation, evaluate a candidate change, or act on the current Caveman optimization report. Require a logged-in Caveman CLI connection and explicit approval; never infer money or actuation from a profile.
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。