4,928 件 ・ 関連度順
概要と使いどころ
Scheduled health sweep that closes the factory loop - reads CI failures, recent commits, dependency and security advisories, and live queue labels, then files issues and writes an immutable run record. Use for the nightly or weekly monitor routine.
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。
Inspect factory health, stale work, CI, security advisories, and recent run evidence without implementing fixes.
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 factory evidence and propose tighter or looser constraints without editing the charter. Use for a periodic constraint 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。
Show the factory control room from live GitHub labels, open PRs, recent run records, gate health, and charter limits.
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。
Classify incoming issues into the live GitHub label queue, reproduce where cheap, and write an auditable QUEUE.md snapshot. Use when triaging a backlog, running the scheduled triage routine, or deciding what the factory should pick up 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。
Turn an ambiguous factory issue into human-approved product, behavior, design, and implementation slices. Use interactively before 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。
Configure TOTP authenticator apps, send OTP codes via email/SMS, manage backup codes, handle trusted devices, and implement 2FA sign-in flows using Better Auth's twoFactor plugin. Use when users need MFA, multi-factor authentication, authenticator setup, or login security with Better Auth.
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 the user wants to simplify, clean up, refactor, tidy, or refine code — their staged/unstaged git changes or a target file/path. Reduces complexity, flattens nesting, removes redundancy and dead code, scores each change by value (holding low-value churn), then runs tests to prove nothing broke. Invoke on: "simplify", "simplify this", "simplify my code/changes", "clean up", "clean this up", "clean up my changes", "refactor this", "make this cleaner", "tidy this up", "reduce complexity", "flatten this", "remove dead code", "make it more readable", "polish before commit", or "absolute simplify". Acts on your working diff; for repo-wide dead code use absolute-prune; for lint/type debt use absolute-debt.
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。
Improve code quality and maintainability through systematic identification of code smells and application of proven refactoring patterns. Use when the user requests refactoring or provides relevant inputs for this workflow.
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。
Think and work like an expert Manufacturing Engineer. Use when a task calls for Manufacturing Engineer judgment. Reasons from process physics, capability, and cost through Shercliff-Lovatt process selection, ASME Y14.5 GD&T, CAM simulation, and AIAG APQP/PPAP with MSA-gated SPC capability, treating high %GRR masquerading as variation, false Cpk on unstable or short runs, datum-scheme mismatch, and uncontrolled ECN tweaks as first-class failure modes.
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。
Think and work like an expert Fire Protection Engineer. Use when a task calls for Fire Protection Engineer judgment. Reasons from NFPA 13 Hazen-Williams hydraulics (K-factor, remote area, hose stream) and NFPA 101 egress (occupant load, travel distance, capacity factors) through NFPA 92 smoke containment/management, ASET/RSET PBD, and FDS/CFAST/CONTAM/PyroSim modeling while treating breached compartmentation, C-factor/fitting errors, and supply-curve shortfall as first-class failure modes.
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。
Think and work like an expert Additive Manufacturing Engineer. Use when a task calls for Additive Manufacturing Engineer judgment. Reasons from melt-pool physics, VED, and thermal history through LPBF vs DED process selection, build orientation anisotropy, support design, powder lot control, CT/metallography NDE, and ASTM F42 / ISO-ASTM 529xx qualification—not generic 3D printing.
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, implement, or review refactor work in an existing codebase with compatibility, security, and verification controls. Use when the user explicitly requests refactor work.
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。
Calculates species richness, diversity, and turnover using the Hill number framework with iNEXT coverage-based rarefaction/extrapolation, asymptotic diversity estimation, and beta diversity partitioning (betapart turnover vs nestedness). Compares assemblages using coverage-standardized rather than size-standardized rarefaction. Use when quantifying biodiversity from species abundance or incidence data, comparing diversity across sites, or constructing rarefaction curves. Not for clinical 16S microbiome alpha/beta diversity (see microbiome/diversity-analysis).
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。
Restructure existing code safely without changing externally observable behavior. Composes context, design, architecture, code quality, and testing guardrails into a characterization-first refactoring workflow. Use when the user says 'refactor this', 'clean this up', 'untangle this module', 'move this to the right layer', 'simplify this code', or 'improve this structure'.
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。
Apply clean code principles when generating or modifying implementation code. Enforces function focus, naming clarity, complexity management, error handling, and self-documenting style. Use when the user mentions 'clean code', 'code quality', 'coding guidelines', or 'implementation quality'. Loaded automatically by the code-generating molecules (code-forge, refactor-safely, bug-fix). This skill governs the craft of writing individual code units -- not architecture (see architecture), not security posture (see secure-coding), not test structure (see test-quality), and not refactoring workflows (see refactor-safely).
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 the user wants a research-paper figure Skill Factory: build, patch, package, or use reusable specialized paper-figure-making skills from lawful literature/corpus evidence. Generated skills must use a specialized-skill-first workflow, full-feasible local PDF coverage where available, startup-plan-only first replies, target-paper candidate/final image isolation, mandatory image-embedded visual-structure explanation, mandatory non-target concept/modeling example display for abstract visual decisions, saved subtype/style illustration atlases, ChatGPT web Create image / ChatGPT Images 2.0 rendering, Codex $imagegen-first rendering, sample-image transfer rules, all-step/current-position state footers, and a mandatory first-round diverse candidate board followed by P6b/P6c paper-local best-practice optimization before final prompt construction.
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。
Bulk-regenerate broken test selectors after a UI refactor or redesign. Detects drift between old and new DOM with an aria-snapshot diff, maps old locators to new equivalents using role-first + region scoping, validates against the new build, and produces a single PR with grouped per-file selector updates and per-change evidence. Assumes Playwright >= 1.50 (trace viewer DOM-snapshot panel, getByRole filtering, ariaSnapshot). Use when: "UI refactor broke tests," "redesign broke tests," "bulk update selectors," "regenerate selectors after refactor," "selector drift," "fix N broken tests after redesign." Not for: healing one flaky test at runtime — use test-reliability. Not for: writing a new test suite from scratch — use playwright-automation. Not for: re-recording tests after a framework switch (Selenium to Playwright) — use test-migration. Related: test-reliability, playwright-automation, test-migration, ci-cd-integration, visual-testing.
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。
Makes working code easier to read and change without altering what it does, and proves the behaviour stayed the same. Use when someone says "simplify this", "clean up this function", "this file is a mess", "reduce the complexity", "refactor for readability", "tidy my diff before the PR", or when a linter reports high cyclomatic complexity or deep nesting. Covers pinning current behaviour with characterization tests, measuring complexity, choosing a named refactoring, applying it in small verified steps, and reporting suspected bugs found on the way instead of fixing them silently.
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。
Causal inference specialist for causal discovery, counterfactual reasoning, and effect estimationUse when "causal inference, causal discovery, counterfactual, intervention effect, confounder, structural causal model, SCM, dowhy, causal graph, causal, dowhy, scm, dag, counterfactual, intervention, causalnex, confounding, ml-memory" mentioned.
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。
Keeping codebases healthy, performant, and maintainable - refactoring, performance optimization, and technical debt managementUse when "refactor, optimize, performance, technical debt, cleanup, architecture, speed up, bundle size, memory leak, slow query, code smell, complexity, dead code, performance, refactoring, optimization, technical-debt, architecture, cleanup, bundle, memory" mentioned.
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。
Systematic code cleanup and refactoring agent that identifies and fixes code quality issues without changing functionality. Focuses on: - Dead code removal - Import organization - Type improvements - Consistent patterns - File organization Use when "User asks to "clean up" code, User mentions "dead code" or "unused", User wants to "organize imports", User asks about "code quality", User wants to "refactor" without changing behavior, Code review identifies cleanup items, After major feature completion, cleanup, refactoring, code-quality, maintenance, organization" mentioned.
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。
Designing tabletop games - from core mechanics to manufacturing, from prototyping to KickstarterUse when "board game, tabletop, card game, worker placement, deck building, area control, playtesting, rulebook, kickstarter game, game balance, asymmetric factions, euro game, ameritrash, party game, dice game, prototype, game publisher, crowdfunding game, board-games, tabletop, game-design, mechanics, playtesting, kickstarter, manufacturing, rulebook, components, balance, player-interaction" mentioned.
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 the deterministic evidence ledger (artifact_manifest.json + claims.json) that every other Anti-Autoresearch auditor reads. One pass inventories artifacts, derives the observability level (L0 PDF-only / L1 +LaTeX / L2 +repo+results) by fixed rule, and extracts span-anchored, hashed, checkable claims (numbers, comparisons, scope, method, baselines, citations, captions, table cells) into claims.json. An OPTIONAL additive cross-model pass ADDS span-anchored semantic claims — method, theorem statements with their assumptions, definitions, proof/derivation steps and equations, scope, baselines, conclusions, the motivation span, and reproducibility-artifact references (the proof, derivation, and structure anchors the family B/D/G auditors need) — it never invents a number, emits a finding, or computes a verdict. Run FIRST, before any audit skill. Triggers: "build the ledger", "extract claims", "prep for integrity audit", "evidence ledger", "建证据账本".
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。