「cleanup」の検索結果

Works through the surviving mutants that cargo-mutants reports for packages/engine — classify each one as a real test gap, a mutant not worth killing, or dead code, then fix it and verify with `just mutants-diff`. Use when picking up the weekly "Mutatietesten" issue, when asked to improve engine test coverage, or when a pull request fails the Mutation Testing (diff) gate.

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 preparing or publishing a local project as a clean GitHub repository. Covers new repositories, private staging releases, public README cleanup, updates to existing remotes, and research-code packaging without datasets, checkpoints, papers, or generated 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。

Turn meeting notes, transcripts, or rough bullets into decisions, action items, owners, due dates, and follow-up queues. Use when the work is repetitive meeting cleanup or recap writing. Do not use for negotiation strategy, legal advice, or executive decision framing.

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。

Triage email, Slack, ticket, or shared request backlogs into priority buckets, owners, and next actions. Use when the work is repetitive queue handling, intake cleanup, or response sorting. Do not use for strategic decisions, policy interpretation, or substantive contract 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。

Standardize incoming legal or commercial contract requests into request type, missing information, priority, and review queue. Use when the work is intake routing and request cleanup. Do not use for clause interpretation, negotiation strategy, or legal advice on the agreement itself.

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。

Customize a new project created from this Flutter template, including app identity, package name, platform cleanup, icons, splash screen, Firebase/secrets decisions, setup tool execution, code generation, and 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。

Use when considering, evaluating, or performing a refactor, restructure, cross-file rename, or cleanup

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。

Client-side image handling - preview generation, Canvas API resizing, compression, EXIF orientation, format conversion, memory management with object URL cleanup

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。

UI component review patterns. Use when reviewing React components, hooks, props, state, styling, and accessibility. Covers rules of hooks, effect cleanup, render performance, list keys, keyboard and ARIA patterns.

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 adding, reviewing, squashing, deploying, or rolling back Pharos D1 migrations or schema cleanup.

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。

Pick up one bounded Gaia Steward maintenance dispatch, do exactly it, and hand the result back for independent verification. Use when the user says "check the steward lane", "pick up maintenance work", "is there anything for me to fix", "run the steward lane", "/gaia-steward-lane", or when a scheduled routine wakes you with no other instruction. Also use before starting speculative repository cleanup: if Steward has not found the work, the work probably does not exist.

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。

Simplifies and refines code for clarity, consistency, and maintainability while preserving all functionality. Focuses on recently modified code unless instructed otherwise. This skill should be used when code-quality checks pass but the code would benefit from structural cleanup — deduplication, branching simplification, naming improvements, or dead-code removal. Invoked as a subagent from /code-quality or directly via the Task tool.

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。

How to write source comments, doc comments, and test names/tags that have lasting value — and what to never write. Consult before adding or editing comments in core/**, examples/**, tools/**, test/**, docs/**, or any shipped source. Handles "write a comment", "is this comment ok", "comment hygiene", and keeping new code from adding the breadcrumbs the cleanup stream removes.

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。

Structured 6-phase debugging. Build feedback loop first, reproduce deterministically, hypothesize with ranked falsifiable theories, instrument one variable at a time, fix with regression test, cleanup. Use when a bug exists, tests fail unexpectedly, or behavior is wrong and cause is unknown.

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。

State machine patterns for Unity including hierarchical states, async transitions, and cleanup safety.

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 building or reviewing Canvas 2D, Three.js/WebGL, React Three Fiber, GLSL shaders, ShaderToy-to-WebGL adaptation, 2D/3D visualization, game rendering, animation loops, GPU resource cleanup, or rendering performance. Do not use for standard DOM UI, charts already covered by a chart library, or non-graphical performance work; Chinese triggers include Canvas, Three.js, WebGL, GLSL, Shader, 3D, data visualization, games.

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 choosing, implementing, or reviewing browser storage such as localStorage, sessionStorage, IndexedDB, cookies, client persistence, offline data, secure storage, or cleanup strategy; Chinese triggers include browser storage, client persistence.

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。

Marketing analytics and data-driven optimization for B2B SaaS campaigns. Use this skill when analyzing marketing performance, optimizing conversion rates, running A/B tests, conducting customer research, creating marketing dashboards, building attribution models, analyzing funnel metrics, or storytelling with data. Also triggers on: marketing ops, performance analytics, CRO, A/B testing, data storytelling, customer insights, tracking plan, event taxonomy, UTM taxonomy, GA4 audit, consent mode, instrumentation contract, sample size calculation, minimum detectable effect, test duration, not enough traffic to A/B test, statistical significance, Bayesian vs frequentist, sample ratio mismatch, experiment feasibility, martech stack, marketing tech stack audit, tool consolidation, build vs buy, vendor evaluation, shadow IT tools, ESP/CRM/platform migration, switching cost, cutover plan, suppression list migration, data portability, customer interviews, survey design, sampling frame, non-response bias, how many people should I interview, thematic saturation, voice of customer, jobs-to-be-done, buyer persona research, is this finding real, data enrichment, which enrichment provider should we buy, waterfall enrichment, contact data quality, match rate, our list data is bad, email verification, catch-all domain, accept-all, bounce rate from bad data, B2B data decay, how often should we re-verify contacts, ZoomInfo vs Apollo vs Clearbit evaluation, cost per usable record, where did this list come from, GDPR Article 14 notice, purchased list compliance, suppression screening, TAM list building, demand planning, marketing plan, how many leads do we need, what pipeline target should marketing carry, funnel model, top-down vs bottom-up plan, plan of record, annual marketing planning, quarterly planning, pipeline coverage we have to create, sales cycle lag, our leads cannot close this quarter, carry-in pipeline, scenario planning, conservative moderate aggressive forecast, stretch target, reforecast, re-forecast triggers, assumption register, capacity ceiling, addressable audience ceiling, marginal cost per lead, blended CPL is wrong, we missed plan and nobody knows why, AI policy for the marketing team, AI governance, AI acceptable use policy, which AI tools are we allowed to use, can I paste customer data into ChatGPT, shadow AI, AI use register, human review of AI output, when can we stop reviewing every AI draft, can the agent send on its own, the AI made a mistake now what, pull back AI autonomy, AI evals, our AI vendor changed the model, AI output went out wrong, prompt injection, can our AI read prospect replies safely, lethal trifecta, the AI leaked internal notes, turn off auto-generated ad assets, auto-applied recommendations, AI literacy training, EU AI Act Article 4, duplicate records in the CRM, dedupe before import, which value wins in a merge, CRM cleanup.

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 OpenClaw Manager (OCM) safely and correctly for OpenClaw environment lifecycle work, runtime installation/builds, release-shaped local builds, upgrades, migration/adoption, cloning existing user state, service supervision, gateway logs, dev worktrees, plugin testing, model/provider smoke tests, TUI/dashboard checks, cleanup, and concise reporting. Use whenever Codex is asked to use OCM, manage OpenClaw envs/runtimes/services, test OpenClaw with OCM, reproduce existing-user or new-user flows, or avoid breaking user state while working with OpenClaw.

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 a task needs the judgment of a Document Management Specialist — designing or auditing a records retention schedule, planning a content migration between DMS platforms (SharePoint, OpenText, Documentum, M-Files), reconciling migrated vs. source document counts, resolving a version-control or legal-hold conflict, or scoping a defensible-disposal (ROT) cleanup.

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 a task needs the judgment of a Brownfield Redevelopment Specialist and Site Manager — confirming landowner liability protections (BFPP/AAI) before a contaminated-site acquisition closes, matching a cleanup standard to the actual reuse plan instead of defaulting to the most conservative one, sequencing a brownfield funding stack (EPA grants, state VCP, tax credits) by eligibility dependency, or deciding whether an institutional control/deed restriction needs disclosure to a lender or buyer.

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。

Playwright-first strategy for shared DB + shared test accounts. Use when E2E tests collide in QA/staging, need safe parallelism, or require deterministic cleanup without touching baseline data. Covers per-test ownership, namespacing, ID-based teardown, serial shared-state suites, and optional stale-data janitor jobs.

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。

Write, review, and optionally test Bash and Zsh shell scripts following project coding standards. Use for ANY shell scripting task: (1) creating a new Bash or Zsh script, (2) editing, fixing, or reviewing an existing .sh, .bash, or .zsh file, (3) adding error handling, dependency checks, or cleanup traps to shell scripts, (4) implementing API integration or file processing in shell, (5) writing or fixing ShellSpec tests (spec/*_spec.sh), or (6) deciding whether a script should be written in Bash or Zsh. Defaults to Bash; Zsh-specific guidance lives in a reference file that is loaded only when writing Zsh.

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 technical debt analysis across architecture, testing, documentation, and infrastructure. Investigates the codebase, scores findings by impact and effort, and generates a prioritized TECH_DEBT.md remediation plan. Delegates to specialized skills for code quality (scout) and linting (lint-fix). Use when assessing overall project health, planning cleanup sprints, or onboarding to an unfamiliar codebase.

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。