「action」の検索結果

Use when adding or fixing Formax tests (especially Ink UI tests) and you need deterministic synchronization in test interactions.

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。

Pull metrics from analytics dashboards and internal web tools with Firecrawl browser. Use when the user needs dashboard reporting, cross-platform metric summaries, authenticated analytics extraction, date-range reports, or structured metrics from web dashboards.

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。

Monitor competitor pricing, features, changelogs, dashboards, and product changes with Firecrawl. Use for recurring competitive intelligence, pricing tier extraction, feature change tracking, or structured competitor alerts.

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。

Ingest public or authenticated knowledge bases and docs portals with Firecrawl browser. Use for JS-heavy docs, login-gated portals, paginated help centers, support knowledge bases, or structured JSON/markdown extraction from documentation sites.

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。

Cross-agent self-inspection of your AI-agent stack. Audits skills, MCP servers, hooks, plugins, commands, credentials, and memory files across Claude Code, Codex, OpenClaw, and NanoClaw. Produces a structured inventory and narrative briefing with cross-ecosystem risk analysis. Use when the user asks to audit their own setup, check what they have installed, review their agent stack security posture, or understand cross-tool interactions. Use when a user has accumulated skills/plugins/MCP servers over time and wants visibility into their attack surface. Use after installing new skills or plugins. Do NOT use for vetting external code before install (that is repo-forensics). Do NOT use for incident response during active attacks. Do NOT use for fixing or patching vulnerabilities (forensify is read-only).

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。

Core Plain Language standard (ISO 24495-1:2023). Governs all user-facing responses to ensure clear, structured, findable, understandable, and actionable output.

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。

AI SDK for Python (the `ai` package). Use for Python model calls (LLMs, image, video, speech, embedding, transcription, reranking, or evaluation), agents, model interaction tests, tool calling, subagents, approvals, durable execution, telemetry, AI SDK UI backends, and custom providers.

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Review Java code for bugs, duplicate code, correctness risks, maintainability improvements, and missing tests. By default review files modified in git; when the user explicitly names files, classes, packages, or a diff, review that scope instead. Generate a detailed review.md report with actionable comments and 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。

Use this skill when asked to perform any of the following actions in a Java project: - To add jspecify support - To prevent NullPointerExceptions - To better handle Nullability This skill will add jspecify dependency, configure Maven or Gradle build to automatically use jspecify for checking Nullability issues.

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。

Define, track, and act on QA metrics: test coverage percentage, flakiness rate, defect escape rate, MTTR, test execution time trends, automation ROI, quality gates, and SLAs for test suites. Includes metric formulas, realistic targets by company stage, and the action to take when each metric goes red. Use when: "QA metrics," "test metrics," "quality KPIs," "test health," "flakiness rate," "defect escape rate." Not for: building the dashboard UI (Allure/Grafana) — use qa-dashboard; measuring coverage gaps and mutation score — use coverage-analysis. Related: qa-dashboard, coverage-analysis, ci-cd-integration, release-readiness, quality-postmortem.

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 escaped defects and test suite health through blameless postmortems. Covers bug pattern analysis, test suite health reviews, 5 Whys root cause analysis, process improvement cycles, and postmortem/retro meeting templates with action item tracking. Use when: "QA retro," "escaped bugs," "postmortem," "quality incident," "defect analysis," "improvement cycle." Not for: live release go/no-go decisions — use release-readiness. Not for ongoing metric dashboards — use qa-metrics. Not for reviewing existing test code quality — use ai-qa-review. Related: qa-metrics, test-reliability, test-strategy.

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。

Design environment strategy for testing across dev, CI, preview, staging, and production — Docker Compose test infrastructure, multi-stage Dockerfiles, seed-data lifecycle, per-PR preview environments, production parity, and external-dependency stubbing at the HTTP boundary. Use when: "set up test environment," "docker-compose for tests," "per-PR preview environment," "staging parity," "spin up test infra," "environment tiers." Not for: choosing mock-vs-stub-vs-fake per dependency — use service-virtualization; factory and fixture data patterns — use test-data-management; pipeline/Actions config — use ci-cd-integration. Related: test-data-management, ci-cd-integration, contract-testing, service-virtualization.

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。

Runtime per-test healing with evidence: multi-attribute selector healing, environment-aware diagnosis, flake classification, quarantine management, and confidence-scored auto-repair. Goes beyond simple locator fallbacks to cover action-level healing, data healing, and observable repair workflows. Use when: "flaky test," "test stability," "self-healing locator," "broken locator recovery," "unreliable test," "quarantine flaky test." Not for: bulk regenerating selectors after a planned UI refactor — use selector-drift-recovery (this skill heals ONE test at runtime; that one rewrites many tests offline). Not for: classifying/clustering CI failures into bug reports — use ai-bug-triage. Related: playwright-automation, selector-drift-recovery, ci-cd-integration, qa-metrics, ai-bug-triage.

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。

Produce a multi-quarter QA strategy document. Covers scope, risk-based prioritization, test levels (unit/integration/E2E), pyramid analysis, entry/exit criteria, quality KPIs, tool selection rationale, CI scaling levers, and timeline planning. Output is an actionable strategy document, not a shelf document. Use when: "test strategy," "QA strategy doc," "testing approach," "QA roadmap," "multi-quarter QA direction." Not for: a single-sprint or single-release plan — use test-planning. Not for: identifying which areas carry the most risk — use risk-based-testing first. Related: risk-based-testing, qa-metrics, release-readiness, test-planning, test-reliability.

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 AI to write NEW test code from specs, PRDs, user stories, code diffs, bug reports, or OpenAPI specs. Staged pipeline: requirements extraction → risk analysis → coverage matrix → scenario generation → oracle design → test code → human review, with guardrails against hallucinated APIs and weak assertions. Use when: "generate tests from spec," "tests from PRD," "tests from user story," "auto-generate test cases," "AI write tests for me." Not for: testing AI/LLM features in your product — use ai-system-testing. Not for: auditing a pre-existing test suite you did not just generate — use ai-qa-review (Step 7 here only reviews tests THIS pipeline produced). Related: playwright-automation, unit-testing, api-testing, qa-project-context.

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。

Goal-driven E2E testing where a browser agent (Playwright MCP / computer-use) reads a natural-language goal and explores the app via the accessibility tree to assert outcomes — no pre-written script. Covers when intent-driven beats scripted, making agent runs deterministic (pinned model, temperature 0, seeded data, bounded steps, explicit success assertion, snapshot-not-pixel), cost/latency control, the accessibility-tree-first interaction model, CI gating, and graduating a stable run into a scripted Playwright test. Use when: "agentic browser test," "goal-driven browser test," "let an agent explore the app," "natural-language E2E," "browser agent smoke test," "Playwright MCP test." Not for: Writing/maintaining deterministic scripted Playwright tests — that is playwright-automation. Testing your product's OWN LLM features — that is ai-system-testing. Related: playwright-automation, ai-system-testing, exploratory-testing, test-reliability, qa-project-context.

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。

Play Pokémon games via headless emulation. Start a game server, read game state, make strategic decisions, and send actions — all from the terminal.

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。

PM skill for Claude Code, Codex, Cursor, and Windsurf. Diagnoses SaaS metrics, critiques PRDs, plans roadmaps, runs discovery, coaches PM career transitions, pressure-tests AI product decisions, and designs PLG growth strategies. Seven knowledge domains, 12 templates, 40+ frameworks, and an opinionated interaction style that labels assumptions and names tradeoffs.

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。

Release moi-computer to npm — verify locally, hand off to the gated GitHub Actions workflow, then verify the published package. Defaults to a `next` preview; pass `stable` for a `latest` release. Use when the user asks to publish, release, ship a version, or cut a dev preview.

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。

Launches 5 parallel AI agents to produce a comprehensive property analysis with composite Property Score (0-100), investment grade, and actionable recommendations

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 PR comments from GitHub. Fetches inline review comments and issue comments, classifies by blocking vs non-blocking, and summarizes actionable items.

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。

Discovery, download, and configuration hub for the entire aelf agent skill ecosystem. Use when the user wants to: (1) explore or list available aelf skills, (2) install/bootstrap aelf skills into Cursor, Claude Desktop, OpenClaw, Codex, or Claude Code, (3) route an aelf-related intent to the correct skill, (4) check health or audit installed skills, (5) onboard a new skill into the aelf ecosystem. Covers domains: Portkey CA wallet, Portkey EOA wallet, Awaken DEX, eForest NFT marketplace, AelfScan explorer, TomorrowDAO governance, aelf node interaction.

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。

Hotwire Turbo for Symfony UX -- SPA-like speed with zero JavaScript. Covers Drive (navigation), Frames (partial page sections), and Streams (multi-target updates). Use when building ajax navigation, lazy-loaded sections, inline editing, pagination without reload, modals from the server, flash messages via streams, or real-time updates via Mercure/SSE. Code triggers: turbo-frame, turbo-stream, data-turbo-frame, data-turbo, data-turbo-action, turbo-stream-source, TurboStreamResponse, <twig:Turbo:Frame>, <twig:Turbo:Stream:Append>, <twig:Turbo:Stream:Replace>, turbo:before-fetch-request. Also trigger when the user asks "how to update part of the page without reload", "how to make navigation feel like SPA", "how to lazy-load a section", "how to do inline editing", "how to push real-time updates from server", "how to use Mercure with Turbo". Do NOT trigger for client-side JS behavior (use stimulus), server-rendered reactive components (use live-component), or reusable static UI (use twig-component).

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 and assess every issue in the TMDb project board's Backlog column — re-verify each against current main with a read-only fan-out, close the mechanically dead, promote the actionable to Ready with priority, size and execution order, and leave the rest in Backlog naming the decision they need. Use to groom the backlog, before planning a release, or on a schedule. Runs headless: it decides for itself within the rules below.

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。