「quest」の検索結果

Prepares pull requests by running quality gates, drafting descriptions, and validating tests. Use when completing a feature and ready for review.

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Probe memory/summary clarity via dual anchor questions: task progress, info gaps. Use when verifying session state or summary before handoff or compression.

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。

Guides when to ask clarifying questions versus proceed autonomously. Use to reduce unnecessary clarifying questions when intent is clear.

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Routes substantive ML, AI, data, scientific-computing, and software-engineering requests to the smallest useful set of managed repository skills. Invoke proactively when a request names or implies a package, framework, model family, dataset, modality, workflow, backend, deployment target, evaluation method, or implementation approach that may benefit from repository guidance, even if no repository is named. Narrow progressively from area to family to repository root: inspect only the one or two most likely area pages; compare candidates by capability, task surface, model/data format, training versus inference versus evaluation intent, runtime constraints, and root-skill description; then open only the selected root and relevant sub-skills, references, or scripts. Select multiple repositories only when each adds a distinct capability. Do not load the whole collection, treat dependencies or incidental integrations as capabilities, choose by name alone, or force a match when no exact taxonomy family applies.

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。

CI/CD pipeline attack hunting (GitHub Actions focus) - pwn requests (pull_request_target), script injection, self-hosted runner takeover, cache poisoning, OIDC-to-cloud token theft, poisoned pipeline execution. Wiki-first, FIND schema output.

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Drive responsible disclosure of a proven finding to a CVE. Package the report, find the vendor contact, report privately, coordinate a timeline, request the CVE (vendor CNA / GitHub / MITRE), and publish an advisory. Closes the research loop. Triggers - "disclose", "request a cve", "report this to the vendor".

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Analyzes and improves prompts using 31 frameworks across 7 intent categories. Use when a user wants to improve, rewrite, structure, or engineer a prompt — including requests like "help me write a better prompt", "improve this prompt", "what framework should I use", "make this prompt more effective", or any prompt engineering task. Recommends the right framework based on intent (create, transform, reason, critique, recover, clarify, agentic), asks targeted questions, and delivers a structured, high-quality result.

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。

Recover and review local human-AI conversations from Claude Code, Codex, opencode, Grok Build, and Cursor. Use when the user wants to revisit, search, extract, summarize, or compare work across Agent session stores, time ranges, projects, or named conversations. Follow the user's requested output; question clustering, problem-space mapping, artifact review, drift analysis, and insights are optional lenses.

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Reconstruct and report long-running or multi-turn research, architecture questions, reviews, decisions, completion results, and status as a clear, self-contained brief. Use when a reader must re-enter earlier context, understand the real question and its relationships, distinguish verified facts from judgment and open evidence, or make a decision without reconstructing the work log. Quote the relevant original user wording before substantive analysis, keep one coherent main line, use spatial diagrams when relationships matter, and finish with an explicit decision boundary and practical next step.

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。

Processes GDPR Article 16 right to rectification requests, covering verification of corrected data accuracy, notification to recipients under Article 19, timeline management, and completion of incomplete data. Activate for rectification, correction request, inaccurate data, Art. 16, data correction queries.

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Implements the GDPR Article 17 right to erasure (right to be forgotten) workflow, covering all six grounds for erasure, five exceptions, technical deletion versus anonymization decisions, and third-party notification under Article 19. Activate for erasure request, deletion request, right to be forgotten, Art. 17 queries.

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Handles GDPR Article 18 right to restriction of processing requests, covering the four grounds for restriction (accuracy contest, unlawful processing, erasure opposition, legitimate interest pending), technical flagging mechanisms, and lifting procedures. Activate for restriction request, Art. 18, processing freeze queries.

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。

Guides cooperation with GDPR supervisory authorities under Article 31, including procedures for responding to investigations, information requests, and on-site inspections. Covers controller and processor obligations during supervisory authority interactions. Keywords: supervisory authority, Article 31, cooperation, DPA investigation, information request, inspection.

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Guides comprehensive controller self-assessment covering GDPR Articles 5-49 with scoring methodology and reporting format. Activate when conducting internal reviews or benchmarking maturity. Keywords: self-assessment, controller assessment, compliance questionnaire, scoring.

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Manages California Consumer Privacy Act (CCPA) consumer rights requests under Civil Code sections 1798.100-125, covering the right to know, right to delete, right to opt-out of sale, and non-discrimination. Includes 45-day response window and identity verification requirements. Activate for CCPA, California privacy, right to know, right to delete, opt-out of sale queries.

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Готовит заголовок и описание pull request по изменениям в ветке. Используй, когда просят описать PR, подготовить pull request или написать описание изменений.

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。

Interact with LinkedIn — view your profile, browse other profiles, read the feed, search jobs/posts/people, get job details, create posts, like/unlike posts, comment, send connection requests, and follow/unfollow users. Use this skill whenever the user mentions LinkedIn, wants to search for jobs or people, read their LinkedIn feed, view a profile, get job details, create a post, like or comment on content, send a connection request, or follow someone. Also trigger when the user pastes a LinkedIn URL (e.g. linkedin.com/in/..., linkedin.com/jobs/view/...) or mentions LinkedIn networking.

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。

Designs the contract for an API change inside one codebase — a component's props, a function surface, URL or query parameters, an event payload, or a module boundary — through a discovery pass, an options document with one recommendation, a question round, and an adversarial validation round, with every element of the contract justified from one stated goal. Use when you want to design, shape, decide, or nail down an interface, contract, signature, or API change for a capability you can already describe, sized for roughly one pull request. Produces a design document and changes no code. Does not specify what a feature should do — use plan-a-feature. Does not plan delivery or sequencing — use plan-implementation. Does not assess the architecture of existing code — use architectural-analysis. Does not write the code — use tdd. Does not restructure existing code — use refactor. Runs its rounds without pausing for review; to review each round as it lands, use pairing.

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。

Transcribe audio and video from URLs (YouTube, direct media links) using WhisperKit locally. Optionally analyze transcripts with AI when explicitly requested. Use when users provide URLs to media content and request transcription or speech-to-text conversion.

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。

Applies Judea Pearl's causal reasoning frameworks to distinguish correlation from causation, evaluate AI capabilities, and make counterfactual decisions. Reach for this skill whenever Claude encounters questions about causal inference, structural causal models, the limitations of deep learning, AGI, experimental design, covariate selection, or personalized decision-making. Trigger this skill for topics involving Bayesian networks, the do-calculus, the Ladder of Causation, or when a user tries to answer 'what if' or 'why' questions using purely observational data. Pearl's principles are essential for moving beyond probability calculus into true causal understanding.

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。

Fix iOS AVPlayer "CoreMediaErrorDomain error -12939 - byte range length mismatch" or similar video streaming failures when a proxy/edge/CDN service sits between the client and object storage (GCS, S3, R2). Use when: (1) iOS video fails with -12939 "byte range length mismatch - should be length 2 is length N", (2) curl with Range: bytes=0-1 returns full file instead of 2 bytes, (3) one URL path works with Range but another path to the same storage doesn't, (4) proxy advertises Accept-Ranges: bytes but returns 200 with full file for range requests. The root cause is typically a proxy handler that constructs a new request to the backend without forwarding the client's Range header.

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Fix misleading HTTP response header values when debugging with curl -I or curl -sI. Use when: (1) Response headers differ between curl testing and actual browser/client behavior, (2) Cache-Control or other headers show unexpected values despite correct middleware code, (3) Server-side middleware that only applies to GET requests appears to not work when testing with curl -I. The -I flag sends HEAD requests, and middleware that checks for GET method will skip processing, returning handler-level headers instead of middleware-overridden ones.

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。

Debug Fastly Compute deployments that appear successful but return stale/wrong responses. Use when: (1) fastly compute publish succeeds but version check shows old version, (2) new endpoints return 404 after deployment, (3) cache-busted requests work but regular requests fail, (4) fastly domain list doesn't show your custom domain. Covers edge propagation timing, cached error responses, and domain management API differences.

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Assess an openai-guardrails-python GitHub issue or pull request as a maintainer. Use to verify the claimed need and practical impact, compare supported alternatives or competing approaches, separate code quality from repository readiness, recommend the maintainer action, and draft a copy-ready comment when evidence, changes, or closure should be requested.

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。