「que」の検索結果

This skill should be used when the user asks to "write a query letter", "query", "querying", "pitch", "blurb", "back cover copy", "jacket copy", "comp titles", "comparable titles", "synopsis for agents", "submit to agents", "submission tracker", "self-publishing description", "retailer description", "ready to query", "submission readiness check", "submit a short story", "magazine submission", "short-fiction markets", "simultaneous submission", "reprint rights", "order a short story collection", or wants to prepare and track a finished manuscript's submission to agents, publishers, magazines, anthologies, or retailers. NOT for pitching a series of several books (use series-continuity), or self-publishing production, metadata, or book rights deals (use publishing). The blurb, retailer description, and the rights a short-fiction market buys, such as reprint rights, stay here.

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Guides AI agents through the complete GDPR Data Subject Access Request (DSAR) workflow under Article 15, including identity verification, 30-day deadline calculation with extensions, response formatting, exemptions, and fee provisions. Activate when handling DSAR, access request, subject access, Art. 15, or SAR queries.

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Manages Data Subject Access Request procedures for employee requests under Art. 15 GDPR. Covers scope of disclosable HR records, emails, CCTV footage, performance reviews, monitoring data, and training records. Implements third-party data redaction, legal professional privilege, exemptions for ongoing proceedings, and the one-month response timeline. Keywords: DSAR, subject access request, Art. 15, employee records, redaction, privilege, HR data, SAR.

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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.

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Fix "count shows X but list shows empty" bugs when querying related events (comments, reactions, zaps, reposts) for Nostr addressable events (Kind 30000-39999). Use when: (1) REST API shows engagement count > 0 but WebSocket query returns empty, (2) Comments/reactions exist but app shows "No comments yet" or similar, (3) Working with NIP-22 comments, NIP-25 reactions, or NIP-18 reposts on Kind 34235/34236 video events or other addressable events. Root cause: clients may reference addressable events using either E tag (event ID) or A tag (kind:pubkey:d-tag format), and querying only one tag misses events tagged with the other.

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Fix "DownloadObjectRequest not found" error in google-cloud-storage Rust crate. Use when: (1) Trying to download objects from GCS using the Rust SDK, (2) Looking for a download request type in http::objects::download module, (3) Compile error about missing type. The download_object method uses GetObjectRequest from the get module, not a separate DownloadObjectRequest type.

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Fix silent failures in Fastly Compute@Edge when using send_async for fire-and-forget requests. Use when: (1) Background tasks (migrations, webhooks, notifications) triggered from Compute never complete, (2) send_async PendingRequest is immediately dropped, (3) Fire-and-forget pattern works locally but fails in production, (4) An async trigger "logs as triggered" but the target service never receives the request (especially after renaming backend constants — the URL hostname is cosmetic, the backend name determines routing, so a global rename can silently reroute calls to the wrong service). Also covers silent backend-not-found errors when FALLBACK_BACKENDS or other backend constants reference names not configured in the Fastly dashboard.

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Write correct, performant SQL across all major data warehouse dialects (Snowflake, BigQuery, Databricks, PostgreSQL, etc.). Use when writing queries, optimizing slow SQL, translating between dialects, or building complex analytical queries with CTEs, window functions, or aggregations.

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Converges an underspecified request into a typed IntentSpec before planning or acting. Use when the user asks to implement, add, change, refactor, fix, migrate, configure, handle, triage, sort out, look into or decide something and the request leaves decisions open (which objects, which approach, what happens on failure, which trade-off), contradicts itself, admits two readings, or rests on an approach its sources may rule out — in a codebase, a ticket queue, a research brief or a runbook — or when the user says "clarify the intent", "what do you need from me", or invokes intent-router. Looks up what its sources hold (code, history, decision records, a ticket log, an order record, the policy in force) before asking; asks only preference or irreversible questions, one at a time, with a recommended default; halts instead of guessing; checks the delivered work against the spec. Silent on a fully stated task its sources do not contradict. Not for explaining existing state ("what does X do", "why is Y slow").

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Grounded in first principles, rigorously examine and refine a user's plan, task, decision, goal, strategy, proposal, or idea through structured, progressively deeper questioning, in order to bridge the gap between the User and the Agent. Use when the user explicitly requests grilling, challenge, pressure-testing, cross-examination, red-team review, pre-mortem analysis, or a decision audit. The goal is to uncover unclear objectives, hidden assumptions, contradictions, weak evidence, missing information, overlooked constraints, dependencies, risks, trade-offs, failure modes, and misalignment between intended outcomes and likely real-world results. Begin by establishing a shared understanding of the user's actual intent, goals, constraints, and success criteria. Ask focused, high-leverage questions rather than broad or repetitive ones. Adapt each question based on previous answers, probing deeper where uncertainty, unsupported assumptions, or strategic weaknesses remain. Regularly summarize the current understanding of the user's intent and position to confirm alignment, expose misunderstandings, and refine the problem or goal definition. Distinguish facts, assumptions, hypotheses, and unknowns. Challenge reasoning rigorously while remaining constructive, respectful, and solution-oriented. Continue until the reasoning is internally coherent, evidence-aware, constraint-conscious, risk-assessed, and translated into a clearer, more actionable goal, decision, or plan. Ultimately, achieving the super-alignment.

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Writes and executes SQL queries from simple SELECTs to complex multi-table JOINs, aggregations, and subqueries. Use when the user asks to query a database, write SQL, run a SELECT statement, retrieve data, filter records, or generate reports from database tables.

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Infer integer allele-specific copy number, tumor purity, and ploidy from tumor sequencing by jointly modeling read depth (logR) and B-allele frequency (BAF) with ASCAT, Sequenza, FACETS, PURPLE, and PureCN (tumor-only). Covers the purity-ploidy identifiability problem, the diploid-baseline (dipLogR) anchor, major/minor copy number, loss of heterozygosity, sunrise/contour fit diagnostics, and reconciliation of conflicting fits. Use when tumor analysis needs absolute copy number rather than relative log2, when estimating purity and ploidy, calling LOH or copy-neutral LOH, resolving whole-genome doubling, running tumor-only allele-specific calling, or choosing among ASCAT, Sequenza, FACETS, and PureCN.

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Query UniProt protein database. Use when user asks about protein sequences, functions, annotations, domains, or protein identifiers. Triggers on "uniprot", "protein function", "protein sequence", "gene product", "protein info".

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Analyze DNA/RNA/protein sequences. Use when the user provides a sequence and asks for analysis, translation, GC content, ORFs, motifs, restriction sites, or primer design. Triggers on "sequence", "translate", "GC content", "ORF", "primer", "restriction", "complement", "reverse complement".

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Access UniProt for protein sequence and annotation retrieval. Use this skill when: (1) Looking up protein sequences by accession, (2) Finding functional annotations, (3) Getting domain boundaries, (4) Finding homologs and variants, (5) Cross-referencing to PDB structures. For structure retrieval, use pdb. For sequence design, use proteinmpnn.

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ESM2 protein language model for embeddings and sequence scoring. Use this skill when: (1) Computing pseudo-log-likelihood (PLL) scores, (2) Getting protein embeddings for clustering, (3) Filtering designs by sequence plausibility, (4) Zero-shot variant effect prediction, (5) Analyzing sequence-function relationships. For structure prediction, use chai or boltz. For QC thresholds, use protein-qc.

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Produce an intensive, cited analytical report: executive summary, multi-angle findings, contrarian views, open questions, and full sources. Use only when the user needs rigorous synthesis of a complex topic (scientific, technical, policy, or market-analytical) that cannot be answered with a short search, and wants a formal written report, not a recommendation list. Do not use for product picks, top-N lists, quick lookups, or routine "find out about X" tasks. If the request does not clearly need this kind of report, do not use this skill. Do not use for a literature review over published papers. This skill collects evidence from the open web. A request for the literature on a biomedical, clinical, life-science, or other scientific topic — papers, studies, trials, preprints — belongs to firecrawl-research-papers, which queries Firecrawl's paper index (PubMed, bioRxiv, medRxiv, arXiv) instead of searching websites.

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Designs automated email sequences end to end: the trigger, branches and exit rules, the full copy of every message, and the sender setup that keeps them out of spam (SPF, DKIM, DMARC, one-click unsubscribe, consent). Covers onboarding and welcome series, lead nurture, abandoned cart, dunning, win-back and sunset flows. Use when someone asks for a "drip campaign", "welcome sequence", "onboarding emails", "nurture sequence", "win-back emails", "lifecycle emails", "email automation", or says "our emails land in spam" or "nobody clicks our onboarding emails". In-app onboarding belongs to onboarding-cro.

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Work with Amazon SQS for reliable message queuing. Create standard and FIFO queues, configure dead-letter queues for failed messages, send and receive messages in batches, and build decoupled event-driven architectures.

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You are an expert in amqplib, the Node.js client for RabbitMQ and AMQP 0-9-1 protocol. You help developers implement reliable message queuing with work queues, pub/sub fanout, topic routing, RPC patterns, dead letter queues, and message acknowledgment — building decoupled microservices that communicate asynchronously through RabbitMQ.

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Graphile Worker expert for high-performance PostgreSQL job queues with trigger-based job creation and millisecond job pickup via LISTEN/NOTIFY. Use when "graphile worker, postgres trigger job, listen notify queue, postgraphile worker, database trigger queue, transactional job, graphile-worker, postgresql, triggers, listen-notify, job-queue, postgraphile, high-performance, supabase" mentioned.

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The art of reviewing code that improves both the codebase and the developer - sharing knowledge, maintaining standards, and building cultureUse when "code review, PR review, pull request, merge request, review comments, LGTM, review feedback, approve PR, request changes, review checklist, code quality, review standards, code-review, pull-request, PR, quality, standards, feedback, collaboration, mentoring" mentioned.

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Ask Questions If Underspecified workflow skill. Use this skill when the user needs Clarify requirements before implementing. Use when serious doubts arise and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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Agent Orchestrator workflow skill. Use this skill when the user needs Meta-skill que orquestra todos os agentes do ecossistema. Scan automatico de skills, match por capacidades, coordenacao de workflows multi-skill e registry management and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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。