「exit」の検索結果

Busca web e pesquisa usando Perplexity AI. Use quando o usuário disser "pesquisar", "encontrar", "procurar", "perguntar", "pesquisar" ou "qual é o mais recente" para consultas genéricas. NÃO para docs de biblioteca/framework (use Context7) ou perguntas do workspace.

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。

Mitigate incidental code complexity when control flow is tangled, nesting is deep, names are hard to parse, or reasoning requires cross-file hops. Use when a review stalls on readability, you need an analysis-first refactor plan before edits, or you want essential-vs-incidental verdicts, ranked simplification steps, a small structural sketch, and a TRACE assessment (analysis-only; no edits).

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。

Search the live web via Perplexity Search API. Use when you need current documentation, release notes, vendor pages, news, domain-constrained web search, or date/recency filtering. Not for local codebase search or stable docs already in 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。

Builds DRAFT user personas from the research notes, interview summaries, survey free text and support themes the user provides: for each persona the goals, pains, jobs to be done, context of use, quotes exactly as supplied with their participant codes, and the evidence count behind every attribute, with names, demographics and any detail no participant stated left UNKNOWN rather than invented. Use when the user asks to "build personas from these interviews", "who are our users based on this research", "draft user personas", "turn these research notes into personas" or "update our personas with the new interviews". Do not use for theming a batch of customer feedback into counts, use customer-feedback-theme-synthesis instead; for exit interviews, use exit-interview-synthesis; for the requirements those personas imply, use product-requirements-draft. Drafts for human review; never approves, authorises or signs 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。

Synthesises training survey responses and manager notes into a DRAFT training needs report by role: coded needs with record counts per role (n of N), the evidence type behind each count, anonymised quotes cited to record codes, disagreements between what staff report and what managers observe, gaps where a role is under-represented or a required skill has no evidence, and follow-up questions to settle before any course is designed. Never rates an individual, ranks a team or turns a count into a competence verdict. Use when the user asks to "analyse this training survey", "what training do our teams need", "summarise the manager feedback on skills gaps", "build a training needs analysis by role" or "which roles have the biggest skill gaps". Do not use for turning an agreed need into a course, use course-outline-builder instead; for exit interviews, use exit-interview-synthesis. Drafts for human review; never approves, authorises or signs 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。

Synthesises a batch of customer feedback (survey answers, reviews, ticket and chat comments) into a DRAFT themed report: coded themes with record counts and shares, anonymised verbatim quotes cited to record codes, contradictions and unverified claims, and the follow-up questions to answer next. Produces no sentiment score or percentage positive as fact; ratings appear only as distributions with n. Use when the user asks to "theme this feedback", "what are customers saying", "summarise these survey answers", "analyse these reviews", "find the top complaints in these comments" or "code this voice of the customer data". Do not use for exit interviews, use exit-interview-synthesis instead; for sorting a live queue and drafting replies, use ticket-triage-pack; for project retrospectives, use lessons-learned-synthesis. Drafts for human review; never approves, authorises or signs 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。

実装計画・リファクタリング計画・レビュー対応計画・並列作業のタスク分解など、計画を成果物として書き出すときに必ず参照するコミット計画ルール。plan モードでは ExitPlanMode で plan を提示する前に必須、計画ドキュメントや job-graph のタスク分解・エージェントへの指示文でも同様に適用する。計画成果物にコミット計画セクション（論理的に独立した修正単位での分割と Conventional Commits 形式のメッセージ）が無ければ実装に入らない。「実装計画を立てて」「planを立てて」「実行計画を作って」「リファクタリング計画を立てて」「コミット計画を立てて」「タスクを分解して」と依頼される、複数の独立した修正をまとめるか分けるか計画段階で判断する等の場面では、ユーザーが「コミット」に一切言及していなくても必ず発火する。コミットの実施（素材収集・メッセージ確定・git commit 実行）は commit-flow スキルが担う。

ISTQB/JSTQB のテスト計画（test planning）活動を支援するスキル。テスト対象のプロダクトリスク（product risk）を発生可能性×影響度で識別・評価し、それを根拠にテストアプローチ（どこを厚くテストするか）・開始基準（entry criteria）・完了基準（exit criteria）を定めた test-plan.md を作る。「テスト計画を立てて」「テスト戦略を決めたい」「何をどこまでテストすべきか整理して」「リスクベースでテスト範囲を決めたい」と依頼されたときに使う。単発のテスト作成依頼（単にユニットテストを書きたいだけ）は対象外。/test-plan <テスト対象名> で明示的に呼び出されたときのみ使用する。

ISTQB/JSTQB のテスト完了（test completion）活動のうち、テスト実行結果の評価と完了基準（exit criteria）判定を支援するスキル。テスト実行ログ（test-execution-log.md）と test-plan の完了基準を突き合わせ、完了とみなせるかを判定した test-summary-report.md を作る。テスト完了時の最終レポートにも、テスト途中の中間評価にも使える。「テスト結果をまとめて」「完了基準を満たしたか判定して」「テストレポートを書いて」「今どこまでテストが進んだか評価して」と依頼されたときに使う。単発のテスト作成依頼（単にユニットテストを書きたいだけ）は対象外。/test-report <テスト対象名> で明示的に呼び出されたときのみ使用する。

When the user wants to create or optimize popups, modals, overlays, slide-ins, or banners for conversion purposes. Also use when the user mentions "exit intent," "popup conversions," "modal optimization," "lead capture popup," "email popup," "announcement banner," "overlay," "collect emails with a popup," "exit popup," "scroll trigger," "sticky bar," or "notification bar." Use this for any overlay or interrupt-style conversion element. For forms outside of popups, see cro. For general page conversion optimization, see cro.

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。

TDD を主張する実装の RED/GREEN/REFACTOR VERIFY 証跡（tdd-ledger）の妥当性を検査し、フェーズ順序・exitCode・test-cases との対応の欠落や偽装を検知する

Add or define specific fields in a collection's readSchema. Use when fields are missing from materialization field selection, pruned by complexity limit, need type refinement, need to be protected from future inference changes, or need to be required for downstream consumers. Use when user says "missing fields", "pruned fields", "field not showing up", "complexity limit", "define field in read schema", "restrict field type", or "protect field from inference".

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 code for readability, then simplify it to be easy to follow and understand. Use this skill when the user wants to simplify code, improve readability, clean up for handoff, make code skimmable, reduce complexity, refactor for clarity, strip boilerplate, prepare code for future maintainers, or make a codebase easier to pick up. Trigger liberally — any request touching readability, simplification, cleanup, or "make this easier to understand" should use this skill. Also trigger when the user says things like "review this", "clean this up", "tidy this", "make this simpler", "reduce complexity", "flatten this", "strip the noise", or "I want to understand this code quickly next time I open it".

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。

SEO + GEO (AI-search) + brand-entity desk (Busabase App-in-Skill) — wired to Google Search Console for a dashboard over search analytics and an agent-prepared SEO opportunities review queue, plus an AI-visibility tracker (are we cited in ChatGPT / Perplexity / Gemini / Claude / Copilot answers), a GEO content-optimization queue gated by geo-qa, and an entity / knowledge-panel readiness checklist. Use when the user invokes $kelly-seo or /kelly-seo, mentions SEO, Google Search Console, GSC, search analytics, search performance, queries, keywords, rankings, positions, impressions, clicks, CTR, striking-distance queries, title/meta rewrites, content briefs, internal links, SEO opportunities review, GEO, generative engine optimization, AI search, AI visibility, being cited by AI answer engines, ChatGPT/Perplexity/Gemini/Claude/Copilot citations, brand entity, knowledge panel, Wikidata, schema.org, or entity readiness.

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。

Answer Engine Optimization (AEO) skill — optimize content to be cited by AI language models (ChatGPT, Perplexity, Claude, Gemini, Mistral) as authoritative sources. Distinct from SEO — AEO optimizes for citation in LLM-generated responses, not search rankings. Use when planning content for AI-first search audiences, auditing existing content for E-E-A-T signals, tracking which pages get cited by which LLMs, or building a citation-friendly content strategy. Triggers — 'AEO audit', 'optimize for ChatGPT', 'get cited by Perplexity', 'LLM citation strategy', 'answer engine optimization', 'content for AI search', 'E-E-A-T audit'. Output is a markdown audit report (default) or JSON for pipeline integration. Stdlib-only Python tools.

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。

Google Workspace CLI (gws) リファレンス。Rust 製。 `gws <service> <resource> <method>` 形式で Gmail / Drive / Calendar / Sheets / Docs / Chat 等 19 サービスを統一操作。Google Discovery Service 経由の動的コマンド生成。 `+` プレフィックスのヘルパーコマンド（+send, +agenda, +upload, +triage 等）、 ワークフロー合成（+standup-report, +meeting-prep, +weekly-digest）、 OAuth / サービスアカウント / CI ヘッドレス認証、env vars / .env / keyring 設定、 グローバルフラグ（--dry-run, --page-all, --sanitize）、exit codes、Model Armor 統合。

Assesses ChIP-seq quality across antibody specificity, fragmentation, enrichment, replicate concordance, and library complexity. Computes FRiP, NSC/RSC (phantompeakqualtools), library complexity (NRF/PBC1/PBC2), deepTools plotFingerprint (JS distance, AUC, synthetic JS), ChIPQC, IDR with ENCODE Nself/Nt rules, and detects hyper-ChIPable artifacts. Use when validating an antibody, diagnosing failed peak calls, deciding whether to proceed with downstream analysis, grading against ENCODE thresholds, or auditing replicate concordance.

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。

LSF 作业内存不足时的调整技能。当作业因内存不够被挂起(SSUSP)或终止(EXIT/MEMLIMIT)时，通过调整 bsub -n 的节点（核数/slot）数量来扩大可用内存——可用运行内存 ≈ 5GB × 申请节点数。当用户说内存不足、作业被杀、OOM、需要更大内存，或排查 EXIT/SSUSP 作业时，配合 lsf-ncpgr skill 一起使用。

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。

When the user wants to create or optimize popups, modals, overlays, slide-ins, or banners for conversion purposes. Also use when the user mentions "exit intent," "popup conversions," "modal optimization," "lead capture popup," "email popup," "announcement banner," "overlay," "collect emails with a popup," "exit popup," "scroll trigger," "sticky bar," or "notification bar." Use this for any overlay or interrupt-style conversion element. For forms outside of popups, see form-cro. For general page conversion optimization, see page-cro.

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。

Decompose tasks into structured, outcome-defined process entries with complexity triage. Load when user says "decompose this", "break this down", "what steps do I need", "plan this out", "what's the process for", "how do I approach this", or when any complex task needs structured execution planning. Includes a problem-understanding pass before complexity triage. Routes to `problem-to-plan` when the user needs planning deliverables (spec + plan + TODO). Does NOT replace brainstorming — brainstorming is design approval (upstream), this is execution planning (downstream).

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。

Simplify application code for clarity without changing behavior — refactor after tests pass, reduce nesting and duplication, match project conventions. Load when refactoring for readability, cleaning up after a feature ships, or when code review flags complexity. Also triggers on "simplify this code", "code simplification", "make this easier to read", "reduce complexity", "refactor for clarity". Not for compress/split/prune-skill (skill-library files). Pairs with technical-debt-audit.

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。

Assesses ChIP-seq quality across antibody specificity, fragmentation, enrichment, replicate concordance, and library complexity. Computes FRiP, NSC/RSC (phantompeakqualtools), library complexity (NRF/PBC1/PBC2), deepTools plotFingerprint (JS distance, AUC, synthetic JS), ChIPQC, IDR with ENCODE Nself/Nt rules, and detects hyper-ChIPable artifacts. Use when validating an antibody, diagnosing failed peak calls, deciding whether to proceed with downstream analysis, grading against ENCODE thresholds, or auditing replicate concordance.

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。

LSF 作业内存不足时的调整技能。当作业因内存不够被挂起(SSUSP)或终止(EXIT/MEMLIMIT)时，通过调整 bsub -n 的节点（核数/slot）数量来扩大可用内存——可用运行内存 ≈ 5GB × 申请节点数。当用户说内存不足、作业被杀、OOM、需要更大内存，或排查 EXIT/SSUSP 作业时，配合 lsf-ncpgr skill 一起使用。

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。

Optimize content for AI Overviews (formerly SGE), ChatGPT web search, Perplexity, and other AI-powered search experiences. Generative Engine Optimization (GEO) analysis including brand mention signals, AI crawler accessibility, llms.txt compliance, passage-level citability scoring, and platform-specific optimization. Use when user says "AI Overviews", "SGE", "GEO", "AI search", "LLM optimization", "Perplexity", "AI citations", "ChatGPT search", or "AI visibility".

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。