「ttl」の検索結果

Optimizes application performance. Use when performance requirements exist, when you suspect performance regressions, or when Core Web Vitals or load times need improvement. Use when profiling reveals bottlenecks that need fixing.

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。

Expert-level number theory knowledge. Use when working with divisibility, prime numbers, modular arithmetic, congruences, Diophantine equations, cryptography, quadratic residues, or analytic number theory. Also use when the user mentions 'prime', 'divisibility', 'modular arithmetic', 'congruence', 'GCD', 'Euler totient', 'Fermat little theorem', 'Chinese remainder theorem', 'quadratic residue', 'Diophantine equation', 'RSA', or 'prime factorization'.

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。

Measure real frontend performance — Core Web Vitals (LCP, CLS, INP), page-load traces, CPU and network throttling, Lighthouse accessibility/SEO scores, and heap growth — using chrome-devtools-mcp against a live browser. Trigger when asked "is the page fast enough", "measure Core Web Vitals", "why is this route slow", "run Lighthouse", "check for a memory leak", before any performance-gated certification (Built for Shopify, Core Web Vitals thresholds, store listing review), and before any launch or traffic-multiplier event. Measurement only — this skill never drives functional journeys.

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。

Performance audit — bottleneck profiling, N+1 query detection, hot-path analysis; explicit request only, not part of regular feature work.

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Marketing de produto, posicionamento, estratégia GTM e inteligência competitiva. Inclui definição de ICP, metodologia de posicionamento April Dunford, playbooks de lançamento, battlecards competitivas e guias de entrada em mercados internacionais. Use ao desenvolver posicionamento, planejar lançamentos de produtos, criar mensagens, analisar concorrentes, entrar em novos mercados, capacitar vendas ou quando o usuário menciona marketing de produto, posicionamento, GTM, go-to-market, análise competitiva, entrada em mercado ou capacitação de vendas.

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Triage a GitHub issue that already has the cc-issue-created label and is assumed to be ready to start. First inspect the comment history to decide whether human confirmation is needed (cc-need-human-check, highest priority); otherwise decide whether the issue should be closed as not needed, unanswered confirmation items remain (cc-answer-issue-questions), the description is stale relative to settled comment-history content and must be refreshed before execution (cc-update-issue), or it can move to execution (cc-exec-issue). Dependency checks are out of scope.

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Convert human-written CLAUDE.md into AI-native structured-label format. Battle-tested across 4 models. Same rules, fewer tokens, higher compliance.

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 audit-related documents (control design documents, bottleneck analyses, requirements definitions, etc.) for quality, scoring them 0-100 with a severity-rated findings list. Use when reviewing audit documents, checking control design quality, or verifying cross-document consistency. Supports documents governed by US GAAP, IFRS, or J-GAAP.

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Choose the design and the numbers a research run can honestly claim — write the structured METHOD RECORD (databases, date of access, search string, criteria, screening counts) that r0 validates, pick the claim strength a design can actually carry, record a statistic as data rather than prose, route to the reporting guideline the design owes, and decide what to preregister. Use when a run's question, design, search or statistics have to be settled before the writing, or when a claim needs a strength or a number attached to 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。

Knowledge base synthesized from the Guide to the Systems Engineering Body of Knowledge (SEBoK) v2.14 by the BKCASE Project. Use for the established systems-engineering canon: SE fundamentals and foundations (systems science, systems thinking, the systems approach, modelling/MBSE), the life cycle (ISO/IEC/IEEE 15288/24748/24774 process, development & agile approaches, model selection/tailoring), technical management, concept definition, architecture/realization (the Vee), V&V, maintenance/sustainment, SE standards, and the application contexts (product, service, enterprise, systems-of-systems, healthcare), plus enabling SE (business/team/individual), related disciplines, quality attributes/specialty engineering, implementation examples, and emerging knowledge. Good for grounding background, vocabulary, and prior-art positions across all eight SEBoK Parts. SCOPE LIMITS: this pack is SEBoK v2.14 only (released 2026-05-18); it synthesizes and signposts rather than reproducing the source — it does not reproduce ISO/IEC/IEEE standard text, is a curated guide (not a complete textbook), and is thin on AI-integration depth (Part 8 is a staging area, not settled practice). LICENCE: content is CC BY-NC-SA 3.0 — non-commercial use only, attribution to the BKCASE Project required, share-alike obligations carry forward.

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。

Prepares a DRAFT data incident impact brief from the incident notes and data inventory the user provides: the timeline as stated, systems involved matched to the inventory, data categories held and evidenced as involved, people and record counts potentially affected with the source and method behind every figure, third parties, notification questions for legal and the data protection officer, and every UNKNOWN with the evidence that would settle it. No legal determination: it never states whether the incident is a reportable breach, whether notice is due, to whom or by when. Use when the user asks to "prepare the impact brief for this incident", "what data was involved", "how many people are affected", "map the incident to our data inventory" or "pull together what legal needs on the incident". Do not use for the blameless postmortem or root-cause timeline, use incident-postmortem-drafter instead. Drafts for human review; never approves, authorises or signs off.

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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。

Compares a changed HR policy with the version it replaces and drafts a clause-by-clause change log, a plain-language DRAFT employee briefing, a manager FAQ answered only from the policy text with clause references, and a list of open questions the people team must settle before release. Use when the user asks to explain, announce, summarise or brief a policy change, a new policy version or an updated handbook section, or says "what changed in the leave policy", "brief managers on the new handbook section" or "compare the old and new versions of this policy". Do not use for a single announcement with no version comparison, use announcement-drafter instead; for an FAQ from documents that are not a policy change, use faq-builder; for contract clauses, use clause-comparison-table. 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。

Turns one decision discussion (meeting transcript, chat thread, email chain or notes) into a DRAFT one-page decision memo: the decision as stated, status, options considered, evidence cited, dissent recorded verbatim, owner, effective date and review date, with UNKNOWN for anything the discussion did not settle. Use when the user asks to "write up the decision", "record what we decided", "capture the decision from this thread", "draft the decision memo" or "add this to the decision log". Do not use for full minutes or action items, use transcript-to-actions instead; for a technical design decision, use architecture-decision-record; for a paper seeking a decision not yet taken, use board-paper-skeleton. 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。

Explain a stack trace or error message and suggest concrete 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。

Write a clear, conventional commit message from the staged diff.

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。

A minimal example skill. Greets the user and demonstrates the SKILL.md format.

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。

Short alias for /llms: settle the current question through a targeted, bounded consensus of other LLMs. Use when you want outside models to agree or disagree with a plan before acting. All logic lives in the llms skill. Triggers: /LLLL, llll.

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。

Decide whether to reuse, improve, or scaffold a deterministic helper. Use when repeated work is slow, brittle, or blocked by a missing local 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。

AI PR intake and multi-agent governance for deployless repositories. Use when a repo has high-volume bot or agent PRs, parallel agent work, noisy low-context PRs, merge-queue pressure, CI exhaustion, duplicate fixes, ownership confusion, or review bottlenecks that threaten a single-mainline delivery model.

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Picks the right WordPress storage primitive for plugin data: options, user/post/term/comment meta, transients, site options, site transients, or custom tables. Covers grouped settings, autoload management, transient TTL rules, serialized/JSON blob trade-offs, multisite storage caveats, and naming conventions. Use when scaffolding settings, choosing persistence for plugin-owned data, or auditing update_option/get_option/get_post_meta/set_transient/autoload usage.

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。

Implement bounded, idempotency-aware API throttling, backoff, and retry handling for 429 and transient 5xx responses.

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。

Convert human-written CLAUDE.md into AI-native structured-label format. Battle-tested across 4 models. Same rules, fewer tokens, higher compliance.

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。

Create sales enablement systems including one-pagers, decks, battlecards, objection handling, demo narratives and proof assets.

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。