「anti」の検索結果

Extrem kurzes, substantielles Ausgabe-FORMAT fuer EINE Entscheidung oder Entscheidungsgruppe, wenn die dahinterstehende Analyse bereits existiert (im Chat entstanden, aus `decide`, aus `decision-briefing`, aus einem Ticket oder Dokument): Kontext in 2-3 Zeilen, Pro/Contra je Option als Stichpunkte im paveman/caveman-reduzierten Stil, Talking Points, eine begruendete Empfehlung, und ein Pfad/Link zur Vollanalyse zum Komplett-Nachlesen. Nutze diesen Skill bei "gib mir das kurz mit Pro Contra und Empfehlung", "executive summary zur Entscheidung", "kurzkontext zu <Thema>", "fass die Entscheidung X in einer Kurzform zusammen", /decision-shot, oder wenn ein Ergebnis an jemanden weitergereicht werden soll, der nur die Essenz braucht, nicht die volle Herleitung. NICHT nutzen, um viele offene Entscheidungen zu SAMMELN und per Buchstabe abzufragen -- das macht decision-briefing; NICHT nutzen, um eine Entscheidung erst zu ANALYSIEREN -- das macht decide. decision-shot setzt eine fertige Analyse voraus und komprimiert nur deren Praesentation.

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。

Erweitert automation-self-care um Letter Hooks, Preflight-Bootloader, Dokument-Traversierungsregeln und selbstheilende Prompt-Kontext- Anreicherung für KI-Agenten und CLIs ohne native, ereignisgetriebene JSON-Lifecycle-Hooks (wie Antigravity / Gemini CLI). Nutzen, wenn ein Agent Preflight-Regeln injizieren, vor Arbeitsbeginn Memory/Gardener durchsuchen, Verzeichnis-Dokument-Lesestrategien erzwingen (CLAUDE.md / AGENTS.md) oder Sidecar-Tasks dynamisch an Skills und Sicherheitsprotokolle routen soll.

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。

Baut und betreibt ein anbieterneutrales Self-Care-Kernset für geplante LLM-Aufgaben und Desktop-App-Automationen. Ergänzt deterministisches Policy-Linting, Lock- und Modell-Governance, semantische Prompt-Prüfung, konsistente Mehrflächen-Updates, Mini-Checks und Regeldatei-Hygiene. Nutzen, wenn ein Agent seinen nativen Scheduler entdecken oder eine bestehende Automations-Flotte mit Rollback, Readback und Löschschutz kontinuierlich verbessern soll. Löst aus bei automation self-care, Scheduler-Task-Pflege, Desktop-App-Automations-Wartung, Automations-Flotten-Audit, selbstheilende Zeitpläne, core-set-textautomations, basic-text-automations, textbased-automation-core, textbased-automation-drivers, textbased-governance-automations oder textbased-desktopapp-automations.

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。

Bindet das model-agnostische Computer-Use-Modul open-compute (Screenshot-Wahrnehmung, Windows-UIA-Elementklicks, sicherheitsgegatete Aktionen) fuer ALLE Agenten des Systems ein -- Claude Code, Codex und agy/Antigravity. Nutzen, wenn ein Dienst eine interaktive GUI- oder Browser-Aktion braucht, die kein reiner Text-/API-Weg loesen kann (Paradebeispiel: Tailscale-Reauth im Browser, sonstige Login-/Consent-Dialoge, Dialogfenster-Klicks). Standard ab 2026-08-02: open-compute statt Aufgeben oder Abwarten nutzen.

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。

Structured 6-step procedure for improving, renovating, or rebuilding existing pipelines, individual project folders, documentation structures, or software stacks. Addressable as "pipeline optimizer" (for whole topic pipelines, e.g. a software, research, or game-dev pipeline) or "project-folder optimizer" (for individual project folders within a pipeline, e.g. a single software tool or paper project). Triggers on tasks like "improve pipeline X", "optimize the stack", "rebuild Y", "renovation", "pipeline refactoring", "clean up project folder", "improve folder structure", "unify conventions", "documentation consolidation", "integrate into existing system", or any substantial intervention in established structures. Delivers building-stock analysis, purpose clarification, ideal sketch, gap plan, empirical pain-point identification, and retests with fresh subagents. Prevents parallel standards, duplication, and pipeline breaks.

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 an evidence map for one document, section or contested claim: every factual claim extracted verbatim and numbered, its supporting passage in the supplied sources or UNKNOWN, the strength of that support as evidenced, contradictions between sources, and the specific question or document that would close each gap, returned in the chat as Markdown tables for the author or reviewer. Use when the user asks to "check what this document actually proves", "map the evidence behind these claims", "which of these statements are supported", "build an evidence map", "what would we need to back this up" or "is this claim substantiated". Do not use for requesting evidence of security controls from control owners, use control-evidence-request-pack instead. 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。

Drafts answers to the questions in a received RFP, RFQ or tender from the organisation's past responses, answer library, case studies, policies and other documents provided: one row per buyer question with a draft answer marked Reused, Adapted or Missing, its source and date, and a legal or pricing review flag on every answer touching terms, liability, warranties, insurance, data protection or price. Use when the user asks to "draft our RFP response", "answer this tender from our past proposals", "pre-fill the bid questions", "first pass at the RFQ answers" or "reuse our previous bid content". Do not use for writing the buyer's own RFP or requirements, use rfp-requirements-pack instead; for security or due diligence questionnaires use vendor-security-questionnaire-prefill; for scoring received supplier responses use rfp-comparison-pack. 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。

Reviews marketing, packaging, web or sustainability text against the environmental-claim rules the user supplies (house policy, regulator guidance extract, industry code) and returns a DRAFT claims review: every environmental claim quoted verbatim with location and family, the evidence the text or evidence pack offers for it with date and scope, the supplied rule passages that bear on each claim, observations as read, and separate question lists for legal and for marketing. No compliance, greenwashing or substantiation verdict. Use when the user asks to "review these green claims", "check this copy against our environmental claims policy", "what evidence do we have for carbon neutral here", "list the sustainability claims in this brochure" or "prepare questions for legal on this eco campaign". Do not use for mapping general factual claims to evidence, use claims-evidence-map instead. 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 sustainability or ESG reports the user provides into a DRAFT question pack for reviewers: every quantitative disclosure as stated with unit, period, boundary and page, year-over-year changes computed only from the stated figures with the arithmetic shown, targets with stated and computed progress, restatements and inconsistencies, gaps and specific questions to the report owner. No assurance, accuracy or conformance verdict. Use when the user asks to "prepare review questions on our sustainability report", "what changed year on year in this ESG report", "check the figures in this report before it goes to the board", "find the gaps in this ESG disclosure" or "build a question pack for the report owner". Do not use for reviewing marketing text against environmental-claim rules, use green-claims-review instead. 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。

Builds one DRAFT message house for a product, service, programme or initiative from the source material the user provides: an umbrella statement, two to four pillars and graded proof points under each, with every proof point that lacks a source flagged, a claim sensitivity table, an audience coverage check and open questions. Use when the user asks to "build a message house", "write our messaging framework", "what are the key messages for this launch", "turn this research into messaging pillars" or "test our current messaging against the evidence". Do not use for a campaign one-pager, use campaign-brief-builder instead; to check whether a written document's claims are substantiated, use claims-evidence-map. 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。

Drafts one anonymised DRAFT case study (situation, approach, outcome as evidenced, optional lessons) from engagement notes, status reports, closing reports, emails and metrics extracts the user supplies, with a fact trace to the notes, an evidence grade on every result, quotes only where the user supplied them with speaker role and permission, an anonymisation log and a confidentiality checklist that must be completed before publication. Use when the user asks to "write a case study from these notes", "turn this engagement into a customer story", "draft a success story for the website", "anonymise this project write-up" or "prepare a reference case for the proposal". Do not use for redacting identifiers from an existing document, use document-deidentification-pass instead; for checking whether a document's claims are substantiated, use claims-evidence-map. 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。

Drafts an architecture or design decision record (ADR) from the discussion notes, design review minutes, chat threads and diagram descriptions the user provides: context, decision drivers, options considered with arguments as stated, the decision as the group worded it, consequences and a review date, with every factual claim tagged evidenced, asserted or UNKNOWN so unsupported claims are marked for the author to substantiate. Use when the user asks to "write an ADR", "document the architecture decision", "record why we chose this design", "turn these design review notes into a decision record" or "draft the design decision log entry". Do not use for a business or governance decision with no technical design, use decision-memo-builder instead; for the change ticket that implements the decision, use change-request-pack. 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。

Prepares a DRAFT leadership-meeting briefing pack from the reports, meeting notes, emails and dashboard exports the user provides: one headline, metrics exactly as stated with period and source, decisions the meeting must take, risks as reported, talking points with anticipated questions, a source reference on every line and UNKNOWN where the material is silent. Use when the user asks to "brief the leadership team", "prepare the exec pack", "pull together a briefing for the steering committee", "summarise these reports for the CEO", "what do I tell the board on Monday" or "build the pre-read for the management meeting". Do not use for a formal board or committee paper seeking a resolution, use board-paper-skeleton instead; for a personal one-page prep drawn from the user's own calendar and inbox, use meeting-prep-onepager. 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。

AI-first web search via Exa — semantic/neural ranking, category-scoped results, fresh content retrieval, and one-shot answer synthesis with citations. Use when the user needs current web information better ranked than general web search, or a grounded answer with sources.

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。

Audit and repair a kohya-style LoRA dataset before training. Use when the user asks to check, validate, inspect, fix, clean, 体检, or 修 a dataset, captions, or tags. Inspect image count, repeats and step budget, resolution and aspect buckets, duplicates, corrupt or non-RGB images, missing or empty captions, trigger consistency, and tag-frequency hygiene. Return a structural PASS, WARN, or FAIL report and apply only confirmed fix_dataset.py actions, dry-run first with displaced originals quarantined.

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。

FastAPI service setup: app/main.py, API routers, pydantic-settings, lifespan/app.state services, middleware, health checks, pytest/TestClient, Uvicorn, Gunicorn/uvicorn-worker, Docker/Compose. Use for scaffolding or modernizing Python API services; skip for Flask/Django-only, frontend-only, non-Python APIs, or Supabase-specific work.

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。

Full-text keyword search across every conversation at once (Telegram, Facebook, ChatGPT, Claude, notes, transcripts) over a local BM25/FTS5 catalog: instant, zero tokens, with snippets and chat-title hits. The exact-words lane, complementary to semantic search. Triggers: "/search <words>", "where did I write about <X>".

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。

Protocol for two LLM agents (Claude, Codex or any other) to agree and work from one shared folder: message format, channels, roles, evidence rules, wake-up alarm and anti-patterns. Triggers: /llm-pact, agree with codex, connect a second agent, pair rules, agent consensus, two-agent protocol.

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。

Check whether something was already decided before proposing it: searches the decisions journal, the declined registry and the rules codex plus semantic search, and returns what was decided, why, what was rejected, and under what condition to revisit. Triggers: "/precedent <topic>", "did we already decide this".

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。

Find a person, lead, contact or company by name in any spelling: a deterministic search that catches transliteration across alphabets, wrong-keyboard-layout typing and typos. This is the exact-name lane (zero tokens), complementary to semantic vault search. Triggers: "/find <name>", "show everyone named <X>".

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。

Ask the second brain in plain words: semantic search over the curated vault with embeddings plus a reranker, returning the smallest relevant slice instead of a corpus dump. Token-cheap. Triggers: "/ask <question>", "what do I have on <topic>", "what did I write about X".

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。

Rewrite machine-written text so it reads like a busy human wrote it: cut 50-70%, break the even rhythm, drop AI-tell words and filler, keep facts and protected terms. Use before publishing any post, reply or email drafted by a model. Triggers: /ai-slop, /slop, humanize, anti-slop, make it shorter and alive.

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。

Senior engineer CLI expertise for AI agents — workflows, safety guardrails, gotchas, and anti-patterns across cloud, IaC, containers, databases, dev tools, and platforms

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 after substantial web projects or repeated design defects to convert evidence into reusable skill improvements through root-cause analysis, regression scenarios, and controlled proposals without silently changing the core OS.

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。