「fact」の検索結果

Behavioral guidelines to reduce common LLM coding mistakes. Use when writing, reviewing, or refactoring code to avoid overcomplication, make surgical changes, surface assumptions, and define verifiable success criteria.

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Angular performance optimization and best practices guide. Use when writing, reviewing, or refactoring Angular code for optimal performance, bundle size, and rendering efficiency.

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Design production-grade multi-agent workflows with clear pattern choice (sequential, parallel, hierarchical), handoff contracts, failure handling, and cost/context controls. Use when architecting a multi-step agent pipeline, choosing between single-agent vs multi-agent approaches, or refactoring an LLM workflow that suffers from context bloat or unreliable handoffs.

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Use before non-trivial coding work to pick a task-appropriate working mode — debug, fix, review, test-first, refactor, optimize, migrate, upgrade, integrate, spike, or unstuck — then follow its checklist. Modes are procedures, not personas. Not needed for trivial one-line edits or non-coding tasks.

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Use when designing, reviewing, refactoring, or enforcing Angular application architecture with isolated lazy features, eager core/layout, reusable ui/pattern architecture building blocks, one-way dependency graphs, eslint-plugin-boundaries rules, or folder placement decisions. This skill takes precedence over angular-developer when architecture rules conflict with general Angular advice.

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Capture baseline evidence from a live website URL: screenshots, scroll states, hover/click behavior, parallax, timers, and transition recordings. Use when reference artifacts are needed before implementation or comparison; not to build or diagnose a clone.

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Rewrite AI-sounding prose so it reads as considered human writing — cut the tells (em-dash pile-ups, "it's not X, it's Y" antitheses, inflated vocabulary, uniform rhythm, empty uplift) while preserving every fact, claim, and the author's own voice. Use when the user says "make this not sound like AI", "de-AI this", "humanize/humanise this", "this reads like ChatGPT", "remove the AI tells", "it sounds robotic/generic", or shares a draft (post, email, essay, docs, cover letter) to sharpen into a more human voice. Ships a stdlib scanner that flags the mechanical tells.

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Use when Amazon Ads optimization analysis, recommendations, approval packets, demos, recordings, or public artifacts must preserve exact source KPIs while masking account/profile/project names, products, ASINs, SKUs, campaigns, ad groups, keywords, search terms, targets, placements, filenames, URLs, and source-derived identifiers. Produces masked approval packets only; it must not directly mutate Amazon Ads.

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Multi-angle review of responses before delivery. Audits from 4 perspectives -- factual accuracy, logical consistency, completeness, and safety. Use for high-stakes responses where errors have real consequences.

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Assign confidence scores (0-100) to every claim in a response. Helps users understand which parts are verified facts and which are educated guesses. Use when the user needs to know how much to trust each part of the answer.

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Ensure every factual claim in a response is backed by verifiable evidence from the codebase, documentation, or external sources. Use when preparing technical documentation, code reviews, or any output where accuracy is non-negotiable.

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Run or resume a deliverable active-outreach workflow across lead discovery, screening, customer intel, and outreach email. Use when an operator or system pipeline needs durable run state, visible intermediate artifacts, evidence gates, report-bound human approval, and a final editable email draft that is never auto-sent.

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OpenClaw-native version of the foreign-trade outreach email skill. Use structured operator input plus customer-intel source context to generate conservative first-touch or follow-up email drafts without overstating inferred facts or inventing recent customer events.

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Keep a small requested change focused when optional refactors, architectural redesign, or unrelated cleanup threaten to expand its scope.

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Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, artifacts, posters, or applications (examples include websites, landing pages, dashboards, React components, HTML/CSS layouts, or when styling/beautifying any web UI). Generates creative, polished code and UI design that avoids generic AI aesthetics.

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Package completed data analysis and ML work so an independent recipient can reproduce the claimed results, verify artifact lineage, and operate the handoff within its stated scope. Use when finalizing a project, study, model package, or review bundle; not for deploying to a live system.

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Build or modify frontend interfaces using repository-native components while preserving accessibility, responsive behavior, state handling, and visual verification. Use for UI features, design implementation, forms, client-side defects, and frontend refactors; do not use for backend-only or infrastructure-only work.

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Build or modify backend APIs, services, jobs, persistence, and integrations while preserving contracts, authorization, data integrity, and failure behavior. Use for server-side features, database changes, background processing, backend defects, and service refactors; do not use for frontend-only or infrastructure-only work.

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Architect systems like Fable 5 — deep understanding before design, modular thinking, iterative refinement. Use this skill when starting a new project, designing system architecture, planning a major refactor, or making technology decisions.

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Decides how far ahead to plan, what to discount future payoffs by, and which backlog items can be dropped without analysis - using receding-horizon planning, an explicit discount factor, and branch-and-bound pruning against the incumbent. Use when building a roadmap, prioritising a backlog, arguing about short-term versus long-term, setting quarterly or annual plans, or when planning has become an end in itself.

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Turn briefs, mockups, and product context into Stitch UI screens, design systems, variants, prototypes, Tailwind-friendly HTML, and screenshots. Use when the user wants to explore a screen, edit an existing Stitch project, compare visual directions, build a multi-screen prototype, or save local design/code-export artifacts from natural-language input.

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Build GenAI and agentic apps with genai-tk (tclatos/genai-tk), a YAML-driven IoC toolkit over LangChain, DeepAgents, DeerFlow, and Prefect. Use this skill whenever the user mentions genai-tk, genai_tk, `cli init`, `cli agents run`, `LangchainAgent`, `create_harness`, `get_llm`, `global_config()`, `model_id@provider`, `agents:`/`agent_defaults:` profiles, `RetrieverFactory`, `cli workflow`, or BAML extraction. Also trigger for app_conf.yaml, GENAITK_PROFILE, CodeAct, OpenSandbox, decision models, or "the toolkit".

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Generate the ≤10-minute Loom walkthrough script for a completed Weekly Community Signal report — a plain spoken briefing that dives straight into what the team needs to know, walking the report top to bottom (CopilotKit page first, then AG-UI in the same order). Straightforward and factual, NOT a radio show or a performance; no hype opener, no blame framing. Runs after every report (invoked by weekly-report) and on "loom script", "walkthrough script", "record the loom", "narrate the report".

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Answer questions about the venue rate facts recorded in this model and identify what its source leaves unspecified.

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。