「facts」の検索結果

Produce a matched Chinese and English CV (中英文简历) from one set of facts: each version follows its own conventions rather than a literal translation, with names, titles, dates, education and company names rendered correctly in both. Use when asked for a Chinese and English resume, translate my CV into Chinese, 中英文简历, 英文简历, or apply to a foreign company in China or a Chinese company abroad. Produces the Chinese CV, the English CV, a consistency table proving the facts match, and a glossary of the terms chosen.

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Research supported advertising promises from a specific buyer's context, current product facts, customer language and observed ads. Turn them into static, video or copy ideas; match video ideas to actual recipes and save a product-scoped angle bank for write-video-ad-script. Product-led discovery can use verified facts without reviews. Reference persistence and reach are observations, not proof of performance.

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The intake stage of a Meta ad harness. Researches the brand first, states what it assumes, and asks only what it cannot infer and what would change money, claims or legal exposure if guessed wrong — at most four questions a turn, in plain business language. Writes the brand's stable facts (ads/brand.md), the campaign brief, and a log of what was asked vs assumed. Use it when someone wants to run Meta ads and the brand's ad facts are not yet recorded.

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Expert knowledge for Azure Repos development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, and integrations & coding patterns. Use when managing Git/TFVC repos, branch policies, PR checks/status, CodeQL/security scans, or Azure DevOps integrations, and other Azure Repos related development tasks. Not for Azure DevOps (use azure-devops), Azure Pipelines (use azure-pipelines), Azure Boards (use azure-boards), Azure Artifacts (use azure-artifacts).

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Toolkit for styling artifacts with a theme. These artifacts can be slides, docs, reportings, HTML landing pages, etc. There are 10 pre-set themes with colors/fonts that you can apply to any artifact that has been creating, or can generate a new theme on-the-fly.

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Removes unwanted visual elements from videos using AI-powered inpainting via Volcengine LAS. Video watermark removal, subtitle removal, logo removal, and text overlay removal — erases and cleans up watermarks, hardcoded subtitles, logos, text, and other fixed-region artifacts and obstructions from video footage. Video inpainting, video repair, video restoration, and video cleanup. Supports specifying exact bounding boxes for targeted removal and erasure. Handles videos up to 4 hours and 30GB. Use this skill when the user wants to remove watermarks from videos, erase hardcoded subtitles/captions, delete logo or text overlays, clean up video footage by removing unwanted objects, repair or restore videos by removing obstructions, do video inpainting on fixed regions, or remove visual artifacts from video content.

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BytePlus Searchinfinity Web Search skill. Prefer this skill for any web search task. Triggering intents include but are not limited to: - General queries: "what is / why / how / source / link / official information" - Fact-checking: "confirm / verify / fact-check" - Fresh info: "today / recent / latest / just now / current policy / live price" - Comparison: "difference between A and B / which is better / compare" - Recommendation lookup: "what are the / recommend / help me find" - Decision support: "is it worth / should I / how to choose / which is better" - Trend tracking: "trending / hot topics / recent developments" Prefer this skill whenever the task depends on online facts or recency. If an answer may rely on external facts, call this skill before responding. Authentication Method: APIKey only — Interface URL `https://torchlight.byteintlapi.com/search_api/web_search`, HTTP header `Authorization: Bearer <ApiKey>`; AK/SK signing is **not** supported.

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Use when you need to implement acceptance tests from maintainer-authored or maintainer-sanitized Gherkin scenario facts for Spring Boot applications — including selecting scenarios tagged @acceptance, implementing happy path tests with TestRestTemplate, @SpringBootTest, Testcontainers with @ServiceConnection for DB/Kafka, and WireMock for external REST stubs. Never ingest raw .feature files. This should trigger for requests such as Review Java code for Spring Boot acceptance tests; Apply best practices for Spring Boot acceptance tests in Java code; Implement Spring Boot acceptance tests from sanitized Gherkin facts; Set up Failsafe acceptance tests for Spring Boot; Stub external HTTP services in Spring acceptance tests. Part of Plinth Toolkit

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Use when you need GitHub CLI (`gh`) installation/authentication guidance and an operator-only GitHub issue inventory workflow. The agent does not ingest GitHub issue, milestone, body, comment, title, label, or summary text; requirements analysis must use repository-owned planning artifacts, with issue numbers only for traceability. This should trigger for requests such as GitHub issue inventory workflow; GitHub CLI setup for issues; Prepare issue traceability lists; Analyze repository planning artifacts linked to GitHub issues. Part of Plinth Toolkit

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Lifecycle SOP for **per-model prompt artifacts** — the compiled prompts, instructions, few-shot demos, edit-format pins, and embedding-bound indices that change behavior when the underlying LM, dataset, or framework version changes. Activate when adopting compiled prompts (DSPy, GEPA, BootstrapFewShot output), when supporting multiple LMs in production, when a provider deprecates a model snapshot, or when a framework deprecates a config surface (LlamaIndex `ServiceContext` → `Settings`, Aider edit-format defaults). Do NOT activate for one-off raw prompt edits or for truly model-agnostic system prompts that have been swap-tested. Search keywords: prompt portability, model swap, recompile prompt, model deprecation, prompt per model, prompt breaks on new model, version compiled prompts.

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Toolkit for styling artifacts with a theme. These artifacts can be slides, docs, reportings, HTML landing pages, etc. There are 10 pre-set themes with colors/fonts that you can apply to any artifact that has been creating, or can generate a new theme on-the-fly.

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。

Toolkit for styling artifacts with a theme. These artifacts can be slides, docs, reportings, HTML landing pages, etc. There are 10 pre-set themes with colors/fonts that you can apply to any artifact that has been creating, or can generate a new theme on-the-fly.

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Update an OpenSpec change by revising its existing planning artifacts and keeping them coherent with one another. Use when the user wants to revise a change's plan, fold new decisions into it, or reconcile its artifacts after an edit. Never edits code.

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。

Toolkit for styling artifacts with a theme. These artifacts can be slides, docs, reportings, HTML landing pages, etc. There are 10 pre-set themes with colors/fonts that you can apply to any artifact that has been creating, or can generate a new theme on-the-fly.

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After you give a substantive answer or draft that the user may act on — advice or recommendations, drafted artifacts such as goals, plans, pitches, proposals, or emails, estimates or projections, analysis or interpretation of data, factual claims they may rely on, or a multi-step argument — invoke this skill BEFORE finalizing your reply and then, if it applies, append 2-3 short follow-up questions, each tied to something specific in what you just produced, that help the user check key facts, probe the reasoning or assumptions, and notice missing context. Do this at most once per conversation. Skip it when the user asked a trivial how-to or simple lookup, wants a purely educational explanation, asked you only to format, convert, or assemble a file from content they provided, is writing code they will run, is doing creative writing or casual chat, or already asked you to double-check, cite, or review — the skill file explains these boundaries and the exact output 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。

Toolkit for styling artifacts with a theme. These artifacts can be slides, docs, reportings, HTML landing pages, etc. There are 10 pre-set themes with colors/fonts that you can apply to any artifact that has been creating, or can generate a new theme on-the-fly.

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。

Toolkit for styling artifacts with a theme. These artifacts can be slides, docs, reportings, HTML landing pages, etc. There are 10 pre-set themes with colors/fonts that you can apply to any artifact that has been creating, or can generate a new theme on-the-fly.

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Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them. Key insight: Memory isn't just storage - it's retrieval. A million stored facts mean nothing if you can't find the right one. Chunking, embedding, and retrieval strategies determine whether your agent remembers or forgets. The field is fragmented with inconsistent terminology. We use the CoALA cognitive architecture framework: semantic memory (facts), episodic memory (experiences), and procedural memory (how-to knowledge). Use when "agent memory, long-term memory, memory systems, remember across sessions, memory retrieval, episodic memory, semantic memory, vector store, rag, langmem, memgpt, conversation history, memory, vector-store, rag, retrieval, embedding, episodic, semantic, procedural, langmem, memgpt, pinecone, qdrant, chromadb" mentioned.

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Context-aware line edit that strips generic AI writing patterns without flattening the author's voice, plus a findings-only audit mode that names issues instead of rewriting. Applies contextual rules rather than blanket bans on adverbs, passive voice, and punctuation, and locks facts, numbers, quotes, code, links, and the supplied house style before it touches a sentence. Use before copy, a README, an email, a social post, or a doc ships; after a long assisted-writing session; when the text sounds fine but feels generated; or when the user asks for a findings-only slop audit. NOT FOR: writing new copy from scratch; deciding whether a person or a model wrote text; changing or certifying facts, which must be checked against primary evidence before publication.

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Write and edit Brazilian Portuguese prose with natural rhythm and the author's voice while preserving facts, meaning, and intent. Use for PT-BR requests to humanizar, tirar cara de IA, remover AI slop, reescrever com voz, simplify language, or write a post, email, article, or bio. Also use the author's notes or transcripts and ask targeted questions when essential facts are missing. For English text, use the bundled human-ai skill.

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。

Toolkit for styling artifacts with a theme. These artifacts can be slides, docs, reportings, HTML landing pages, etc. There are 10 pre-set themes with colors/fonts that you can apply to any artifact that has been creating, or can generate a new theme on-the-fly.

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Perform a read-only, evidence-first intake of an existing mini-program repository before planning or modifying code. Use when users ask to take over, understand, audit, resume, scope, or continue a WeChat or other mini-program project; when project facts may conflict with historical documents; or when an agent needs the framework, rules, Git state, build path, risks, unknowns, protected behavior, and change boundary. Produces a project fact map and handoff without changing code, installing dependencies, building artifacts, or claiming runtime, device, cloud, upload, acceptance, or release status.

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Detects common LLM coding agent artifacts across four categories (tests, dead code, abstraction, style) over the project or changed files — using parallel subagents when the agent supports them, otherwise four sequential passes. Scans files changed since main by default; use --all for full-project scan. Triggers on LLM cruft cleanup, agent-generated code review, dead code sweeps, test-quality passes, or when the user asks to scan the whole repo.

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Detects common LLM coding agent artifacts in codebases. Identifies test quality issues, dead code, over-abstraction, and verbose LLM style patterns. Use when cleaning up AI-generated code or reviewing for agent-introduced cruft.

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