「edge」の検索結果

User story generation - INVEST criteria, acceptance criteria (BDD/Given-When-Then), story mapping, epic decomposition, edge case stories, NFR stories

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。

Analyze brownfield codebase and create initial continuity ledger

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。

Property-based testing (PBT) patterns with fast-check (JS/TS), Hypothesis (Python), and gopter (Go). Generate random inputs, define invariants, shrink failures to minimal cases. Adapted from Trail of Bits. Use when testing pure functions, parsers, serializers, state machines, or any code where example-based tests miss edge cases.

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。

Proje bilgisini 4 katmanli yapida organize et. Aktif projeler, sorumluluk alanlari, referans kaynaklar ve arsiv. Progressive summarization ile bilgiyi katman katman ozumse. Second brain pattern'i ile codebase bilgisini yapilandir.

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。

Generate exactly one finished CRT retro computer-interface illustration from an uploaded portrait, group photo, creature, object, or described theme. Use when the user wants anchored subjects re-authored as an exaggerated circa-1980s bitmap editorial caricature beneath early Macintosh or Minitel-style windows, optionally invoking the exact Chinese presets 街头怪诞, 巨像符号, or 冷面几何. Before generation, collect a named or image-derived palette and aspect ratio through a strict two-stage intake unless supplied. Preserve each intended subject as a distinct identity, one shared pixel lattice, restricted non-skin checkerboard midtones, low-facet geometry, unequal extraction windows, mandatory barrel-curved CRT edges, and the immutable tait-crt-interface-skill title-bar signature. Never trace, auto-pixelate, imitate photographic detail, omit, merge, or duplicate subjects, retry, resize, expose prompts, or ask users to choose a style preset.

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Graph database implementation for relationship-heavy data models. Use when building social networks, recommendation engines, knowledge graphs, or fraud detection. Covers Neo4j (primary), ArangoDB, Amazon Neptune, Cypher query patterns, and graph data modeling.

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Use when `paper-review` needs a claim-by-claim evidence gap report grounded in an extracted claim ledger.

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 when a review workspace has manuscript text and needs a traceable claim ledger.

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 this skill to get documentation for third-party APIs, SDKs or libraries before writing code that uses them to ensure you have the latest, most accurate documentation. This is a better way to find documentation than doing web search. This includes when a user asks for tasks like "use the OpenAI API", "call the Stripe API", "use the Anthropic SDK", "query Pinecone", or any other time the user asks you to write code against an external service and you need current API reference. Fetch the docs with chub before answering, rather than relying on your pre-trained knowledge, which may be outdated because of recent changes to these APIs. Be sure to use this skill when the user asks for the latest docs, latest API behavior, or explicitly mentions chub or Context Hub. Ensure `chub` is available, run `chub --help`, then follow the instructions there.

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。

Query and maintain the user's local LLM Wiki from any Codex or Claude Code conversation or project. Use when the user asks to search personal knowledge, recall prior research or decisions, answer from the wiki, create durable notes, ingest a source into the wiki, update topic/concept/entity/question pages, write back useful answers, or run wiki lint/reindex workflows.

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。

SwiftUI animation foundations - withAnimation, transitions, matchedGeometryEffect, PhaseAnimator, KeyframeAnimator, springs, gestures.

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Use `graphify-dotnet` to generate codebase knowledge graphs, architecture snapshots, and exportable repository maps from .NET or polyglot source trees, with optional AI-enriched semantic relationships. USE FOR: graphify commands; graph JSON, HTML, SVG, Cypher, Markdown, and Obsidian exports; repository map and architecture snapshot generation. DO NOT USE FOR: unrelated stacks; generic tasks that do not need this specific guidance. INVOKES: inspect the repository context, edit targeted files, and run relevant build, test, lint, or validation commands when changes are made.

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Make .NET projects compatible with Native AOT and trimming by systematically resolving IL trim/AOT analyzer warnings. USE FOR: making projects AOT-compatible, fixing trimming warnings, resolving IL warnings (IL2026, IL2070, IL2067, IL2072, IL3050), adding DynamicallyAccessedMembers annotations, enabling IsAotCompatible. DO NOT USE FOR: publishing native AOT binaries, optimizing binary size, replacing reflection-heavy libraries with alternatives. INVOKES: no tools — pure knowledge skill.

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Expert knowledge of the libvlc C API (3.x and 4.x), the multimedia framework behind VLC media player. Use when helping with LibVLC or LibVLCSharp for media playback, streaming, or transcoding. USE FOR: LibVLC Skill implementation, review, migration, debugging, or documentation work. DO NOT USE FOR: unrelated stacks; generic tasks that do not need this specific guidance. INVOKES: inspect the repository context, edit targeted files, and run relevant build, test, lint, or validation commands when changes are made.

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Generate HeyGen presenter videos via the v3 Video Agent pipeline — handles Frame Check (aspect ratio correction), prompt engineering, avatar resolution, and voice selection. Required for any HeyGen video generation. Replaces deprecated endpoints with v3. Use when: (1) generating any HeyGen video (via API or otherwise), (2) sending a personalized video message (outreach, update, announcement, pitch, knowledge), (3) creating a HeyGen presenter-led explainer, tutorial, or product demo with a human face, (4) "make a video of me saying...", "send a video to my leads", "record an update for my team", "create a video pitch", "make a loom-style message", "I want to appear in this video", "generate a HeyGen video", "make a talking head video". Accepts avatar_id from heygen-avatar for identity-first HeyGen videos, or uses a stock presenter. Returns video share URL + HeyGen session URL for iteration. Chain signal: when the user wants to create/design an avatar AND make a video in the same request, run heygen-avatar first, then return here. Conjunctions to watch: "and then", "and immediately", "first...then", "X and make a video", "design [presenter] and record" = always CHAIN. If the user provides a photo AND wants a video, route to heygen-avatar first. NOT for: avatar creation or identity setup (use heygen-avatar first), cinematic footage or b-roll without a presenter, translating videos, TTS-only, or streaming avatars.

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将计划、规范或当前对话拆分为一组 tracer-bullet ticket，每个 ticket 声明其阻塞边（blocking edges），发布到已配置的跟踪器——本地文件中每个 ticket 一个文件以文本表示边，真实跟踪器上以原生阻塞链接表示。

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Deploy, update, validate, and troubleshoot the AgentKit hybrid-cloud customer-service demo in this directory. Use when a user asks an AI coding agent to follow the README, deploy or update the demo, configure OpenAPI/Runtime/Knowledge/Memory/Sandbox/MCP/Skills/A2A, run customer-view validation, or record manual platform steps and failures.

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当用户提到知识库时，默认使用此技能进行处理，进行 Viking 知识库服务进行相关操作

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官方Viking CLI 命令行助手：本CLI覆盖火山引擎/BytePlus VikingDB(向量库)/Knowledge(知识库)/Memory(记忆库)的数据集管理和数据的读写及检索， 可用于扩展Agent的知识检索边界 提升Agent的记忆能力； 当用户对知识库/向量库提问时，使用本Skill; 当用户要操作向量库/知识库 或 从向量库/知识库检索信息时使用本Skill； 当用户要记忆检索和记忆存储时，使用本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。

调用火山引擎Viking知识库的远程API，检索和query相关的知识库数据。使用场景包括：查询知识库内容、获取相关文档数据、检索特定信息等。当需要搜索数据回答用户问题时使用此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。

火山引擎 Supabase . 管理 AIDAP Supabase 项目、工作区、数据库、分支、Auth、Realtime、Edge Functions、Storage、API Keys、SQL、Schema 变更、TypeScript 类型生成和 MCP 集成。

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。

ByteHouse 知识库的管理能力，包括创建知识库、添加知识库内容、查询知识库

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 which AI features to build, delay, shelve or kill, and whether each one gets better as models improve. Produces an AI Bet Memo (feature-by-feature verdicts, retest triggers, scaffolding ledger, moat and safety check) from a roadmap or idea list. Use for 'AI strategy', 'AI roadmap', 'what should we build with AI', 'will the next model make this obsolete', 'build vs wait for the model', 'will OpenAI/Anthropic build this', 'agent or chatbot', 'is this feature worth shipping'. Draws on 118 Lenny's Podcast guests incl. Nick Turley, Kevin Weil and Dianne Penn.

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。

Pull request lifecycle: commit, codex review, sync, review, fix, status, cleanup, and PR mining. Use when user wants to commit changes, get a second-opinion code review from Codex, push changes, create a PR, check PR status, fix review comments, clean up branches after merge, or mine tribal knowledge from PR reviews. Use for "commit my changes", "codex review", "push my changes", "create a PR", "pr status", "fix PR comments", "clean up branches", "mine PRs", or "address feedback".

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。