「lab」の検索結果

動画用音声生成スキル。ElevenLabs TTS APIでscenes.jsonのナレーションから 音声ファイルを生成し、動画と同期可能な形式で出力する。 「ナレーション生成」「TTS」「音声追加」等で発動。

Index of Apple developer documentation for iOS, macOS, and related frameworks. Use when looking up what APIs exist in a framework, browsing available documentation, or deciding what docs to fetch. Covers SwiftUI, UIKit, Core Animation (QuartzCore), XCTest, HealthKit, Combine, SwiftData, and more.

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 pull requests with scope validation, requirements compliance, and line comments. Use when reviewing GitHub or GitLab PRs.

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。

Implements GitHub or GitLab issues via parallel subagents with review gates between task batches. Use when resolving multi-step issues end-to-end.

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。

Detects git forge (GitHub/GitLab/Bitbucket) and maps CLI commands cross-platform. Use when writing skills that must run on any git hosting provider.

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。

Operate doccano for text annotation, dataset import/export, auto-labeling, deployment, and repo maintenance.

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。

Route BERTopic topic modeling, embedding, vectorizer, labeling, visualization, and serialization workflows.

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Guides core Autodistill workflows for foundation-model auto-labeling, target-model distillation, CLI/plugin registry use, custom model interfaces, and utilities.

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Route AIMET install, PyTorch/ONNX quantization, GenAILab, model access, cluster/Pod, Qualcomm SDK, optimization, export, and repository tasks to focused operating guidance.

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。

Operate the Agriculture_KnowledgeGraph agricultural Neo4j graph, Django demo, entity labeling, crawler, and relation-extraction 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。

Validate, lint, audit, or fix .gitlab-ci.yml pipelines, stages, and jobs.

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, generate, or scaffold .gitlab-ci.yml pipelines, stages, and jobs.

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。

Research why neural architectures and training methods work through forward computation, geometry, gradients, optimization dynamics, historical experiments, and competing explanations. Use for deep mechanism questions about neural networks, AI models, layers, representations, or training; explain the meaning in plain language with evidence and explicit limits.

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Evaluate Agent Skill design quality with an opinionated, practice-derived rubric informed by public specifications and examples. Use when reviewing, auditing, comparing, or improving SKILL.md files and skill packages. Produces evidence-backed findings, optional multi-dimensional scoring, and prioritized changes; treat the result as diagnostic guidance, not official certification or a universal standard.

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Deeply research technical architecture, source code, mechanisms, SDKs, frameworks, project comparisons, and system-design options across repositories, history, official docs, issues, discussions, and high-quality community signals. Use when the user asks for deep research, source-level understanding, architecture comparison, mechanism satisfaction, or evidence for designing a system. Always map the question and obtain confirmation before beginning substantive research; skip for simple factual lookups or straightforward implementation tasks.

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Coach leaders and managers to prepare, run, and review high-bandwidth meetings that advance real work and decisions. Use for meeting retrospectives, employee work reviews, decision meetings, agenda and question design, direct feedback, meeting-quality diagnosis, or repeated low-value discussions. Preserve direct standards without taking over the worker's work or degrading the truth signal.

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Recover and review local human-AI conversations from Claude Code, Codex, opencode, Grok Build, and Cursor. Use when the user wants to revisit, search, extract, summarize, or compare work across Agent session stores, time ranges, projects, or named conversations. Follow the user's requested output; question clustering, problem-space mapping, artifact review, drift analysis, and insights are optional lenses.

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Create platform-native content that resonates with each community's culture. Use when adapting technical content for WeChat, Hacker News, Reddit, Medium, Twitter/X, Dev.to, or LinkedIn. Transforms generic writing into content that feels written BY that community, not AT them.

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Design and build AI agents for any domain. Use when users: (1) ask to "create an agent", "build an assistant", or "design an AI system" (2) want to understand agent architecture, agentic patterns, or autonomous AI (3) need help with capabilities, subagents, planning, or skill mechanisms (4) ask about AI coding agents, agent runtimes, or similar internals (5) want to build agents for business, research, creative, or operational tasks Keywords: agent, assistant, autonomous, workflow, tool use, multi-step, orchestration

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Reconstruct and report long-running or multi-turn research, architecture questions, reviews, decisions, completion results, and status as a clear, self-contained brief. Use when a reader must re-enter earlier context, understand the real question and its relationships, distinguish verified facts from judgment and open evidence, or make a decision without reconstructing the work log. Quote the relevant original user wording before substantive analysis, keep one coherent main line, use spatial diagrams when relationships matter, and finish with an explicit decision boundary and practical next step.

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Coach workers, employees, and individual contributors to turn meetings, transcripts, minutes, and work notes into better execution: infer leader expectations, diagnose previous work, identify next actions, judge delivery readiness, and prepare a high-bandwidth update. Use after meetings, during work, before reviews, or across repeated low-value discussions. Perform semantic review from source evidence.

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This skill should be used when the user asks for a "sensitivity reader", "authenticity reader", "cultural review", "is this portrayal okay", "real people in my novel", "defamation", "can I use song lyrics", "epigraph permission", "permissions", "quote permission", "fair use", "AI disclosure", "do I need to disclose AI", "send to my editor", "editorial round", "Word file for my editor", "editor review copy", "co-author", "collaborate on a book", "shared world", "back up my book", "does this echo my source", "similarity check", "check overlap with my earlier books", or wants to run human editorial, ethics, permissions, or collaboration workflows around a story project. NOT for contracts or selling rights (use publishing), or reader feedback rounds and review copies for readers, including the GitHub review-copy setup (use feedback-triage).

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Use when annotating nullability in Spring Boot 3 / Spring Framework 6 code, integrating Kotlin, or adding static nullability checks without assuming Spring Framework 7 JSpecify defaults.

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Maintenance of the contributor issue pipeline for JSONbored/awesome-claude (HeyClaude) — closing issues that are already done but not marked so, and keeping a small, high-value contributor-available backlog stocked with real website/content/feature/bugfix work. Runs every ~24h via a dedicated scheduled task. Invoke for "run the issue gardening", "audit open issues for stale/complete ones", "generate new contributor issues", or any recurring/scheduled run of this process. `reference.md` (next to this file) has the exhaustive label/milestone/ template detail — read it before doing real work, not just this file. This is the awesome-claude-specific instance; JSONbored/loopover and JSONbored/metagraphed each have their own separate copy with different conventions and a much larger backlog target — do not cross-apply either repo's specifics to this one without being asked.

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。