「agent workflow」の検索結果

Audit, design, categorize, distribute, and measure agent skills using lessons from Anthropic's Lessons from building Claude Code: How we use skills. Use when reviewing an existing skill, deciding whether a workflow deserves a skill, planning a skill library, turning team knowledge into skills, choosing skill categories, writing trigger descriptions, designing progressive disclosure, or planning skill marketplace and usage measurement.

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 you want Codex to review its own recent history (last N days or specific period) and improve its behavior. Produces minimal, high-signal updates to AGENTS.md and tiny reusable skills. The goal is long-term fluency — Codex gradually becomes better at your specific style, constraints, and 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。

Use when an agent needs to write user stories for a project

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。

Transform legacy codebases into AI-ready projects with Claude Code configurations. Use when (1) analyzing old projects to generate AI coding configurations, (2) creating CLAUDE.md, skills, subagents, slash commands, hooks, or rules for existing projects, (3) user wants to enable vibe coding for a codebase, (4) onboarding new team members with AI-assisted development, (5) user mentions "make project AI-ready", "generate Claude config", or "create coding standards for AI".

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。

Applies the mental models and frameworks of Andrej Karpathy (deep learning, former Director of AI at Tesla, founding member of OpenAI, Eureka Labs). Use this skill whenever you are helping the user build neural networks from scratch, debug deep learning pipelines, evaluate AI agent workflows, design LLM apps, or navigate the transition to Software 3.0 (vibe coding). It is highly relevant for pedagogy (untangling complex knowledge), assessing AI capabilities vs. limitations (jagged intelligence, tokenization limits), and architectural decisions (end-to-end optimization vs. complex pipelines). Reach for this whenever discussing LLM training, autonomous systems, or AI-assisted coding.

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。

Applies the reasoning, principles, and frameworks of Andrew Ng (machine learning pioneer, co-founder of Coursera and DeepLearning.AI, Stanford University, and former Google Brain lead). Use this skill whenever the user is navigating AI application development, agentic workflows, automation strategy, AI-native software engineering, or rapid prototyping. Trigger this skill when discussing career advice in the AI era, evaluating AI regulations, structuring machine learning projects, or deciding how to integrate AI into a business. It emphasizes task-based automation, data-centric ML, and driving the cost of proof-of-concepts to zero.

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。

Build DSPy 3.2.x programs through spec, program, metric and baseline; extend to optimization and export when requested and justified by task budget. Orchestrates the other four DSPy skills (dspy-fundamentals, dspy-evaluation-harness, dspy-gepa-optimizer, dspy-rlm-module) in the correct order. Use for greenfield DSPy builds; prototypes may stop at a validated baseline.

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。

Agent-applied GRACE 3 to GRACE 4 migration workflow. CLI validates the result but does not convert or delete files.

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。

Source-backed research workflow for AI agents: scope, sources, claims, and task-appropriate Lite or Full checks.

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。

Source-backed research for AI agents: scope, sources, claims, and task-appropriate Lite or Full 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。

Interview employees to identify recurring daily work, office workflows, and repetitive tasks that can be automated, co-piloted, or supported by AI. Use when the user wants to find what AI can take off their plate, map available tools such as ChatGPT/OpenAI, Claude, Microsoft Copilot, Excel or PowerPoint add-ins, Google Workspace, no-code/RPA tools, coding agents, or internal automation platforms, and write tool-aware specs with fallback paths.

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 Personal Corp operating loop needs setup, repair, a new department, or task routing: HQ files and agent rules, GitHub issue workflow, corp-* owner map, department repositories, or deciding which repo an issue belongs to. Triggers: "corp doctor", "почини контур", "заведи отдел", "куда положить задачу", "настрой штаб", "проверь операционку". Replaces corp-init, corp-new and task-routing.

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。

Inspect an unfamiliar repository, turn a focused Markdown behavior scenario into a deterministic test in the repository's native test stack, run it, and preserve traceability between intent and code. Use when asked to add scenario tests, compile acceptance criteria or Given/When/Then Markdown into executable tests, reproduce a user-visible regression, or convert a narrow workflow specification into stable web, API, CLI, desktop, or mobile interaction coverage. Do not use for broad exploratory journeys or agent-judged smoke tests.

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。

Lightweight standalone design spec for AI coding agents: codebase scan → bounded clarify pass (3–5 questions, not a grill) → reviewed design doc written to docs/plans/ → independent scored review → stop. No task board, no build. Use when you want a spec to discuss, hand off, or review before committing to implementation. Chains into absolute-work when ready to build. Triggers on "absolute spec", "write a spec", "spec out this feature", "draft a design doc", "I want a spec to hand off / review, don't build it yet".

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。

Diátaxis-driven documentation for AI coding agents: write, improve, or audit tutorials, how-tos, reference, explanation, and developer docs (README, CONTRIBUTING, ADRs). Detects the docs stack; gates on the outline before writing prose; verifies every claim against the code before it ships. Triggers on "absolute docs", "write docs", "write a tutorial", "write a README", "document this", "improve this doc", "audit our docs".

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。

End-to-end, phase-gated SDLC for AI coding agents: relentless design interview → reviewed spec → dependency-graphed task board → safe-wave TDD execution → verification → converge. Handles features, bugs, refactors, greenfield projects, planning breakdowns, and migrations. Triggers on "absolute work", "build this end-to-end", "plan and build", "break this into tasks", "grill me on this plan", "pick up this ticket", "run this migration".

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。

Proofread and correct text for grammar, spelling, punctuation, style, clarity, and consistency, with support for multiple style guides and readability analysis. Use when the user requests proofreading or provides relevant inputs for this workflow.

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。

Translate text between languages with cultural adaptation, terminology consistency, and support for multiple document types including technical docs, marketing copy, and UI strings. Use when the user requests translation or provides relevant inputs for this workflow.

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。

Write clear, concise, and accurate technical documentation including API references, user guides, tutorials, changelogs, and architecture docs, tailored to the target audience. Use when the user requests technical writing or provides relevant inputs for this workflow.

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。

Set up and orchestrate multi-container Docker applications using docker-compose, including service configuration, networking, volumes, and environment management. Use when the user requests docker compose setup or provides relevant inputs for this workflow.

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。

Score and prioritize leads based on firmographic fit and behavioral engagement signals, producing ranked tiers for sales team focus. Use when the user requests lead scoring or provides relevant inputs for this workflow.

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。

Enrich CRM records with firmographic and contact data, filling gaps in company and person profiles to improve segmentation, routing, and outreach quality. Use when the user requests crm data enrichment or provides relevant inputs for this workflow.

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。

Schedules meetings with internal and external stakeholders by parsing natural language requests, resolving time zones, checking calendar availability, proposing optimal slots, and managing invitations and RSVPs. Use when the user requests meeting scheduler or provides relevant inputs for this workflow.

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。

Design professional, scalable logos with complete brand identity deliverables including color palettes, typography, format variations, and usage guidelines. Use when the user requests logo design or provides relevant inputs for this workflow.

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。