「agentic ai」の検索結果

Turn any installed skill (gstack, custom, third-party) into an ATO agent the user can summon into war-rooms. Reads a SKILL.md, extracts the persona, strips runtime boilerplate, and writes an agent file at `.claude/agents/<slug>.md` (project-scoped) or `~/.claude/agents/<slug>.md` (global). Prompts for a model roster (primary + 1-2 alts) so cross-family dispatch in war-rooms produces real disagreement. Companion to `ato-warroom` — that skill summons agents this skill creates. Use when asked "turn this skill into an agent", "register X as a war-room agent", or when scoping a new persona before a war-room runs.

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 monitoring, tracing, or debugging agentic workflows in production. Keywords: observability, tracing, OpenTelemetry, Langfuse, latency, token cost, loop detection, telemetry.

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 designing, implementing, or debugging complex agentic workflows using state machine patterns. Keywords: state machine, durable execution, agent orchestration, graph-based agents, workflow checkpoints, C4 protocol.

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。

Agentic workflow patterns for autonomous LLM reasoning. Use when building ReAct agents, implementing reasoning loops, or creating LLMs that plan and execute multi-step tasks.

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 effective AI agent systems with clear architecture, capability planning, and tool integration. Use when building agent systems, designing agent workflows, planning tool ecosystems, defining agent roles and boundaries, or architecting multi-agent systems. Covers agent patterns, agentic loops, capability design, and system composition.

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 custom AI agents in Claude Code from a user's problem statement. This skill analyzes the user's use case, asks smart clarifying questions, researches the internet for similar agents (GitHub repos, blogs, Claude Code community patterns), and then architects and builds production-ready Claude Code agents — including subagents, skills, hooks, slash commands, MCP integrations, and CLAUDE.md configuration. Use this skill whenever the user wants to create an agent, build an automation workflow, set up a Claude Code subagent, design a multi-agent system, or says things like 'build me an agent for...', 'automate this with Claude Code', 'I want a subagent that...', 'help me create a workflow', 'set up an agent pipeline', or any variation of wanting Claude Code to do something specialized. Also trigger when the user mentions agent architecture, agent SDK, agentic workflows, or task delegation in Claude Code — even if they don't use the word 'agent' explicitly.

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。

Remove AI-writing tells from README, docs, and user-facing text. Must be done line-by-line, no regex. Use after agents write any user-visible prose.

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 a polished markdown plan into self-contained beads in .beads/beads.jsonl. Every bead must be executable without reopening the plan. Use after the plan has converged.

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-of-session ritual. Close finished beads, file new ones for remaining work, run verify, commit in logical groupings, push. Work is not complete until git push succeeds.

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。

Run the full Forge verification suite (format:check + lint + typecheck + build + test). Use this before claiming any task complete or before opening a PR.

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。

Remove telltale AI-writing patterns from README, docs, and user-facing text. Must be done line-by-line, manually. No regex. Use after agents write any user-visible prose.

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-of-session ritual — close finished beads, file new ones for remaining work, run verify, commit in logical groupings, push. Work is not complete until git push succeeds.

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。

30 → 5 → 15 funnel for brainstorming new features on the existing Forge codebase. Use when adding capability to a mature install, not for greenfield.

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 a polished markdown plan into self-contained beads in .beads/beads.jsonl. Treat this as a distinct translation problem, not task extraction. Use after /plan-synthesize produces a stable hybrid plan.

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 persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases.

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。

Run Karpathy-style autoresearch optimization on any content. Generates 50+ variants, scores with a 5-expert simulated panel, evolves winners through multiple rounds, outputs optimized version + full experiment log. Use when optimizing landing pages, email sequences, ad copy, headlines, form pages, CTA text, or any conversion-focused content. Triggers on "optimize this page", "run autoresearch", "score these variants", "A/B test this copy".

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。

AI-powered revenue intelligence: sales call insight extraction, content-to-revenue attribution, and multi-source client reporting. Use when asked to analyze sales calls, build revenue attribution models, or generate client reports.

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。

AI-powered conversion rate optimization: landing page audits, CRO scoring, survey segmentation, and lead magnet generation. Use when asked to audit a landing page, run a CRO analysis, segment survey responses, or generate lead magnets. For content quality scoring see content-ops.

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 building data pipelines, ETL jobs, event processors, message-broker producers/consumers, application caching layers, database migrations, BigQuery queries, or event-sourcing handlers in TypeScript. Triggers on edits to indexer/ETL/migration code, Prisma or Drizzle schemas, BigQuery integration code, RabbitMQ/Kafka/SQS/BullMQ producer or consumer code, Redis cache wrappers, or mentions of "data engineering", "ETL", "pipeline", "indexer", "event sourcing", "data warehouse", "data migration", "message queue", "RabbitMQ", "Kafka", "SQS", "BullMQ", "Redis cache", or "caching".

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。

Catalog APIs, persistence models, and message/event payloads into DATA_MODEL.md at the project root. Use after implementation when a diff touches request/response types, schemas, ORM models, queue payloads, or webhook shapes. Triggers on "document data model", "update DATA_MODEL", "catalog API shapes".

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 implementing analytics with PostHog in a TypeScript app — capturing events, identifying users, adding feature flags, tracking errors, or wiring API lifecycle telemetry. Triggers on edits to analytics integration files, or mentions of "PostHog", "analytics", "feature flag", "event tracking", "capture", "identify", "A/B test", or "experiment".

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 writing, reviewing, or architecting Rust code — systems programming, async services, CLI tooling, web backends, or any work in `.rs` files. Triggers on editing `.rs` or `Cargo.toml` files, or mentions of "Rust", "Tokio", "Axum", "cargo", "borrow checker", "lifetime", "trait object", "async Rust", "crate", "rustc", or explicit requests to "build this in Rust" / "rewrite X in Rust". For adversarial security review of Rust code see security-reviewer.

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 building games or interactive software in Phaser 3 with TypeScript — scenes, gameplay code, input, Arcade physics, animations, audio, tweens, asset preloading, save systems, performance work, or scaffolding with Vite. Triggers on "Phaser", "Phaser 3", "Phaser.Scene", "Phaser.Game", "Arcade physics", "Matter physics", "tilemap", "Tiled", "GameObject", "Container", "Group", "Vite + Phaser", or any `.ts`/`.js` file in a Phaser project. For game design and balance see game-systems-designer, game-balancer; for game intake see game-design-shaper. For Godot/C# see godot-engineer.

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。

Estimate the full development cost of an existing codebase from lines of code, architectural complexity, and team-composition overhead. Use for 'how much would this cost to build', 'what did this codebase cost', development-cost or build-cost estimates, calendar-time estimates, and Claude/AI ROI on a delivered codebase. Estimates by measured LOC and complexity, not by ticket volume.

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。