「subagents」の検索結果

Full Ad Strategy Orchestrator. Launches 5 parallel subagents to build a complete advertising strategy from a single URL — audience personas, creative concepts, funnel architecture, competitive intelligence, and budget allocation. Produces a composite Ad Readiness Score (0-100) with a unified, client-ready strategy report.

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。

60-Second Ad Readiness Snapshot — quick assessment without subagents

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Create GRACE 4 worker and reviewer subagent presets that understand .grace artifacts, scopes, assertions, and verification evidence.

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Profile and debug Hermes sessions from their JSONL transcripts. Find a session and its subagents, build a queryable event table, summarize the work as a hierarchical table of contents, break down wall time, inference, tools, tokens, and estimated cost per agent, identify errors and improvement opportunities, and export a shareable Perfetto trace. Use when a user wants to inspect what a Hermes session did, debug agent or subagent activity, understand session cost or latency, create a session timeline, generate a trace, or learn how to improve the next agent run.

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Provider-neutral deep-research orchestration for brainstorming, technology choices, comparisons, and state-of-the-art surveys. The Research Lead makes scope and decision calls while native subagents scout, search, verify, synthesize, audit, write, and commit every research artifact.

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Orchestrate a MULTI-PHASE, dependent, or resumable run over many cc-fleet PROVIDER subagents from a JS script, off the main context (`cc-fleet workflow`). Use for fan-out→barrier→synthesis, per-item pipelines, loop-until-dry, or a run that must survive a kill and `--resume` from its journal. Trigger ONLY to run a cc-fleet provider workflow. NOT a flat fan-out of independent tasks (that is /cc-fleet:subagent — cheaper, no script); NOT trivial single-shot work; NOT for editing or researching cc-fleet's own workflow 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。

Orchestrate a MULTI-PHASE, dependent, or resumable run over many provider subagents from a JS script, off the main context (`cc-fleet workflow`). Use for fan-out→barrier→synthesis, per-item pipelines, loop-until-dry, or a run that must survive a kill and `--resume` from its journal. NOT a flat fan-out of independent tasks (that is /cc-fleet:subagent — cheaper, no script); NOT interactive collaboration (that is /cc-fleet:team); NOT trivial single-shot work for the main session.

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Run a one-shot or flat parallel batch of provider LLM subagents (headless `cc-fleet subagent`) that return a result. Use when fanning out N independent tasks, doing bulk per-file work, or calling a specialized provider model (DeepSeek / GLM / Kimi / Qwen / MiniMax). NOT a long-lived collaborator you message back and forth (that is /cc-fleet:team); NOT a multi-phase pipeline with dependencies or resume (that is /cc-fleet:workflow); NOT trivial work the main session should just do.

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。

Fan out a one-shot or flat parallel batch of cc-fleet PROVIDER subagents (headless `cc-fleet subagent`) that return a result — DeepSeek / GLM / Kimi / Qwen / MiniMax, or a Codex/Claude subscription. Trigger ONLY to delegate a task to a cc-fleet provider worker (parallel research, bulk per-file work, a specialized model). Do NOT trigger for a multi-phase pipeline (that is /cc-fleet:workflow), for ordinary local shell/coding, or for editing or researching cc-fleet's own code.

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Repo-local workflow skill for large coding tasks. Initializes .agent/tasks/TASK_ID artifacts, installs project-scoped Codex and Claude subagents, updates AGENTS.md plus the repo's Claude guide file with the workflow, and runs a spec-freeze → build → evidence → verify → fix loop with fresh-session verification.

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AI SDK for Python (the `ai` package). Use for Python model calls (LLMs, image, video, speech, embedding, transcription, reranking, or evaluation), agents, model interaction tests, tool calling, subagents, approvals, durable execution, telemetry, AI SDK UI backends, and custom providers.

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60-Second Property Snapshot — quick assessment without subagents for fast property evaluation with signal, key factors, and CTA for full analysis

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Adversarially review the current implementation plan with three independent critic subagents, reconcile their findings into a consensus, and apply the agreed feedback to the plan. Use after drafting a plan (in plan mode or a plan/design doc) and before starting implementation, or whenever the user asks to pressure-test, critique, or harden a plan.

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Analyze an open-source project from What/Why perspective (not how-it's-implemented). Use when the user says "/analyze-oss", "분석해줘 이 오픈소스", "이 레포 뭐하는거야", "analyze this repo", "what does X do", "이거 왜 쓰는거야", "이 라이브러리 분석", provides a GitHub URL and wants understanding, or asks to deeply understand an OSS project's purpose, value, target users, and usage flow. Clones the repo to ~/opensource-analysis/<repo-name>/ (git pull if already exists), dispatches parallel subagents per analysis lens, then synthesizes a What/Why-focused report in chat. Supports optional user-specific follow-up questions.

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。

Deep web research skill using parallel subagents + chromux browser-explorer + Gemini. Spawns multiple WebSearch research agents AND browser-explorer agents (via chromux for JS-heavy/dynamic sites), plus a Gemini CLI deep research source, then synthesizes everything into a cited report. Uses WebSearch, WebFetch, chromux browser-explorer, and Gemini CLI. Invoke with /deep-research <topic>.

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Apply when context is filling up: large outputs, long files, repeated reads, fan-out planning. Route bulk to subagents; keep summaries in the main thread, not raw payloads.

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Apply to any non-trivial work, not just bulk work: edits, migrations, analyses, checks. Build the tool that does it or proves it (codemod, script, generator, or a skill your subagents follow) instead of working by hand. The tool is the artifact a reviewer can rerun.

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Audits and reorganises what an AI coding agent carries in its context window: instruction files, path-scoped rules, skills, subagents, hooks and the running conversation. Use when someone says "the agent ignores our CLAUDE.md", "set this repo up for Claude Code / Codex / Cursor", "my AGENTS.md is too long", "quality drops in long sessions", "it keeps inventing APIs", "what should go in a skill versus the rules file", or asks how to hand work to a subagent. Grounded in the documented loading behaviour of Claude Code, with the equivalents for Codex, Gemini CLI and Cursor.

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Make hand-painted watercolour-and-ink cartoon videos (MP4) with code — p5.js + p5.brush rendered frame by frame in headless Chrome, encoded with ffmpeg — starring Clawd or any character. Use when the user asks to make an animation, animated short, cartoon, music video, lyric video, animated GIF/loop or "a video of Clawd…", or mentions the PDoomVideo / ClaudeAnimationBase approach. Covers storyboarding, the engine API, the render-and-look review loop, music/lyric sync and splitting long videos across subagents.

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Full SEO audit powered by the DataForSEO API. Orchestrates 5 specialist subagents (keywords, technical, competitors, content, backlinks) hitting DataForSEO Keywords Data, SERP, On-Page, Backlinks, and Labs endpoints in parallel, then produces a composite SEO Score (0-100) with prioritized action plan and audit JSON ready for PDF export.

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Fans Claude work out over the Anthropic API from surfaces with no native subagents, chiefly claude.ai chat and projects, with parallel calls with a shared cached context, streaming, long-lived conversation threads with cached history, and named agent pools; also delegates to Gemini through the Cloudflare AI Gateway on any surface. Use for multi-perspective analysis, a panel of reviewers, or task decomposition from claude.ai, or for Python code that calls several Claude instances concurrently over the API.

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。

60-Second Agency Snapshot — rapid 5-dimension assessment without subagents, outputs a compact scorecard

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Exhaustive problem space exploration using the MIT Synthetic Neurobiology "tiling tree" method. Partitions a problem into MECE (Mutually Exclusive, Collectively Exhaustive) subsets recursively via parallel subagents, then evaluates leaf ideas against specified criteria. Use when users say "tiling tree", "tile the solution space", "exhaustively explore approaches to", "what are all the ways to", or request a MECE breakdown of a problem. Requires orchestrating-chat-agents skill.

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DEPRECATED, renamed to orchestrating-chat-agents. Kept so existing imports of its scripts keep working; every module here loads the same module from orchestrating-chat-agents. Use orchestrating-chat-agents for parallel Claude calls over the API from claude.ai, and dispatching-subagents for fan-out in Claude 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。