Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add agentsope/SkillAlchemy --skill agentsop-crewaigit clone --depth 1 https://github.com/agentsope/SkillAlchemyWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/agentsope/skillalchemy/agentsop-crewai)<a href="https://agentmods.dev/skills/agentsope/skillalchemy/agentsop-crewai"><img src="https://agentmods.dev/badge/skills/agentsope/skillalchemy/agentsop-crewai.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00045 | $0.07100 |
| Opus 5 | $0.00023 | $0.03550 |
| Sonnet 5 | $0.00009 | $0.01420 |
| Haiku 4.5 | $0.00005 | $0.00710 |
Grade A, and why
agentsop-crewai scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 495 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CrewAI SOP — Role-Based Multi-Agent Orchestration
框架口号: "Framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly." [github.com/crewAIInc/crewAI]
1. 何时激活 (When to Activate)
1.1 直接信号 (Direct triggers)
- 用户说 "我需要 researcher + writer + reviewer 这种团队配合"
- 用户说 "用 CrewAI 实现 / 我已经在用 crew.kickoff()"
- 任务可以拆为 2–5 个专业角色，且每个角色有明确职责边界
- 流程是线性 pipeline（数据→分析→报告）或轻度分支
1.2 反向信号 (Skip CrewAI when)
- 单 agent + tool-use 就够 → 直接用 SDK / Instructor（CrewAI 是 over-engineering）
- 需要状态图 + 循环 + 中断恢复 → LangGraph 更合适
- 需要 agents 之间自由对话辩论 → AutoGen 更合适
- 需要严格条件路由（"if X then only A else B"）→ 用 CrewAI Flows 而非 hierarchical Crew，或直接 LangGraph
- 延迟敏感（<500ms） → 多 agent 编排不适合
1.3 心智门槛 (Mental check)
"An agent needs agency, otherwise it's just another script." — João Moura, CrewAI 创始人 [softwareengineeringdaily.com/2025/06/03/crew-ai-with-joao-moura/]
如果你能用 if/else 提前写死流程，不要用 Crew。Crew 的本质是把"决策权"让渡给 LLM 角色。
2. 核心心智模型 (Mental Model)
2.1 四元抽象 (The 4 primitives)
Agent (role + goal + backstory) ← 谁
↓ 持有
Task (description + expected_output + agent + context) ← 做什么
↓ 组装
Crew (agents + tasks + process) ← 怎么协作
↓ 选择
Process (sequential | hierarchical) + Flow (event-driven 编排) ← 控制流
2.2 为什么 "role + goal + backstory" 三件套？
CrewAI 的核心假设：LLM 在 role-playing 状态下表现更好。
- role: 函数性身份 ("Senior Data Researcher") — 决定 prompt 主语
- goal: 个体目标 ("Uncover cutting-edge developments in {topic}") — 决定决策方向
- backstory: 经验/性格 ("You're a seasoned researcher with a knack for…") — 校准语气与判断风格
"Backstory provides depth to the agent's persona, enriching its motivations and engagements within the crew." [docs.crewai.com/en/concepts/agents]
关键洞察: backstory 不是装饰。它是 system prompt 的最大杠杆——同一个 role+goal，换 backstory 会显著改变产出质量与风格。
2.3 Sequential vs Hierarchical vs Flow
| 维度 | Sequential | Hierarchical | Flow |
|---|---|---|---|
| 任务路由 | 静态列表顺序 | manager LLM 动态分派 | @listen 事件驱动 |
| 控制力 | 高 (写死顺序) | 低 (manager 自由发挥) | 最高 (代码 + 状态) |
| Token 开销 | 1× 基线 | 1.3–1.5× (manager overhead) | 接近 1× |
| 调试难度 | 低 | 高 (manager 黑盒) | 中 |
| 何时用 | 80% 场景默认 | 真正需要动态分派 | 复杂分支 + 多 Crew 编排 |
| 已知坑 | task context 自动透传可能膨胀 | manager 会"执行所有 task"而非"按需调用" | 学习曲线 + 状态设计 |
What ships with it
7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 7d ago First seen · 495 lines · 45 tokens per session scan A fa9426934f9d
agentsop-crewai is a skill published in the GitHub repository agentsope/SkillAlchemy (366 stars, last pushed 4d ago), licensed MIT. It adds 45 tokens to every session and 7,100 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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