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 magnus919/agent-skills --skill crewaigit clone --depth 1 https://github.com/magnus919/agent-skillsWrote 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/magnus919/agent-skills/crewai)<a href="https://agentmods.dev/skills/magnus919/agent-skills/crewai"><img src="https://agentmods.dev/badge/skills/magnus919/agent-skills/crewai/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/magnus919/agent-skills/crewai"><img src="https://agentmods.dev/badge/skills/magnus919/agent-skills/crewai.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00070 | $0.01520 |
| Opus 5 | $0.00035 | $0.00760 |
| Sonnet 5 | $0.00014 | $0.00304 |
| Haiku 4.5 | $0.00007 | $0.00152 |
Grade A, and why
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 9d 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 — 146 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CrewAI Expert Skill
CrewAI is a framework for role-based multi-agent orchestration. Unlike LangGraph's low-level state-machine graphs, CrewAI provides a higher abstraction: agents are defined as Roles with Goals and Backstories, crews are composed with built-in sequential or hierarchical workflows, and inter-agent delegation is built into the framework.
Core Paradigm
from crewai import Agent, Task, Crew, Process
from crewai.tools import tool
@tool("search")
def search_web(query: str) -> str:
"""Search the web for information."""
return f"Results for: {query}"
researcher = Agent(
role="Senior Researcher",
goal="Find accurate information on any topic",
backstory="Expert researcher with 10 years of experience",
tools=[search_web],
verbose=True,
)
writer = Agent(
role="Technical Writer",
goal="Write clear reports from research findings",
backstory="Experienced technical writer",
verbose=True,
)
research_task = Task(
description="Research the topic thoroughly",
expected_output="A detailed research brief",
agent=researcher,
)
write_task = Task(
description="Write a report based on research",
expected_output="A well-structured report",
agent=writer,
)
crew = Crew(
agents=[researcher, writer],
tasks=[research_task, write_task],
process=Process.sequential,
verbose=True,
)
result = crew.kickoff()
Core Principles
- Agents are Roles, not functions. Role + Goal + Backstory defines the agent's identity. Strong role definitions reduce hallucination.
- Tasks declare what, not how. Description + expected_output defines the task. The agent figures out execution.
- Sequential is for pipelines, Hierarchical is for complexity. Sequential runs tasks in order. Hierarchical uses a manager agent to delegate and validate.
- Manager LLM is required for Hierarchical. Without
manager_llm, hierarchical process fails silently. - Delegation loops are real.
allow_delegation=Truewithoutmax_iterbounds can cause infinite handoffs. - Tool errors don't raise. A failed tool call marks the task as failed but doesn't raise an exception. Check task output.
What ships with it
15 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.
- evals/evals.json 2.7 KB
- README.md 1.6 KB
- references/agent-design.md 2.0 KB
- references/callbacks.md 817 B
- references/crew-patterns.md 1.7 KB
- references/faq-and-troubleshooting.md 1.2 KB
- references/flows.md 1.6 KB
- references/memory-system.md 1.6 KB
- references/task-design.md 1.6 KB
- references/tool-integration.md 1.4 KB
- references/validation-audit.md 1.4 KB
- scripts/check-setup.py 652 B runs code
- templates/hierarchical-crew.py 1.2 KB runs code
- templates/research-crew.py 1.2 KB runs code
- templates/support-crew.py 1.4 KB runs code
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.
- 9d ago Changed · +1 lines · +13 tokens per session 81f2c3dce351
- 12d ago First seen · 145 lines · 57 tokens per session scan A dab14d929afd
crewai is a skill published in the GitHub repository magnus919/agent-skills (76 stars, last pushed yesterday), licensed MIT. It adds 70 tokens to every session and 1,520 once invoked, about $0.0003 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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