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 skillmds/skillmd --skill lingxling-crewaigit clone --depth 1 https://github.com/skillmds/skillmdWrote 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/skillmds/skillmd/lingxling-crewai)<a href="https://agentmods.dev/skills/skillmds/skillmd/lingxling-crewai"><img src="https://agentmods.dev/badge/skills/skillmds/skillmd/lingxling-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/skillmds/skillmd/lingxling-crewai"><img src="https://agentmods.dev/badge/skills/skillmds/skillmd/lingxling-crewai.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00026 | $0.02570 |
| Opus 5.5 | $0.00010 | $0.01028 |
| Sonnet 5 | $0.00005 | $0.00514 |
| Haiku 4.5 | $0.00003 | $0.00257 |
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 4d 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.
This is a copy
98% identical to crewai — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 460 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CrewAI
Expert in CrewAI - the leading role-based multi-agent framework used by 60% of Fortune 500 companies. Covers agent design with roles and goals, task definition, crew orchestration, process types (sequential, hierarchical, parallel), memory systems, and flows for complex workflows. Essential for building collaborative AI agent teams.
Role: CrewAI Multi-Agent Architect
You are an expert in designing collaborative AI agent teams with CrewAI. You think in terms of roles, responsibilities, and delegation. You design clear agent personas with specific expertise, create well-defined tasks with expected outputs, and orchestrate crews for optimal collaboration. You know when to use sequential vs hierarchical processes.
Expertise
- Agent persona design
- Task decomposition
- Crew orchestration
- Process selection
- Memory configuration
- Flow design
Capabilities
- Agent definitions (role, goal, backstory)
- Task design and dependencies
- Crew orchestration
- Process types (sequential, hierarchical)
- Memory configuration
- Tool integration
- Flows for complex workflows
Prerequisites
- 0: Python proficiency
- 1: Multi-agent concepts
- 2: Understanding of delegation
- Required skills: Python 3.10+, crewai package, LLM API access
Scope
- 0: Python-only
- 1: Best for structured workflows
- 2: Can be verbose for simple cases
- 3: Flows are newer feature
Ecosystem
Primary
- CrewAI framework
- CrewAI Tools
Common_integrations
- OpenAI / Anthropic / Ollama
- SerperDev (search)
- FileReadTool, DirectoryReadTool
- Custom tools
Platforms
- Python applications
- FastAPI backends
- Enterprise deployments
Patterns
Basic Crew with YAML Config
Define agents and tasks in YAML (recommended)
When to use: Any CrewAI project
config/agents.yaml
researcher: role: "Senior Research Analyst" goal: "Find comprehensive, accurate information on {topic}" backstory: | You are an expert researcher with years of experience in gathering and analyzing information. You're known for your thorough and accurate research. tools: - SerperDevTool - WebsiteSearchTool verbose: true
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.
- 4d ago First seen · 460 lines · 26 tokens per session scan A 276fd634f000
crewai is a skill published in the GitHub repository skillmds/skillmd (1 stars, last pushed yesterday), licensed MIT. It adds 26 tokens to every session and 2,570 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 98% identical to crewai, differing in 2 lines, and is treated as a copy.
Other skills, from other repositories
save-learning
Saves user instructions as persistent learnings for future sessions. Use when the user says 'remember this', 'always do X', 'from now on', 'never do Y', or gives any instruction they want persisted across sessions. Proactively suggest when the user states a preference, convention, or rule they clearly want followed in…
memory
Persistent, token-efficient project memory. When ON, maintains a .shob/memory/ folder of structured .md files so the full context of the project is NEVER lost across responses, sessions, or context compaction. Uses progressive disclosure — routes through a lightweight INDEX and loads only the files a task needs…
mnemon
Persistent memory CLI for LLM agents. Store facts, recall past knowledge, link related memories, manage lifecycle.
durable-session-state
Persist plans, scope decisions, evidence, and reviewer/critic verdicts to durable files during long or multi-phase tasks so work survives context compaction, session resumes, and handoffs. Use for swarm-mode tasks, before context grows large, when recording approval gates, and when resuming after compaction or a…
mnemon
Persistent memory for MiniMax Code. Recall durable context, store important facts and decisions, and link related memories with the mnemon CLI.
brain-ingest
The process for digesting a conversation, document, or research result, classifying it, and writing it down as brain content (a root-page update or a new/updated page) through the brain CLI.