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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. Use when: crewai, multi-agent team, agent roles, crew of agents, role-based agents.

56

Quality

70%

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tessl review fix ./cli-tool/components/skills/ai-research/crewai/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

53%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A well-organized, pattern-based reference with good anti-pattern guidance, but its core code example is literally cut off mid-line, secondary examples reference undefined objects and unimported classes, and persona/expansion prose inflates the token budget. Adding a sequenced setup workflow and fixing the truncated example would lift it substantially.

Suggestions

Fix the truncated first code example: 'return Task(config' ends mid-expression - complete the writing_task method and the @crew method so the flagship pattern is copy-paste runnable.

Make the hierarchical and planning examples self-contained: import/define ChatOpenAI (or use crewai's LLM config), and define the task variables (research_task, analysis_task, writing_task) they reference.

Replace the persona preamble ('You are an expert... You think in terms of...') and the 'With planning enabled' numbered explanation with a brief end-to-end workflow: install crewai, write agents.yaml/tasks.yaml, define the CrewBase class, run crew.kickoff(), and inspect crew.usage_metrics or task output for verification.

DimensionReasoningScore

Conciseness

Mostly efficient pattern-oriented content, but the persona preamble ('You are an expert in designing collaborative AI agent teams. You think in terms of roles, responsibilities, and delegation...') and the planning explanation ('CrewAI generates step-by-step plan... Agents see overall structure') restate things Claude can infer or doesn't need. Matches anchor 3: mostly efficient with some unnecessary explanation that could be tightened.

3 / 5

Actionability

Concrete code and YAML examples exist, but the flagship 'Basic Crew with YAML Config' example is truncated mid-statement ('return Task(config'), the hierarchical example uses ChatOpenAI without importing it and references undefined research_task/analysis_task variables, and no install/run commands are given. This matches anchor 3: some concrete guidance but incomplete with missing key details, not anchor 4's 'minor gaps'.

3 / 5

Workflow Clarity

Patterns carry 'When to use' notes and progress from basic to hierarchical to planning, but there is no sequenced workflow for standing up a crew (install, write configs, run, verify output) and no checkpoints for validating that a crew produced the expected output. Fits anchor 3: sequence/checkpoints only implicit rather than anchor 4's explicit clear sequence.

3 / 5

Progressive Disclosure

No bundle files exist, and the ~240-line body is well-sectioned (Capabilities, Requirements, Patterns with When-to-use, Anti-Patterns, Limitations, Related Skills) with no nested references and easy navigation. Minor gap: the long YAML config and multi-code-block examples could be split into reference files, keeping it at anchor 4 rather than 5.

4 / 5

Total

13

/

20

Passed

Description

75%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A solid description with clear domain coverage and an explicit 'Use when' trigger clause containing natural keywords. It is held back from the top level by feature-list framing instead of concrete actions, keyword-only trigger phrasing, and marketing fluff (the '60% of Fortune 500' over-claim).

Suggestions

Cut the unverifiable marketing claim ('the leading role-based multi-agent framework used by 60% of Fortune 500 companies') and the filler sentence 'Essential for building collaborative AI agent teams' - they add tokens without adding information.

Reframe the capability list as concrete actions, e.g. 'Design agents with roles, goals, and backstories; define tasks with expected outputs; orchestrate crews with sequential or hierarchical processes; configure memory; build flows.'

Add situational phrasing and synonyms to the trigger clause, e.g. 'Use when building multi-agent teams, defining agent roles, or orchestrating a crew of AI agents with CrewAI.'

DimensionReasoningScore

Specificity

Lists many specific domain features ('agent design with roles and goals, task definition, crew orchestration, process types (sequential, hierarchical, parallel), memory systems, and flows') rather than generic language, though framed as 'Covers X' feature enumeration instead of concrete actions and padded with the unverifiable 'used by 60% of Fortune 500 companies' over-claim. Fits anchor 4 (several specific items, minor gaps) more than anchor 5 (comprehensive concrete actions).

4 / 5

Completeness

Explicitly answers both what ('Covers agent design... crew orchestration... memory systems, and flows') and when (an explicit 'Use when:' clause with concrete trigger terms). Below anchor 5 because the 'when' is a bare keyword list without situational phrasing like 'when building' or 'when the user mentions', placing it between anchors 4 and 5.

4 / 5

Trigger Term Quality

'Use when: crewai, multi-agent team, agent roles, crew of agents, role-based agents' provides good natural keyword coverage of phrases users would say. Missing common variations such as 'multi-agent workflow', 'agent collaboration', or 'crew of AI agents', so it falls between anchors 4 and 5.

4 / 5

Distinctiveness Conflict Risk

'crewai' names a distinct framework and triggers like 'crew of agents' and 'role-based agents' are framework-specific, giving a mostly distinct niche. Minor overlap risk with other multi-agent framework skills via the generic trigger 'multi-agent team', matching anchor 4.

4 / 5

Total

16

/

20

Passed

Validation

93%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

15

/

16

Passed

Repository

davila7/claude-code-templates

Path

cli-tool/components/skills/ai-research/crewai/SKILL.md

Reviewed

Table of Contents

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