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ai-agent-development

AI agent development workflow for building autonomous agents, multi-agent systems, and agent orchestration with CrewAI, LangGraph, and custom agents.

65

0.97x
Quality

56%

Does it follow best practices?

Impact

97%

0.97x

Average score across 3 eval scenarios

SecuritybySnyk

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./plugins/AI-Agents-Safe-Coding-Skills-claude/skills/ai-agent-development/SKILL.md

The canonical home for this skill is ai-agent-development in sickn33/agentic-awesome-skills

SKILL.md
Quality
Evals
Security

Quality

Content

38%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.

The skill presents a clearly sequenced seven-phase workflow with good navigation and honest pointers to sibling skills, but it delivers almost no executable guidance — no code, commands, or framework-specific details — and its repetitive generic action lists waste tokens. Validation is limited to an unverifiable checkbox list.

Suggestions

Replace generic per-phase actions with concrete, framework-specific guidance (e.g., an actual CrewAI crew definition or LangGraph StateGraph snippet in the relevant phase).

Add explicit validation checkpoints with feedback loops, e.g. how to test agent behavior after Phase 2 and what to do when tests fail, instead of the unchecked 'Quality Gates' list.

Collapse the seven near-identical phase templates into a compact routing table (phase -> skills to invoke -> one prompt), cutting most of the padded length.

DimensionReasoningScore

Conciseness

The body is heavily padded: seven near-identical phase templates whose five one-line actions each ('Define agent purpose', 'Design agent capabilities', 'Test agent behavior') restate what Claude already knows about building agents, conveying little new information across ~170 lines. This matches 'noticeably verbose; several unnecessary explanations or padded sections' rather than the mostly-efficient anchor at 3.

2 / 5

Actionability

There is no code, command, or framework-specific API anywhere; the only semi-concrete artifacts are one-line 'Copy-Paste Prompts' that just invoke other skills and an ASCII architecture sketch. The actions ('Choose agent framework', 'Implement agent logic') are high-level hints missing the specific steps to execute, matching the level-2 anchor rather than the vague-only anchor at 1 because the skill-invocation prompts are at least directly usable.

2 / 5

Workflow Clarity

The seven-phase sequence is clearly ordered and each phase names skills to invoke, but there are no validation checkpoints inside the workflow. The 'Quality Gates' checklist ('Agent logic working', 'Evaluation passing') states outcomes without any way to verify them, matching 'steps listed but validation gaps; checkpoints missing or implicit'.

3 / 5

Progressive Disclosure

The body is well structured with clear per-phase headers, 'Skills to Invoke' sections that clearly signal sibling-skill references, and no bundle files exist to misorganize (no references/, scripts/, or assets/ directories). Navigation is easy and nothing is deeply nested, fitting 'good structure; most content appropriately placed; minor organization gaps' — held back from 5 by the repetitive phase templates that could be condensed.

4 / 5

Total

11

/

20

Passed

Description

57%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.

The description clearly communicates the domain and names relevant frameworks, giving it decent trigger keywords, but it lacks any 'when to use' guidance and describes sub-domains rather than concrete actions. Given the dense ecosystem of similar agent skills it references, it also carries moderate conflict risk.

Suggestions

Add an explicit trigger clause, e.g. 'Use when building autonomous agents, multi-agent systems, or agent orchestration with CrewAI, LangGraph, or custom frameworks.'

Replace sub-domain nouns with concrete actions (e.g. 'design agent roles, implement tool integration, set up agent memory and orchestration graphs').

Add common synonyms users would say, such as 'agent framework', 'LangChain', and 'agent workflow', to improve trigger term coverage.

DimensionReasoningScore

Specificity

The description names the domain ('AI agent development workflow') and lists several items ('building autonomous agents, multi-agent systems, and agent orchestration with CrewAI, LangGraph, and custom agents'), but the 'actions' are mostly naming sub-domains rather than concrete operations like 'extract text' or 'fill forms'. It matches the anchor 'names domain and 1-2 concrete actions, but not comprehensive' more than the level-4 anchor's 'several specific actions'.

3 / 5

Completeness

The 'what' is clearly stated (workflow for building agents, multi-agent systems, orchestration), but there is no 'Use when...' clause or equivalent explicit trigger guidance, which per the rubric caps completeness at 3. It is not a 4 because the 'when' is entirely absent rather than merely imprecise.

3 / 5

Trigger Term Quality

Natural phrases users would say are present: 'autonomous agents', 'multi-agent systems', 'agent orchestration', 'CrewAI', 'LangGraph'. Good coverage, but common variations like 'agent framework', 'LangChain', or 'agent workflow' are missing, so it falls just short of the comprehensive-synonyms anchor at 5.

4 / 5

Distinctiveness Conflict Risk

The description is somewhat specific (framework names CrewAI/LangGraph help), but it spans the whole agent-development space, which the body shows is covered by many sibling skills (crewai, langgraph, autonomous-agents, agent-memory-systems, etc.), creating real overlap risk with those closely related skills. It sits between 'somewhat specific but could still overlap' (3) and 'mostly distinct' (4), landing on 3 given the crowded niche.

3 / 5

Total

13

/

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
administrakt0r/AI-Agents-Safe-Coding-Skills
Reviewed

Table of Contents

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