ai-agent-development

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

Quality

60%

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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-structured, token-efficient workflow skeleton with clear phase sequencing, but it reads as a table of contents rather than a guide: every action is an abstract directive with no executable code, commands, or concrete framework-specific guidance. Validation is limited to an end-of-workflow checkbox list with no feedback loops between phases.

Suggestions

Add concrete executable content — e.g. minimal CrewAI crew and LangGraph graph code snippets, or install/run commands — so at least the core phases have copy-paste-ready guidance instead of directives like 'Implement agent logic'.

Add per-phase validation checkpoints with error-recovery guidance (e.g. 'run the agent eval suite; if below threshold, revisit Phase 1 metrics') rather than only an end-of-workflow checklist.

Replace the duplicated 'Skills to Invoke' + 'Copy-Paste Prompts' blocks with a single reference per phase, and clarify where the ~14 referenced sibling skills actually live.

Dimension	Reasoning	Score
Conciseness	The body is terse and assumes Claude's competence — no basic-concept explanations — but the per-phase 'Copy-Paste Prompts' blocks largely duplicate the 'Skills to Invoke' lists, which is trimmable redundancy keeping it below the lean 5 anchor.	4 / 5
Actionability	Actions are high-level directives like 'Define agent purpose', 'Implement agent logic', and 'Design memory structure' with no executable code, commands, or concrete API usage; the only 'copy-paste' content is one-line invocations of other skills, matching the 'minimal concrete guidance, missing specific steps' anchor.	2 / 5
Workflow Clarity	The seven phases are clearly sequenced and a quality-gates checklist exists, but per-phase 'Test...' steps lack validation criteria and there are no feedback loops (what to do when a test fails), matching the 'steps listed but checkpoints missing or implicit' anchor.	3 / 5
Progressive Disclosure	No bundle files exist, and the body is well-organized with consistent headers, an architecture diagram, quality gates, and limitations; the gap is that ~14 sibling skills are referenced by bare backtick names with no signaling of where they live or how to navigate to them.	4 / 5
	Total	13 / 20 Passed

Description

66%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 reasonably specific description with good natural trigger terms and a distinct agent-development niche anchored by named frameworks. Its main weakness is the absence of any 'Use when...' trigger clause, leaving the 'when to use' question unanswered, and its reliance on a single generic action verb.

Suggestions

Add an explicit 'Use when...' clause, e.g. 'Use when building autonomous agents, multi-agent systems, or orchestrating agent workflows with CrewAI or LangGraph.'

Replace the single generic verb 'building' with more distinct concrete actions (e.g. 'design, implement, and orchestrate') to strengthen specificity.

Add common user synonyms such as 'agent framework', 'LangChain', or 'agent workflow' to broaden trigger-term coverage.

Dimension	Reasoning	Score
Specificity	The description lists several concrete deliverables ('autonomous agents, multi-agent systems, and agent orchestration') and names specific frameworks ('CrewAI, LangGraph, and custom agents'), but the only action verb is the generic 'building', so it falls between the 3 and 5 anchors rather than achieving comprehensive multi-action coverage.	4 / 5
Completeness	It clearly answers 'what' (builds autonomous agents, multi-agent systems, orchestration with named frameworks) but contains no 'Use when...' clause or equivalent trigger guidance, which per the judging guidelines caps completeness at 3.	3 / 5
Trigger Term Quality	Natural terms users would say are present ('autonomous agents', 'multi-agent systems', 'agent orchestration', 'CrewAI', 'LangGraph'), but common variations like 'agent framework', 'LangChain', or 'agent workflow' are missing, matching the 'good coverage, a few natural terms missing' anchor.	4 / 5
Distinctiveness Conflict Risk	The named frameworks and agent-specific scope create a mostly distinct niche with clear triggers, though there is minor overlap risk with closely related skills like 'autonomous-agents' or generic 'ai-ml' workflows.	4 / 5
	Total	15 / 20 Passed

Validation

93%

Warnings & errors only

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

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	15 / 16 Passed

Repository: boisenoise/skills-collections
Path: skills/antigravity-ai-agent-development/SKILL.md
Commit: 7390864

Reviewed: 5 days ago

Table of Contents

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