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agent-builder

Design and build AI agents for any domain. Use when users: (1) ask to "create an agent", "build an assistant", or "design an AI system" (2) want to understand agent architecture, agentic patterns, or autonomous AI (3) need help with capabilities, subagents, planning, or skill mechanisms (4) ask about Claude Code, Cursor, or similar agent internals (5) want to build agents for business, research, creative, or operational tasks Keywords: agent, assistant, autonomous, workflow, tool use, multi-step, orchestration

Quality

82%

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SKILL.md
Quality
Evals
Security

Quality

Content

65%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 body is a well-structured conceptual overview with excellent progressive disclosure to verified bundle files, but it is noticeably repetitive — the trust-the-model theme is restated many times — and never presents an explicit ordered build workflow, leaving the reader to assemble the steps from the references. Actionable design guidance (numeric capability limits, decision tables) is a strength.

Suggestions

Cut the 'Key Principles' section — items 1-3 and 5 restate The Three Elements and the trust philosophy already covered; merge any unique wording into those sections to tighten conciseness.

State the core 'trust the model, don't over-engineer' message once (e.g., in The Core Philosophy) and remove its repetitions from the CRITICAL callout, Anti-Patterns rows, and closing section.

Add an explicit ordered build sequence, e.g.: 1. Run `python scripts/init_agent.py <name>` 2. Pick 3-5 capabilities from `references/tool-templates.py` 3. Start from the loop in `references/minimal-agent.py` 4. Add complexity only per the Progressive Complexity table — turning the philosophy into an executable workflow.

DimensionReasoningScore

Conciseness

The core message ("trust the model, don't over-engineer") is repeated roughly five times — the opening blockquote, the "CRITICAL: Trust the model" callout, Key Principles #1/#5, the Anti-Patterns table, and the closing sections — and "Key Principles" largely restates "The Three Elements". This is more than the minor trimming of anchor 4, but the body never explains concepts Claude already lacks, so it stays above anchor 2.

3 / 5

Actionability

Concrete design directives are present ("Start with 3-5 capabilities", the Progressive Complexity table with explicit when-to-add triggers, the anti-patterns table) and point to complete working code in the bundle. Not anchor 5 because the body itself contains no copy-paste-ready code or commands; not anchor 3 because the guidance is specific and complete rather than pseudocode-level.

4 / 5

Workflow Clarity

The design-thinking checklist and complexity table provide a framework, but the actual build sequence (scaffold with init_agent.py, pick tools from tool-templates.py, start from minimal-agent.py, iterate) is never laid out as an ordered procedure — the sequence and checkpoints are implicit, matching anchor 3. No destructive/batch operations, so no validation cap applies.

3 / 5

Progressive Disclosure

The body is a concise overview with a categorized Resources section giving one-line descriptions of one-level-deep references (agent-philosophy.md, minimal-agent.py, tool-templates.py, subagent-pattern.py, scripts/init_agent.py), all of which exist in the bundle and match their labels. Clear overview, well-signaled references, easy navigation — anchor 5.

5 / 5

Total

15

/

20

Passed

Description

91%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 strong description: it clearly states what the skill does and enumerates five explicit, naturally-phrased trigger scenarios plus a synonym keyword line. The only weaknesses are high-level action descriptions and a few generic keywords that slightly raise conflict risk with workflow/automation skills.

DimensionReasoningScore

Specificity

The description lists several concrete actions — "Design and build AI agents", "understand agent architecture, agentic patterns", "build agents for business, research, creative, or operational tasks" — with only minor gaps. It falls short of anchor 5 because the actions remain high-level (no mention of testing, evaluation, or deployment), and exceeds anchor 3 because coverage goes well beyond 1-2 actions. Voice is third-person imperative, so no person penalty applies.

4 / 5

Completeness

It explicitly answers both questions: the what ("Design and build AI agents for any domain") and a detailed when ("Use when users: (1)…(5)" with concrete trigger phrases). This is an exact match for the anchor-5 example pattern; no 'Use when' cap applies.

5 / 5

Trigger Term Quality

It quotes the exact natural phrases users would say ("create an agent", "build an assistant", "design an AI system", "ask about Claude Code, Cursor") and adds a synonym-rich keyword line (agent, assistant, autonomous, workflow, tool use, multi-step, orchestration). This matches the anchor-5 comprehensive-coverage-with-synonyms example; only trivial omissions like "bot" or "automation" keep it from being perfect, which is closer to 5 than to anchor 4.

5 / 5

Distinctiveness Conflict Risk

The agent-building niche is clear and triggers are distinct, but the generic keywords "workflow", "multi-step", and "orchestration" create minor overlap risk with workflow/automation-related skills. Mostly distinct with minor overlap — anchor 4, not 5.

4 / 5

Total

18

/

20

Passed

Validation

100%

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

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

shareAI-lab/learn-claude-code

Path

skills/agent-builder/SKILL.md

Reviewed

Table of Contents

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