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spawn

Generate PhD-level expert agent prompts for Claude Code. Creates comprehensive 500-1000 line agents with detailed patterns, code examples, and best practices. Triggers on: spawn agent, create agent, generate expert, new agent, agent genesis.

71

1.84x
Quality

58%

Does it follow best practices?

Impact

94%

1.84x

Average score across 3 eval scenarios

SecuritybySnyk

Advisory

Suggest reviewing before use

Optimize this skill with Tessl

npx tessl skill review --optimize ./data/skills-md/0xdarkmatter/claude-mods/spawn/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

27%

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

This skill is comprehensive in scope but suffers from severe verbosity and redundancy - the same information (file paths, scope options, output formats) is repeated across multiple sections. The 10-part agent template is a useful structural guide, but the skill would benefit enormously from splitting reference material into bundle files and eliminating duplicate content. The workflow lacks validation feedback loops for ensuring generated agents meet quality targets.

Suggestions

Reduce redundancy by stating file paths, scope options, and output formats once in a single reference section, then referring back to it - this alone could cut 30-40% of content.

Extract the 10-part template, YAML frontmatter documentation, and quality checklist into separate bundle files (e.g., TEMPLATE.md, FRONTMATTER.md, CHECKLIST.md) to improve progressive disclosure.

Remove the 12-URL documentation links section - Claude can find these itself, and they add token cost without actionable value.

Add explicit validation steps in the workflow: after generation, verify line count is 500-1000, count code blocks >= 10, count URLs >= 10, and loop back to fix if targets aren't met.

DimensionReasoningScore

Conciseness

Extremely verbose at ~300+ lines with significant redundancy. File paths are repeated 5+ times, scope explanations are duplicated across sections, the documentation links section lists 12 URLs that Claude can find itself, and the quality checklist largely restates the generation requirements. Much content explains concepts Claude already knows (YAML frontmatter, markdown formatting, file creation).

1 / 3

Actionability

Provides a concrete 10-part template structure and clear file path conventions, but the actual agent generation process relies on a vague 'Research official docs' step. The code examples shown are only for file structure/frontmatter, not for the core task of generating agent content. The AskUserQuestion interface is described but not with executable code.

2 / 3

Workflow Clarity

The Implementation Steps section provides a clear sequence for the three modes, and the examples illustrate the interaction flow well. However, there are no validation checkpoints for the generated content quality (e.g., verify line count meets 500-1000 target, verify URL count >= 10). The quality checklist exists but is presented as a static list rather than integrated into the workflow with explicit verify-and-fix loops.

2 / 3

Progressive Disclosure

Everything is in a single monolithic file with no bundle files. The skill references benchmark agents (python-expert.md, react-expert.md) but these aren't provided. The 10-part template, YAML frontmatter documentation, quality checklist, and documentation links could all be split into separate reference files. Content that should be in supporting files is inline, making the skill overwhelming.

1 / 3

Total

6

/

12

Passed

Description

89%

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

This is a solid description that clearly communicates its purpose and includes explicit trigger terms. The main weakness is that the capability description relies on somewhat vague qualifiers ('PhD-level', 'comprehensive', 'best practices') rather than listing more concrete actions. Overall it performs well for skill selection purposes.

Suggestions

Replace vague qualifiers like 'PhD-level' and 'best practices' with more concrete actions, e.g., 'Generates agent prompts with tool definitions, error handling patterns, multi-step workflows, and domain-specific code examples.'

DimensionReasoningScore

Specificity

Names the domain (agent prompt generation) and some actions ('Creates comprehensive 500-1000 line agents with detailed patterns, code examples, and best practices'), but the actions are somewhat vague — 'detailed patterns' and 'best practices' are not concrete enough to fully qualify as specific actions.

2 / 3

Completeness

Clearly answers both 'what' (generates PhD-level expert agent prompts with patterns, code examples, best practices) and 'when' (explicit 'Triggers on:' clause with specific trigger phrases).

3 / 3

Trigger Term Quality

Includes explicit trigger terms ('spawn agent', 'create agent', 'generate expert', 'new agent', 'agent genesis') that are natural phrases a user would say when needing this skill. Good coverage of variations.

3 / 3

Distinctiveness Conflict Risk

The niche of generating expert agent prompts for Claude Code is quite specific and distinct. The trigger terms like 'spawn agent', 'agent genesis' are unlikely to conflict with other skills.

3 / 3

Total

11

/

12

Passed

Validation

90%

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

Validation10 / 11 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

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

Warning

Total

10

/

11

Passed

Repository
NeverSight/skills_feed
Reviewed

Table of Contents

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