skill-creator

Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

1.74x

Quality

90%

Does it follow best practices?

Impact

94%

1.74x

Average score across 3 eval scenarios

Securityby

Low

Low-risk findings worth noting

Quality

Content

81%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 thorough, highly actionable skill-creation workflow with clear sequencing and strong feedback loops, and it makes good use of one-level-deep references. Its main weakness is conciseness — chatty asides and repetition inflate the length, and some inlined schemas belong in the existing reference file.

Suggestions

Trim conversational filler ('Cool? Cool.', the plumber/grandparent tangent, 'billions a year in economic value', and the verbatim repeat of the core loop at the end) to tighten conciseness.

Replace the inlined timing.json, eval_metadata.json, and feedback.json schema blocks with a pointer to references/schemas.md, keeping only one representative example inline.

Consolidate the Cowork/Claude.ai-specific notes so environment differences are stated once rather than re-explained across sections, including the all-caps viewer reiteration.

Dimension	Reasoning	Score
Conciseness	The bulk of the body is efficient instructional content, but it is interspersed with several padded conversational asides ('Cool? Cool.', the plumber/grandparent tangent, 'we are trying to create billions a year in economic value here!') and repeats the core loop verbatim at the end, so it could be noticeably tightened.	3 / 5
Actionability	Fully executable guidance throughout — complete JSON blocks for evals.json, eval_metadata.json, timing.json and feedback.json, plus copy-paste bash commands (aggregate_benchmark, generate_review.py, run_loop, package_skill) covering the common cases.	5 / 5
Workflow Clarity	The multi-step process is clearly sequenced (Capture Intent → Interview → Write SKILL.md → Test Cases → 5-step eval run → improve → iterate → description optimization) with explicit feedback loops (run → review → improve → repeat) and grading-based validation checkpoints plus termination criteria.	5 / 5
Progressive Disclosure	References are one-level-deep and clearly signaled ('See references/schemas.md', 'Read the template from assets/eval_review.html', 'Read agents/grader.md'), but the body inlines several full JSON schemas despite a dedicated references/schemas.md and runs ~480 lines, leaving minor organization gaps.	4 / 5
	Total	17 / 20 Passed

Description

100%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 is exemplary: it states concrete capabilities, provides an explicit 'Use when...' clause with natural trigger phrasings and synonyms, and carves out a distinct meta-skill niche with low conflict risk. It reads like one of the rubric's good examples.

Dimension	Reasoning	Score
Specificity	Lists multiple specific concrete actions — 'Create new skills, modify and improve existing skills, and measure skill performance' plus 'run evals', 'benchmark skill performance with variance analysis', and 'optimize a skill's description' — giving comprehensive coverage rather than just naming the domain.	5 / 5
Completeness	Explicitly answers both what ('Create new skills, modify and improve existing skills, and measure skill performance') and when ('Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals...') with concrete trigger phrases.	5 / 5
Trigger Term Quality	Natural phrasings a user would actually say are well covered with synonyms — 'create a skill from scratch', 'edit, or optimize an existing skill', 'run evals to test a skill', 'benchmark skill performance', 'optimize a skill's description for better triggering accuracy'.	5 / 5
Distinctiveness Conflict Risk	Occupies a clear meta-skill niche (skill creation/improvement/evaluation) with distinct triggers (evals, benchmarking, description optimization) and minimal overlap risk with other skills.	5 / 5
	Total	20 / 20 Passed

Validation

100%

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 — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository: av/harbor
Path: .agents/skills/skill-creator/SKILL.md
Commit: 67cbe66

Reviewed: 2 days ago

Table of Contents

Discovery Implementation Validation

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.