a-evolve

Apply A-Evolve's agentic evolution methodology to improve AI agent performance across runs. Use when the user wants to diagnose agent failures, generate targeted skills from error patterns, evolve system prompts, or accumulate episodic knowledge. Works standalone or inside AutoResearchClaw pipelines. Triggers on: "evolve", "self-improve", "diagnose failures", "generate skills from errors", "what went wrong and how to fix it", or any mention of A-Evolve.

Quality

88%

Does it follow best practices?

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Securityby

Passed

No findings from the security scan

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.

A well-structured methodology skill with a clearly sequenced, validation-gated workflow and concrete templates. The main improvement is moving some of the larger inline tables and auxiliary sections into reference files to improve token efficiency.

Suggestions

Move the AutoResearchClaw stage-mapping table and MetaClaw section into a separate reference file (e.g. ARC_INTEGRATION.md) and link to it, reducing SKILL.md length.

Trim the inlined api-pagination-handler example SKILL.md to its essential structure, or relocate it to an examples reference.

Consider an explicit reload verification step (e.g. confirming the generated skill file is loadable/parsable) to strengthen the feedback loop.

Dimension	Reasoning	Score
Conciseness	Mostly lean and assumes Claude's competence, but the inlined example SKILL.md and some prose (ARC/MetaClaw tables) could be tightened or moved to references.	4 / 5
Actionability	Provides concrete, copy-paste-ready templates (observation format, skill example, JSON knowledge entry, prompt patch, location tables) with only minor gaps.	4 / 5
Workflow Clarity	Clear 5-step Solve→Observe→Evolve→Gate→Reload sequence with an explicit Gate validation checkpoint and a refine-or-discard feedback loop for error recovery.	5 / 5
Progressive Disclosure	Well-organized with clear headers and no nested references, but at ~140 lines some content (ARC stage table, MetaClaw section) could be split into one-level-deep reference files.	4 / 5
	Total	17 / 20 Passed

Description

95%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 that clearly states both capabilities and trigger conditions in third-person voice, with rich natural trigger phrases. Minor specificity gaps keep it just short of fully comprehensive.

Dimension	Reasoning	Score
Specificity	Names the domain and several concrete actions ('diagnose agent failures, generate targeted skills from error patterns, evolve system prompts, or accumulate episodic knowledge') with only minor coverage gaps.	4 / 5
Completeness	Explicitly answers both what (the methodology and concrete actions) and when ('Use when the user wants to...' plus a dedicated 'Triggers on:' list).	5 / 5
Trigger Term Quality	Comprehensive natural-term coverage including synonyms and quoted user phrases like 'evolve', 'self-improve', 'diagnose failures', and 'what went wrong and how to fix it'.	5 / 5
Distinctiveness Conflict Risk	Clear niche tied to the named A-Evolve methodology and AutoResearchClaw, with distinct triggers and minimal overlap risk with other skills.	5 / 5
	Total	19 / 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: aiming-lab/AutoResearchClaw
Path: .claude/skills/a-evolve/SKILL.md
Commit: be4ba47

Reviewed: 10 days ago

Table of Contents

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