Self-Evolving Pro
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Please install @user_b392afca/self-evolving-pro according to https://skillhub.cn/install/skillhub.md
About this skill
Problem
Many AI agents keep only the session output after a task. Failures, tool quirks, and script defects are not converted into reusable constraints, so later runs may repeat the same mistakes or treat one-off project details as permanent rules.
How It Works
The skill uses a Reflect → Research → Record → Evaluate → Confirm → Modify → Version lifecycle:
- Automatic logging: after debugging, failed runs, multi-step work, or new API and tool patterns, it records a reflection first.
- External validation: before a full update, it searches papers and community sources to avoid promoting ad hoc experience as a general best practice.
- Controlled editing: it presents diffs for SKILL.md, scripts, or reference docs, waits for user confirmation, prefers appending over deletion, and writes a version log.
It fits long-lived agent workflows, script-based skills, and engineering tasks that need accumulated operational knowledge. Avoid it for one-off Q&A, raw business-data cleanup, or environments where agents should not modify their own instructions.
Use Cases
- After repeatedly debugging external API errors in an agent workflow, record failure causes and call patterns as reusable skill constraints.
- After a multi-step code migration task, capture the working workflow, tool usage, and script defects in an evolution log for later runs.
- Before editing SKILL.md instructions, research external sources and compare findings with existing rules to test whether a new constraint is generalizable.
- In a shared agent skill library, make controlled changes to scripts, references, and version logs so one-off experience does not pollute long-term rules.
Best For
- AI engineers who need agents to capture operational lessons after failed tasks.
- Technical leads maintaining shared agent skill libraries and worried about ad hoc rules spreading.
- Automation platform developers who want to convert debugging conclusions into auditable skill constraints.
- Agent application developers who need long-lived script-based skills and clear version tracking.
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