How do I install the Self Evolving Skills skill?

Run npx skillmds@latest add topprismdata/self-evolving-skills in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Self Evolving Skills safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Self Evolving Skills?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Self Evolving Skills free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Self Evolving Skills?

topprismdata (@topprismdata) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Self Evolving Skills by topprismdata

Name: Self Evolving Skills
Author: topprismdata

Self Evolving Skills

Use when adding new skills to the library, when extracting insights from completed experiments, when consolidating lessons learned, or when running a project retrospective. Triggers after a successful Kaggle competition, after major model breakthrough, or when "we should remember this" comes up.

topprismdata fe23f80 3.5 KB Updated Sep 17, 2026

File contents

Self-Evolving Skills (Voyager-Style)

Context

Static skill libraries grow only when humans add entries. Voyager (Wang et al. NeurIPS 2023) showed agents that auto-extract and register new skills grow 100+ skills without manual work. agy verified this as P1 for ML agents: 43+ skills → 100+ with quality gates.

The core insight: every experiment is a candidate skill; quality gating prevents library pollution.

Guidance

Extract from Text/Conversation

from framework.src.skill_evolution import SkillExtractor

ext = SkillExtractor()
candidates = ext.extract_from_text("""
    We should always check data leakage before training.
    Never use future information in features.
    Make sure to use walk-forward validation for time series.
""")
# → 3 candidates: 1 skill, 1 anti-pattern, 1 principle

Extract from Experiment Log

# 假设实验记录在 EXPERIMENTS.md
candidates = ext.extract_from_experiments_md(experiments_md_content)
# 自动按 importance 排序, top 20 入选

Validate Before Registration

from framework.src.skill_evolution import SkillValidator

val = SkillValidator()
for c in candidates:
    result = val.validate(c, existing_descriptions=existing)
    # verdict: APPROVE / REVISE / REJECT
    # issues: ['Too similar...', 'Missing frontmatter...']
    # similarity_to_existing: 0.0 - 1.0

Register to MCP Library

from framework.src.skill_evolution import SkillRegistry

registry = SkillRegistry(skills_dir="skills/examples")
for c in candidates:
    result = registry.register(c)
    if result.verdict.value == "approve":
        print(f"Registered: {c.name}")
    # 写入 skills/examples/{name}/SKILL.md

Auto-Approve (Production Danger)

# 生产环境慎用 — 应该人工 review
registry.register(candidate, auto_approve=True)

Why This Matters

Without self-evolving:

Skills grow slowly (43+ → manual additions)
Lessons forgotten after session ends
Same mistakes repeated

With self-evolving:

100+ skills in 6 months vs 5
Lessons persist with proper templates
Quality gates prevent bloat

agy: "Self-Evolving Skills is mid-high ROI; agents automatically grow from 43+ to 100+."

When to Apply

When to Use

After successful Kaggle competition
End of major experiment (consolidate learnings)
Project retrospective
When "we learned X" emerges repeatedly

When NOT to Use

One-off observations (not worth a skill)
Without Validator (always validate)
When existing skills cover the lesson

Notes

Always validate first: quality > quantity; bad skills hurt recall
Similarity >0.85 = duplicate: reject or revise
Description must start with "Use when...": ensures triggering
Importance ≥0.7: auto-archive to vault
Tags matter: helps recall filtering
See also: memory-hierarchy-management (where new skills land)

References

Implementation: framework/src/skill_evolution/
Inspired by: Voyager (Wang et al. NeurIPS 2023), AIDE auto-skill-gen
Quality gating: validator checks template + similarity + length
Pairing skill: claudeception (already exists, similar pattern)

---name: self-evolving-skillsdescription: Use when adding new skills to the library, when extracting insights from completed experiments, when consolidating lessons learned, or when running a project retrospective. Triggers after a successful Kaggle competition, after major model breakthrough, or when "we should remember this" comes up.---# Self-Evolving Skills (Voyager-Style)## ContextStatic skill libraries grow only when humans add entries. Voyager (Wang et al. NeurIPS 2023) showed agents that **auto-extract and register new skills** grow 100+ skills without manual work. agy verified this as P1 for ML agents: 43+ skills → 100+ with quality gates.The core insight: **every experiment is a candidate skill**; quality gating prevents library pollution.## Guidance### Extract from Text/Conversation```pythonfrom framework.src.skill_evolution import SkillExtractorext = SkillExtractor()candidates = ext.extract_from_text(""" We should always check data leakage before training. Never use future information in features. Make sure to use walk-forward validation for time series.""")# → 3 candidates: 1 skill, 1 anti-pattern, 1 principle```### Extract from Experiment Log```python# 假设实验记录在 EXPERIMENTS.mdcandidates = ext.extract_from_experiments_md(experiments_md_content)# 自动按 importance 排序, top 20 入选```### Validate Before Registration```pythonfrom framework.src.skill_evolution import SkillValidatorval = SkillValidator()for c in candidates: result = val.validate(c, existing_descriptions=existing) # verdict: APPROVE / REVISE / REJECT # issues: ['Too similar...', 'Missing frontmatter...'] # similarity_to_existing: 0.0 - 1.0```### Register to MCP Library```pythonfrom framework.src.skill_evolution import SkillRegistryregistry = SkillRegistry(skills_dir="skills/examples")for c in candidates: result = registry.register(c) if result.verdict.value == "approve": print(f"Registered: {c.name}") # 写入 skills/examples/{name}/SKILL.md```### Auto-Approve (Production Danger)```python# 生产环境慎用 — 应该人工 reviewregistry.register(candidate, auto_approve=True)```## Why This MattersWithout self-evolving:- Skills grow slowly (43+ → manual additions)- Lessons forgotten after session ends- Same mistakes repeatedWith self-evolving:- 100+ skills in 6 months vs 5- Lessons persist with proper templates- Quality gates prevent bloatagy: "Self-Evolving Skills is **mid-high ROI**; agents automatically grow from 43+ to 100+."## When to Apply### When to Use- After successful Kaggle competition- End of major experiment (consolidate learnings)- Project retrospective- When "we learned X" emerges repeatedly### When NOT to Use- One-off observations (not worth a skill)- Without Validator (always validate)- When existing skills cover the lesson## Notes- **Always validate first**: quality > quantity; bad skills hurt recall- **Similarity >0.85 = duplicate**: reject or revise- **Description must start with "Use when..."**: ensures triggering- **Importance ≥0.7**: auto-archive to vault- **Tags matter**: helps recall filtering- See also: `memory-hierarchy-management` (where new skills land)## References- Implementation: `framework/src/skill_evolution/`- Inspired by: Voyager (Wang et al. NeurIPS 2023), AIDE auto-skill-gen- Quality gating: validator checks template + similarity + length- Pairing skill: `claudeception` (already exists, similar pattern)

Frequently asked questions