Self-Evolving

Improve reusable agent workflows with reflective experiments, value checks, and local pattern memory.

When to Use

User wants the agent to improve a repeated workflow without blind self-rewrites. The skill handles local experiment logs, promotion of proven patterns, and explicit value gates before a new behavior becomes stable.

Architecture

Memory lives in ~/self-evolving/. If ~/self-evolving/ does not exist, run setup.md. See memory-template.md, memory.md, experiments.md, evolution-loop.md, and boundaries.md for the operating model.

~/self-evolving/
├── memory.md        # HOT: stable rules, guardrails, activation cues
├── experiments.md   # WARM: tentative mutations and outcomes
└── archive/         # COLD: retired patterns and old experiments

Quick Reference

Topic	File
Setup guide	setup.md
Memory template	memory-template.md
Hot memory baseline	memory.md
Experiment log format	experiments.md
Evolution cycle	evolution-loop.md
Safety boundaries	boundaries.md

Requirements

• No credentials required
• No extra binaries required
• No network access required

Core Rules

1. Start From Real Friction

• Evolve only after a failed attempt, repeated correction, or measurable bottleneck.
• Do not invent mutations just because a task feels interesting.

2. Change One Lever at a Time

• Test one prompt pattern, decision rule, retrieval step, or file habit per experiment.
• Small mutations make the winning variable obvious.

3. Gate by Value, Not Novelty

• Promote a pattern only when it improves speed, quality, or reliability across at least three comparable uses.
• Unproven ideas stay tentative in experiments.md.

4. Keep Local Evidence

• Record the trigger, mutation, outcome, and next action for every experiment.
• Tell the user before the first persistent write that this skill keeps concise local notes for repeat improvement.
• Promote durable rules into memory.md only after evidence repeats.

5. Prefer Promotion Over Rewrite

• Convert winners into short rules, checklists, or retrieval triggers.
• Stable systems compound by accumulation, not by starting over.

6. Respect Hard Boundaries

• Follow boundaries.md before storing data or changing behavior.
• Never modify the installed skill files, exfiltrate unrelated data, or run hidden experiments on the user.

Common Traps

Trap	Why It Fails	Better Move
Rewriting the whole workflow after one mistake	You cannot isolate what actually helped	Test one mutation and compare against the previous baseline
Promoting an idea after one good run	Lucky wins become noisy defaults	Wait for three comparable wins before promotion
Logging vague lessons like "be smarter"	Future retrieval becomes useless	Write the exact trigger, decision, and expected outcome
Optimizing for novelty instead of value	The system churns without compounding	Keep only behaviors that measurably save time or reduce errors
Learning from silence	Lack of complaint is not proof	Require explicit feedback or repeated success evidence

Security & Privacy

Data that leaves your machine:

• None by default

Data that stays local:

• Stable rules, guardrails, and activation notes in ~/self-evolving/memory.md
• Tentative experiments and outcomes in ~/self-evolving/experiments.md
• First-time local storage should be announced before the first write

This skill does NOT:

• Call external APIs
• Read or store credentials
• Modify its own installed instructions
• Read unrelated files outside the active task plus ~/self-evolving/

Related Skills

Install with clawhub install <slug> if user confirms:

• self-improving — learn from corrections and compound execution quality over time
• memory — keep durable long-term context and retrieval patterns
• decide — compare options and commit to a clear next move
• learning — structure deliberate practice and feedback loops
• proactivity — follow through on next steps once a better pattern is chosen

Feedback

• If useful: clawhub star self-evolving
• Stay updated: clawhub sync