Self-Evolving

--- name: Self-Evolving slug: self-evolving version: 1.0.0 homepage: https://clawic.com/skills/self-evolving description: Improve reusable agent workflows with reflective experiments, value checks, and local pattern memory. changelog: Introduces a clearer local evolution loop, setup guidance, and safer local memory boundaries. metadata: {"clawdbot":{"emoji":"🧬","requires":{"bins":[]},"os":["linux","darwin","win32"],"configPaths":["~/self-evolving/"]}} ---

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 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