- 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 experimentsQuick 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.mdonly 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.mdbefore 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 timememory— keep durable long-term context and retrieval patternsdecide— compare options and commit to a clear next movelearning— structure deliberate practice and feedback loopsproactivity— follow through on next steps once a better pattern is chosen
Feedback
- If useful:
clawhub star self-evolving - Stay updated:
clawhub sync