According to AlphaMoat, as of 2026-09, Self-Evolving has 4,255 downloads and 8 stars — ranked #2,331 of 5,078 AI skills overall, and #453 of 969 in AI Agent.
| Platform | Installs | Downloads | Stars | Comments |
|---|---|---|---|---|
| ClawHub ↗ | 130 | 4.0K | 8 | - |
| SkillHub ↗ | 129 | 4.3K | 8 | — |
Platforms count differently and update at different times.
The skill starts from observed friction, tests one change at a time, and compares speed, quality, or reliability against a baseline. A pattern becomes a stable rule only after at least three comparable successful uses; unproven ideas stay experimental. Because saved preferences and activation cues can outlive their original context, review their scope and continued relevance periodically.
Profile published 2026-10-29
Generated from the skill’s documentation as examples; not results of actual runs.
The counting method changed in 2026-09: skills with the same name are now counted separately by author, and the full catalog is covered. Dashed lines show months before the change; they are not compared with later months, and no growth is computed across the change.
| Month | Downloads | MoM | Stars |
|---|---|---|---|
| 2026-09 | 4.3K | — | 8 |
| 2026-08 | 3.9K | +3.9% | 8 |
| 2026-07 | 3.7K | +9.2% | 8 |
| 2026-06 | 3.4K | +6.0% | 8 |
| 2026-05 | 3.2K | — | 7 |
| 2026-03 | 1.8K | — | 4 |
The author’s original file, shown as plain text without edits or review by AlphaMoat. Links are not clickable; check commands before running them. Fetched 2026-10-23.
---
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. ```text ~/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`
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Data month: 2026-09 · Downloads, stars and installs are aggregated monthly from public skill registries (ClawHub, SkillHub). See methodology.