Iván

Self-Evolving

AI Agentv1.0.0
By IvánUpdated 2026-05-11self-evolving@clawhub_ivangdavila/self-evolving
Workflow OrchestrationMemory

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.

Installs
130
Downloads
4.3K
Stars
8
Overall Rank
#2,331
Top 46% overall · Top 47% in AI Agent
PlatformInstallsDownloadsStarsComments
ClawHub ↗1304.0K8-
SkillHub ↗1294.3K8—

Platforms count differently and update at different times.

What this skill does

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

What you can do with it

  • Review a recurring workflow failure or bottleneck
  • Test one prompt, decision rule, or retrieval change
  • Promote a practice supported by repeated evidence

When to use it

  • A workflow shows a repeated failure, correction, or bottleneck
  • You can preserve a baseline and change one factor at a time
  • Outcomes can be assessed with concrete evidence

Example prompts

Generated from the skill’s documentation as examples; not results of actual runs.

Propose one small change for this recurring workflow issue and record the baseline and outcome. Keep it experimental; do not make it a durable rule until comparable evidence supports it.

Monthly Trend

Downloads4.3K
1.6K3.0K4.5K2026-03 · Downloads 1.8K2026-05 · Downloads 3.2K2026-06 · Downloads 3.4K2026-07 · Downloads 3.7K2026-08 · Downloads 3.9K2026-09 · Downloads 4.3K26-0326-09
Stars8
4682026-03 · Stars 42026-05 · Stars 72026-06 · Stars 82026-07 · Stars 82026-08 · Stars 82026-09 · Stars 826-0326-09

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.

MonthDownloadsMoMStars
2026-094.3K—8
2026-083.9K+3.9%8
2026-073.7K+9.2%8
2026-063.4K+6.0%8
2026-053.2K—7
2026-031.8K—4

SKILL.md (original)

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.

4.5 KB · ClawHub v1.0.0
---
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/"]}}
---
Show all (108 lines)
## 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.