Carlo1911

Skill Evolution — AI skill for Claude Code

AI community

Self-improvement feedback loop for AI agent skills — analyzes past sessions, drafts structured improvement proposals, and gates auto-apply behind an evaluation framework.

How to install Skill Evolution

This entry records only its repository, not the path inside it, so there is no exact command to give. Open Carlo1911/skill-evolution and copy the folder into ~/.claude/skills/, or the file into ~/.claude/agents/.

What Skill Evolution does

Self-improvement feedback loop for AI agent skills — analyzes past sessions, drafts structured improvement proposals, and gates auto-apply behind an evaluation framework. Host-agnostic (Hermes, Claude Code, and any HostAdapter).

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README

Skill Evolution

[![MIT License](https://img.shields.io/badge/license-MIT-blue.svg)](LICENSE) [![Python 3.10+](https://img.shields.io/badge/python-3.10%2B-blue.svg)](pyproject.toml) [![Hosts: Hermes + Claude Code](https://img.shields.io/badge/hosts-Hermes%20%2B%20Claude%20Code-blueviolet)](docs)

Autonomous self-improvement for your agent skills.

Reads past sessions, analyzes them against loaded skills, generates structured proposals, and optionally auto-applies high-confidence improvements. Host-agnostic: ships adapters for **Hermes** and **Claude Code**, with an extensible `HostAdapter` interface for new hosts.

**No daemons. No external services. No GPU.** Just your host agent, a scheduled job, and Python stdlib.

Minimum Configuration

Everything below has a sane default, but two things are **not optional** if you want the pipeline to actually work end to end rather than silently doing nothing or getting stuck:

Variable	Required when	Why
`SKILL_EVOLUTION_HOST`	You're on anything other than Hermes	Defaults to `hermes`. There is no auto-detection — running on Claude Code without setting this to `claude_code` reads (and would try to write) the wrong host's session DB and skills tree.
A provider credential (`ANTHROPIC_API_KEY` by default, or the key matching `SKILL_EVOLUTION_PROVIDER`)	Always, unless you only ever run `deterministic`-only evaluation	The evaluation gate's `llm_judge` (part of the default evaluator set) fails closed without a working provider — every proposal will fail the gate forever, even after you approve it, with no error beyond "gate failed" in the result.

Everything else is optional and only needs to be set to change a default:

Variable	Default	Purpose
`SKILL_EVOLUTION_REPO`	current working directory	Where a cron job finds `scripts/`
`SKILL_EVOLUTION_PROPOSALS_DIR`	`./proposals/`	Where proposal files are read/written
`SKILL_EVOLUTION_AUTO_APPLY` / `SKILL_