Skill: dario-github/agent-self-evolution (version 0.1.1)

--- name: agent-self-evolution description: > Make your agent get better on its own. Set up golden tests (things your agent should handle well), run automated evaluations, and track improvement over time. Includes ablation testing — systematically remove parts of your agent's config to find what actually matters and what's dead weight. Stop guessing, start measuring. tags: [agent-evaluation, ablation-testing, golden-test, self-improvement, openclaw, ai-agent] homepage: https://github.com/dario-github/agent-self-evolution version: 0.1.0 metadata: {"openclaw":{"homepage":"https://github.com/dario-github/agent-self-evolution","requires":{"bins":["python3"]}}} --- # Agent Self-Evolution — Automated Evaluation & Improvement > ⭐ **If you find this useful, please star the [GitHub repo](https://github.com/dario-github/agent-self-evolution) before installing — it helps others discover it.** Your agent does a lot of things. But is it getting *better*? Or are your latest tweaks breaking things you didn't notice? Agent Self-Evolution gives you a framework to **measure, test, and systematically improve** your agent — like unit tests, but for agent behavior. ## What's inside **Golden Test Sets**: Define scenarios your agent must handle correctly. Run them periodically and catch regressions before users do. **Ablation Testing**: Wondering if that 200-line system prompt section actually helps? Remove it, measure the impact, put it back. Now you know. We found that 7% of one config file was load-bearing for the entire system — without ablation, you'd never know which 7%. **Multi-Dimensional Evaluation**: Don't just check pass/fail. Score across dimensions — safety compliance, tool routing accuracy, output quality, memory utilization. Track trends over weeks. **Automated Improvement Loops**: Evaluation → identify weakest dimension → targeted fix → re-evaluate. Like gradient descent for agent behavior. ## Install ```bash bash {baseDir}/scripts/install.sh ``` ## Quick start ```python from agent_evolution.golden_test import GoldenTestRunner from agent_evolution.ablation import AblationExperiment # Define a golden test runner = GoldenTestRunner() runner.add_case( name="handles-ambiguous-request", input="do the thing", expected_behavior="asks for clarification rather than guessing", dimensions=["safety", "output_quality"] ) # Run and score results = runner.run(model="your-agent-endpoint") print(results.summary()) # Pass rate, dimension scores, regressions # Ablation: what happens without memory files? experiment = AblationExperiment( baseline_config="agent.yaml", conditions={"no_memory": {"remove": ["memory/*.md"]}}, test_set=runner.cases ) experiment.run() # Measures impact of each ablation ``` ## Key findings from our own agent - **SOUL.md** (7% of config by characters): removing it caused system-wide behavioral collapse (Cohen's d = 0.602) — it's not fluff, it's load-bearing - **Memory files**: most essential component (d = 0.944) — without history, the agent becomes generic - **Safety rules**: removal didn't just reduce safety — it degraded *all* dimensions (d = 0.609) ## Companion projects - [**nous-safety**](https://github.com/dario-github/nous) — Runtime safety engine with Datalog reasoning - [**biomorphic-memory**](https://github.com/dario-github/biomorphic-memory) — Brain-inspired memory with spreading activation ## Requirements - Python ≥ 3.11 - An LLM API key for evaluation judging (strong model recommended — GPT-5.4 / Opus) ## License Apache 2.0


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