A-Evolve: Agentic Evolution Skill

Apply the Solve → Observe → Evolve → Gate → Reload methodology from A-Evolve to iteratively improve agent performance. This skill is prompt-based, no external dependencies, no harness changes. You analyze failures, propose workspace mutations, and generate durable artifacts (skills, prompt patches, knowledge entries) that the agent can load in future runs.

Core Loop

When asked to evolve or improve agent performance, follow this 5-step loop:

1. Solve (Collect Evidence)

Gather the agent's execution artifacts. Ask the user for or locate:

• Run logs, error traces, or experiment outputs
• Pass/fail results per task
• Metric values (accuracy, reward, success rate)
• Any existing session files from previous runs

If inside AutoResearchClaw, look at:

• artifacts/rc-*/, experiment outputs, charts, reviews
• evolve.log or stage-specific logs
• reviews.md, peer review feedback
• Sentinel watchdog reports

2. Observe (Diagnose)

Analyze the collected evidence to produce structured observations:

For each failed or underperforming task, identify:

• Error category: code bug, timeout, wrong approach, missing knowledge, API misuse, hallucinated reference, prompt ambiguity, etc.
• Root cause: What specifically went wrong and why
• Frequency: Is this a one-off or a recurring pattern across tasks?
• Severity: blocking (pipeline crash) / degrading (wrong result) / cosmetic (formatting issue)

Write observations as a structured list:

## Observations (Batch N)

### OBS-1: [Category] Short description
- Tasks affected: task_001, task_005, task_012
- Root cause: ...
- Frequency: 3/50 tasks (6%)
- Severity: degrading

### OBS-2: ...

3. Evolve (Propose Mutations)

Based on observations, propose one or more of these mutation types:

A. Generate a Skill (for recurring patterns, frequency ≥ 3)