a-evolve
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)
More from aiming-lab/autoresearchclaw
scientific-writing
Academic manuscript writing with IMRAD structure, citation formatting, and reporting guidelines. Use when drafting or revising research papers.
13scientific-visualization
Publication-ready scientific figure design with matplotlib and seaborn. Use when creating journal submission figures with proper formatting, accessibility, and statistical annotations.
12literature-search
Systematic literature review methodology including search strategy, screening, and synthesis. Use when conducting literature reviews or writing background sections.
12statistical-reporting
Statistical test selection, assumption checking, and APA-formatted reporting. Use when analyzing experimental results or writing results sections.
11hypothesis-formulation
Structured scientific hypothesis generation from observations. Use when formulating testable hypotheses, competing explanations, or experimental predictions.
10researchclaw
Run the ResearchClaw autonomous research pipeline from a topic, config, and output directory.
9