Apply A-Evolve's agentic evolution methodology to improve AI agent performance across runs. Use when the user wants to diagnose agent failures, generate targeted skills from error patterns, evolve system prompts, or accumulate episodic knowledge. Works standalone or inside AutoResearchClaw pipelines. Triggers on: "evolve", "self-improve", "diagnose failures", "generate skills from errors", "what went wrong and how to fix it", or any mention of A-Evolve.
Instalar com Codex ou Claude Copie este prompt, cole no Codex, Claude ou outro assistente e deixe que ele revise a página da skill e instale para você.
Um comando direto ignora o prompt de revisão. Verifique a origem antes de executá-lo.
Apply A-Evolve's agentic evolution methodology to improve AI agent performance across runs. Use when the user wants to diagnose agent failures, generate targeted skills from error patterns, evolve system prompts, or accumulate episodic knowledge. Works standalone or inside AutoResearchClaw pipelines. Triggers on: "evolve", "self-improve", "diagnose failures", "generate skills from errors", "what went wrong and how to fix it", or any mention of 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:
Based on observations, propose one or more of these mutation types:
A. Generate a Skill (for recurring patterns, frequency ≥ 3)
Write a new SKILL.md file that teaches the agent how to handle this
pattern. A good evolved skill:
Targets a specific failure category, not generic advice
Contains concrete steps the agent should follow
Includes a "when to apply" trigger condition
Is short (under 100 lines) and self-contained
Example — if the agent keeps failing at API pagination:
---
name: api-pagination-handler
description: >
Handle paginated API responses correctly. Use when making API calls
that may return partial results, or when results seem truncated.
---
When calling any API that supports pagination:
1. Check response for pagination indicators: `next_page`, `offset`,
`has_more`, `cursor`, or truncated result counts.
2. If paginated, loop until all pages are collected.
3. Concatenate results before processing.
4. Set a max-page safety limit (default: 20) to prevent infinite loops.
5. Log total items collected vs expected count if available.
B. Patch the System Prompt (for prompt ambiguity or missing guidance)
Write a short addendum to the system prompt that addresses the gap.
Keep patches minimal — one paragraph per issue. Format:
## Prompt Patch: [Issue]
Append to system prompt:
> When [specific situation], always [specific action] because [reason].
C. Add a Knowledge Entry (for factual gaps or learned heuristics)
Record a reusable insight as a knowledge entry:
{"id":"know-001","category":"experiment_design","insight":"Synthetic benchmarks with <100 samples produce high-variance results. Always use ≥500 samples or report confidence intervals.","source":"observation OBS-3 from batch 2","confidence":0.85}
D. Do Nothing (if observation is a one-off, severity is cosmetic,
or the fix would be too broad / risky)
4. Gate (Validate)
Before accepting any mutation, check:
Specificity: Does it target the observed failure without being so
broad it could cause regressions elsewhere?
Testability: Could you verify this mutation helps by re-running the
failed tasks?
Blast radius: How much of the agent's behavior does this change?
Prefer small, targeted mutations over large rewrites.
Consistency: Does it contradict existing skills or prompt guidance?
If a mutation fails the gate, either refine it or discard it.
Explain your reasoning to the user.
5. Reload (Apply and Record)
Present the accepted mutations to the user. For each:
State what changed and why
Show the artifact (skill file, prompt patch, knowledge entry)
Suggest where to place it in the project
For AutoResearchClaw projects, recommended locations:
Artifact
Location
Evolved skill
.claude/skills/evolved/<skill-name>/SKILL.md
Prompt patch
Append to prompts.default.yaml or custom prompts file
Knowledge entry
docs/kb/evolved_knowledge/<id>.json
Observation log
evolution/observations/<batch>.md
Keep a running version log so the user can track what evolved and when:
## Evolution Log
- evo-1 (2026-03-30): Generated `api-pagination-handler` skill from OBS-1
- evo-2 (2026-03-30): Prompt patch for citation format from OBS-4
Usage with AutoResearchClaw
This skill maps to ARC's pipeline stages:
ARC Stage
Evolution Role
12 EXPERIMENT_RUN
Source of Solve artifacts
13 ITERATIVE_REFINE
Main Observe + Evolve trigger point
15 RESEARCH_DECISION
Natural Gate — PROCEED = accept, REFINE = retry
18 PEER_REVIEW
Additional Observe signal for writing quality
When the user says "evolve my research pipeline" or similar:
Ask which run to analyze (or find the latest artifacts/rc-*/)
Run the Observe step on experiment outputs + review feedback
Propose mutations targeting the weakest pipeline stages
Generate skill files that ARC can load via .claude/skills/
Anti-Patterns
Do NOT:
Generate vague, generic skills ("always be careful", "check your work")
Propose mutations for one-off errors that won't recur
Rewrite the entire system prompt — patch it surgically
Generate more than 3 skills per evolution cycle (quality over quantity)
Mutate tool code unless the user explicitly asks for it
Relationship to MetaClaw
If AutoResearchClaw has MetaClaw enabled (metaclaw_bridge.enabled: true),
evolved skills from this process can be placed in ~/.metaclaw/skills/arc-*/
so MetaClaw injects them into future runs automatically. The two systems
are complementary:
A-Evolve skill: Deep, targeted mutation from structured observation
MetaClaw lesson: Broad pattern captured from pipeline warnings/errors
Both can coexist. Skills generated here are higher-precision; MetaClaw
lessons are higher-recall.
