AutoResearchClaw is a system that turns a research idea into a scientific paper through autonomous and collaborative AI research workflows. It is for researchers who want agents to investigate questions, run experiments, and produce papers, with optional human guidance. Catalogue skills and agents provide parts of its research workflow.
Borrowing it
Nothing to install: this file belongs to aiming-lab/AutoResearchClaw. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/aiming-lab/AutoResearchClaw/main/.claude/skills/a-evolve/SKILL.mdgit clone --depth 1 https://github.com/aiming-lab/AutoResearchClawWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/aiming-lab/autoresearchclaw/a-evolve)<a href="https://agentmods.dev/skills/aiming-lab/autoresearchclaw/a-evolve"><img src="https://agentmods.dev/badge/skills/aiming-lab/autoresearchclaw/a-evolve/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/aiming-lab/autoresearchclaw/a-evolve"><img src="https://agentmods.dev/badge/skills/aiming-lab/autoresearchclaw/a-evolve.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Rogue Agent · line 188 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00100 | $0.01697 |
| Opus 5.5 | $0.00040 | $0.00679 |
| Sonnet 5.5 | $0.00020 | $0.00339 |
| Haiku 4.5 | $0.00010 | $0.00170 |
Grade A, and why
a-evolve scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 204 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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, reviewsevolve.logor stage-specific logsreviews.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)
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
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2mo ago First seen · 204 lines · 100 tokens per session scan A 7b8144bdd37d
a-evolve is a skill published in the GitHub repository aiming-lab/AutoResearchClaw (14,822 stars, last pushed 2mo ago), licensed MIT. It adds 100 tokens to every session and 1,697 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
a-evolve compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| a-evolveaiming-lab/AutoResearchClaw | Skill | 15k | 2mo ago | MIT | A | 100 | Claude Code |
| clawmobile-trace-inductionClawMobile/ClawMobile | Skill | 147 | 1mo ago | MIT | A | 25 | Claude Code, Codex |
| clawmobile-capabilitiesClawMobile/ClawMobile | Skill | 147 | 1mo ago | MIT | A | 18 | Claude Code, Codex |
| clawmobile-policyClawMobile/ClawMobile | Skill | 147 | 1mo ago | MIT | B | 40 | Claude Code, Codex |
| TweetClaw X Source ContextAmariahAK/atlarix-skills | Skill | 2 | 1mo ago | Apache-2.0 | A | 6 | Claude Code, Codex |
| tmuxHKUDS/nanobot | Skill | 50k | today | MIT | A | 20 | Claude Code, Codex |
| add-resendnanocoai/nanoclaw | Skill | 31k | yesterday | MIT | A | 16 | Claude Code |