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Autoresearch Loop
作者
amdf01-debug
· GitHub ↗
· v1.0.0
· MIT-0
414
总下载
3
收藏
2
当前安装
1
版本数
在 OpenClaw 中安装
/install sw-autoresearch
功能描述
Conducts autonomous, iterative research by defining goals, generating hypotheses, verifying results, modifying approaches, and repeating until criteria are met.
使用说明 (SKILL.md)
Autoresearch Skill
Trigger
Autonomous goal-directed iteration — agent modifies, verifies, keeps/discards, and repeats.
Trigger phrases: "research this thoroughly", "autonomous research", "iterate until complete", "deep dive", "autoresearch"
Core Loop
Inspired by Karpathy's autoresearch methodology:
1. Define goal and success criteria
2. Generate hypothesis or approach
3. Execute (search, analyse, synthesise)
4. Verify result against criteria
5. If criteria met → keep result, move to next
6. If criteria not met → modify approach, retry
7. Repeat until all criteria satisfied
Implementation
# Autoresearch: [Topic]
## Goal
[What you're trying to find/prove/analyse]
## Success Criteria
- [ ] [Criterion 1 — specific and measurable]
- [ ] [Criterion 2]
- [ ] [Criterion 3]
## Iteration Log
### Attempt 1
- Approach: [what was tried]
- Result: [what was found]
- Assessment: [met criteria? why/why not?]
- Next: [what to try differently]
### Attempt 2
...
## Final Output
[Synthesised result that meets all criteria]
Rules
- Always define success criteria BEFORE starting research
- Maximum 10 iterations per research question (prevent infinite loops)
- Each iteration must try a DIFFERENT approach (no repeating failed strategies)
- Log every attempt — the failures are as valuable as the successes
- Verify findings from multiple sources before accepting
- Be explicit about confidence level: high/medium/low for each finding
安全使用建议
This skill appears coherent and low-risk because it is instruction-only and requests no credentials. Before installing or enabling autonomous invocation, consider: 1) Review what tools your agent has (web browsing, external API access, filesystem access) — the skill's instructions allow the agent to use whatever search/synthesis tools it already has. 2) If you don't want the agent to access the web or local files, disable those capabilities or require user confirmation. 3) Require the skill to produce explicit citations for claims and a confidence level (the SKILL.md asks for this — enforce it). 4) Keep the provided iteration limit (10) and consider lowering it if you want tighter control. 5) Don't provide secrets or credentials to the agent while running open-ended research. If you want more assurance, ask the publisher for example sessions or an explicit list of allowed sources/tools; if the skill shipped code or an installer, re-evaluate (that would raise new risks).
功能分析
Type: OpenClaw Skill
Name: sw-autoresearch
Version: 1.0.0
The Autoresearch skill provides a structured methodology for an AI agent to perform iterative research tasks. It defines a logical loop of goal setting, execution, verification, and refinement without any executable code, network access, or instructions that subvert security controls. The content in SKILL.md is purely instructional and focused on improving the quality of research outputs.
能力评估
Purpose & Capability
Name/description (autoresearch, iterative verification) matches the SKILL.md loop. The skill requests no binaries, env vars, or installs that would be unrelated to performing research.
Instruction Scope
SKILL.md gives an open-ended methodology (search/analyse/synthesise/verify) and requires verification from multiple sources and iteration caps (max 10). The instructions are high-level and intentionally leave the agent discretion about how to search and which sources to use — this is coherent for a research skill but gives the agent broad authority to query external sources or tools. There are reasonable guardrails (iteration limit, require different approaches, logging), but the skill does not explicitly constrain which data sources or tools may be used or prohibit accessing sensitive files or secrets.
Install Mechanism
Instruction-only skill with no install spec and no code files; nothing is written to disk or downloaded. This is the lowest-risk install profile.
Credentials
No environment variables, credentials, or config paths are requested. The lack of requested secrets is proportionate to a research-oriented skill.
Persistence & Privilege
always is false and the skill is user-invocable; the skill can be invoked autonomously by the agent (platform default) but does not request elevated or persistent privileges. Combined with no requested credentials, the privilege footprint is minimal.
如何使用
- 确保已安装 OpenClaw(本地或 Docker 部署)
- 在对话框中输入安装命令:
/install sw-autoresearch - 安装完成后,直接呼叫该 Skill 的名称或使用
/sw-autoresearch触发 - 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.0
Initial release
元数据
常见问题
Autoresearch Loop 是什么?
Conducts autonomous, iterative research by defining goals, generating hypotheses, verifying results, modifying approaches, and repeating until criteria are met. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 414 次。
如何安装 Autoresearch Loop?
在 OpenClaw 或 Claude Code 对话框中运行命令「/install sw-autoresearch」即可一键安装,无需额外配置。
Autoresearch Loop 是免费的吗?
是的,Autoresearch Loop 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。
Autoresearch Loop 支持哪些平台?
Autoresearch Loop 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。
谁开发了 Autoresearch Loop?
由 amdf01-debug(@amdf01-debug)开发并维护,当前版本 v1.0.0。
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