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Autoresearch Loop

作者 Leo Stehlik · GitHub ↗ · v0.1.0 · MIT-0
cross-platform ✓ 安全检测通过
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在 OpenClaw 中安装
/install autoresearch-loop
功能描述
Runs an autonomous modify-verify-decide loop toward a measurable goal. Use when an agent needs to iterate repeatedly on a codebase, research task, or any pro...
使用说明 (SKILL.md)

Autoresearch Loop

You are running an autonomous improvement loop. The goal is measurable. Each iteration makes one atomic change, verifies it, and keeps or discards the result. You stop when the goal is met, you hit the iteration cap, or you reach a blocker.

Core Loop

1. Read context + lessons file
2. Pick ONE hypothesis
3. Make ONE atomic change
4. Commit (before verification)
5. Run VERIFY — did the target metric improve?
6. Run GUARD — did anything else break?
7. Decision: keep / discard / rework
8. Log the result
9. Health check (3+ discards? escalate)
10. Repeat

Read references/loop-protocol.md for the full loop spec. Read references/pivot-protocol.md for the escalation ladder. Read references/lessons-protocol.md for cross-run learning.

Before Starting

Confirm with the user:

  • Goal — one sentence describing what you want to achieve
  • Metric — what number are you measuring (lower/higher = better)
  • Verify command — how to measure the metric mechanically
  • Guard command — what must not break (optional but recommended)
  • Scope — which files/directories are in play
  • Run mode — foreground (current session) or background (unattended)
  • Iteration cap — unlimited, or stop at N

Show what you found and ask for confirmation. One round minimum. Then say "go" to start.

Verify vs Guard

  • Verify = "Did the target metric improve?" — measures progress
  • Guard = "Did anything else break?" — prevents regressions
  • Guard files are never modified
  • If verify passes but guard fails: rework up to 2 attempts, then discard

Decision Rules

Result Action
Verify pass + Guard pass Keep. Extract lesson.
Verify pass + Guard fail Rework (max 2 attempts). If still failing, discard.
Verify fail Discard. Revert.
Crash Auto-fix attempt. If unfixable, skip.
Syntax error Fix immediately. Does not count as iteration.

Escalation Ladder

See references/pivot-protocol.md for full details.

Trigger Action
3 consecutive discards REFINE — adjust within current strategy
5 consecutive discards PIVOT — abandon strategy, try fundamentally different approach
2 PIVOTs without improvement Web search for external solutions
3 PIVOTs without improvement Soft blocker — stop and report to human

A single successful keep resets all counters.

Long Run Hygiene

  • Every completed experiment must be recorded before the next one starts
  • Re-read original instructions every 10 iterations to prevent context drift
  • Log: one row per iteration (iteration, commit, metric, delta, status, description)

Lessons

Extract structured lessons after:

  • Every kept iteration (what worked and why)
  • Every PIVOT decision (what failed and why)
  • Run completion

Store in autoresearch-lessons.md (not committed). Consult at the start of each run. Keep ~50 entries, summarise older ones with time decay.

安全使用建议
This result is limited because workspace command execution failed before metadata.json and artifact files could be read. Re-run the review with artifact access available before relying on this assessment for installation.
能力标签
cryptocan-make-purchases
能力评估
Purpose & Capability
No provided artifact text showed purpose mismatch or unsafe capabilities.
Instruction Scope
No provided artifact text showed hidden instructions, forced tool use, or scope override behavior.
Install Mechanism
No provided artifact text showed unsafe install behavior or untrusted execution.
Credentials
No provided artifact text showed disproportionate environment access.
Persistence & Privilege
No provided artifact text showed persistence, privilege escalation, credential misuse, or background operation.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install autoresearch-loop
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /autoresearch-loop 触发
  4. 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
v0.1.0
Initial public Autoresearch Loop skill.
元数据
Slug autoresearch-loop
版本 0.1.0
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 1
常见问题

Autoresearch Loop 是什么?

Runs an autonomous modify-verify-decide loop toward a measurable goal. Use when an agent needs to iterate repeatedly on a codebase, research task, or any pro... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 54 次。

如何安装 Autoresearch Loop?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install autoresearch-loop」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 Leo Stehlik(@leostehlik)开发并维护,当前版本 v0.1.0。

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