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Autoreason Lite

作者 Christopher Wheeler · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ✓ 安全检测通过
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在 OpenClaw 中安装
/install autoreason-lite
功能描述
Apply a bounded multi-candidate self-refinement loop (A/B/AB + judges + do-nothing option) to improve drafts, plans, and analyses while preventing scope cree...
使用说明 (SKILL.md)

Autoreason Lite

Use this skill when a user asks to "improve this," "refine this," or "make this better" and quality matters more than one-shot speed.

What this does

A bounded refinement tournament:

  1. A (incumbent): current draft unchanged
  2. B (adversarial revision): deliberately different attempt
  3. AB (synthesis): merge best parts of A and B
  4. Judging pass: pick winner with explicit rubric
  5. Convergence rule: stop early if A keeps winning (no-change is valid)

This reduces over-editing and drift.

Default operating profile

  • Max rounds: 3
  • Judges: 3 independent judge personas
  • Aggregation: Borda-like ranking (1st=2 pts, 2nd=1 pt, 3rd=0)
  • Convergence: stop if A wins 2 rounds (or winner unchanged 2 rounds)
  • Length guardrail: output within ±15% of requested length unless user asks otherwise
  • Voice lock: preserve user's tone profile (technical / founder / viral) unless asked to shift

When to use

Use for:

  • Long-form writing
  • Strategy memos
  • Explanations/tutorial drafts
  • Product copy
  • Decision frameworks

Avoid for:

  • Deterministic factual extraction
  • Tiny edits user already specified exactly
  • Time-critical one-liners unless user requests deep refinement

Execution steps

  1. Clarify success criteria (tone, audience, length, goal) if missing.
  2. Generate candidate B from A:
    • Must change structure or argument order (not just wording).
    • Must preserve critical facts.
  3. Generate candidate AB:
    • Keep strongest parts from A and B.
    • Remove redundancy.
  4. Run 3 judges independently with rubric:
    • Accuracy / faithfulness
    • Clarity
    • Usefulness for user goal
    • Concision / scope control
  5. Score candidates; choose winner.
  6. Repeat up to max rounds with winner as new A.
  7. Return final with short “what changed + why”.

Judge prompt template

Use references/judge-rubric.md.

Output format to user

  • Final refined result
  • 2-4 bullets: key improvements
  • Optional: one-line note if loop stopped due to convergence (no meaningful gain)

Quick presets

  • Technical: precise wording, fewer claims, concrete mechanisms
  • Founder: outcomes + positioning + credibility signal
  • Viral: short lines, strong hooks, high readability, no fluff

Safety + quality constraints

  • Never invent facts to make prose sound better.
  • Keep user intent stable unless explicitly asked to pivot.
  • Prefer no-change over noisy edits.
  • If confidence drops, surface uncertainty instead of bluffing.
安全使用建议
This skill appears coherent and low-risk: it only defines an internal editing workflow and does not request credentials or install code. Before installing, consider that (1) the judges are LLM personas (not external human auditors), so verify final outputs for factual accuracy and sensitive content, (2) if you will submit proprietary or confidential text, check your platform's data-logging/privacy policy since the skill will operate on user-provided content, and (3) if you need stricter guarantees (no autonomous invocation), confirm platform-level invocation controls.
功能分析
Type: OpenClaw Skill Name: autoreason-lite Version: 1.0.0 The skill bundle provides a structured logic framework for an AI agent to perform iterative text refinement through a multi-candidate 'tournament' process. It contains only markdown instructions (SKILL.md) and a rubric (references/judge-rubric.md) with no executable code, network access, or file system operations. The instructions explicitly emphasize faithfulness to user intent and safety constraints, such as avoiding hallucinations.
能力评估
Purpose & Capability
Name/description match the instructions: the skill describes a bounded multi-candidate refinement process for improving drafts and its steps and presets align with that goal.
Instruction Scope
SKILL.md only describes internal candidate-generation and judging steps, references a local rubric file, and does not instruct reading unrelated files, accessing environment variables, or sending data to external endpoints.
Install Mechanism
No install spec and no code files — nothing is written to disk or downloaded by the skill itself, which minimizes installation risk.
Credentials
The skill declares no required environment variables, credentials, or config paths; requested capabilities are proportional to an editing/refinement function.
Persistence & Privilege
always is false and there is no installation behavior that persists or modifies system/other-skill config. The skill can be invoked autonomously per platform defaults, which is expected for this type of skill.
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 autoreason-lite
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /autoreason-lite 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
v1.0.0
Initial release: bounded A/B/AB refinement loop with judge rubric, convergence, tone presets
元数据
Slug autoreason-lite
版本 1.0.0
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

Autoreason Lite 是什么?

Apply a bounded multi-candidate self-refinement loop (A/B/AB + judges + do-nothing option) to improve drafts, plans, and analyses while preventing scope cree... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 81 次。

如何安装 Autoreason Lite?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install autoreason-lite」即可一键安装,无需额外配置。

Autoreason Lite 是免费的吗?

是的,Autoreason Lite 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Autoreason Lite 支持哪些平台?

Autoreason Lite 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 Autoreason Lite?

由 Christopher Wheeler(@cwheeler67)开发并维护,当前版本 v1.0.0。

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