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/ara-rigor-review

SOP: Run the external ARA rigor-reviewer (Seal Level 2, six-dimension semantic review) over ../ara/ and pass its level2_report.json to the user

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de-anthropocentric-research-engine
393200 skills
Install
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill ara-rigor-review --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/ara-rigor-review

Context preview

The summary Claude sees to decide when to auto-load this skill.

SOP: Run the external ARA rigor-reviewer (Seal Level 2, six-dimension semantic review) over ../ara/ and pass its level2_report.json to the user

SKILL.md

ara-rigor-review.SKILL.md
name: ara-rigor-review
description: 'SOP: Run the external ARA rigor-reviewer (Seal Level 2, six-dimension semantic review) over ../ara/ and pass its level2_report.json to the user'
version: 1.0.0
category: ara-from-context
type: sop
campaign: ara-from-context
input: A populated ara/ directory (Level 1 passed)
output: ara/level2_report.json (grade + D1–D6 + findings)
dependencies:
  skills:
  - rigor-reviewer

SOP: ARA Rigor Review

**Key question**: 这份 ARA 的认识论严谨度如何?逻辑弧在结构上闭合了吗?

Preflight

先确认外部 `rigor-reviewer` skill 可 load。不可用则提示安装并**停下**。

Procedure

1. **跑 Level 2**:`Skill` load **rigor-reviewer**,传 `<artifact_dir>` = `../ara/`。 它对 ARA 跑六维语义审查(全是要读懂 + 推理的语义检查,不是结构校验):

  • D1 Evidence Relevance — 证据是否在**实质**上支撑每条 claim;
  • D2 Falsifiability Quality — 证伪标准是否有意义、可操作、范围合适;
  • D3 Scope Calibration — claim 是否恰好断言其证据所支撑的,不多不少;
  • D4 Argument Coherence — 是否从 problem→solution→evidence 逻辑闭合;
  • D5 Exploration Integrity — exploration tree 是否记录了真实研究过程(含失败);
  • D6 Methodological Rigor — 实验设计/baseline/ablation/报告是否到位。

2. **产物**:`rigor-reviewer` 在 artifact 根目录写 `level2_report.json` (每维 1–5 分 + strengths/weaknesses/suggestions + severity 排序 findings + overall grade + 给作者的问题)。

3. **D5 低分不是错误,是"探索素材不足"信号。** 透传给用户,由用户决定是否回 `context-exploring` 补打捞过程线。**本 SOP 不自动循环。**

> 注意:`rigor-reviewer` 的 D1–D6 是 **ARA 自己的**维度,与 DARE 的 D1–D5 评判 > 标准是两套东西,不要混。本 SOP 只透传 ARA 的报告,不施加 DARE 的 D1–D5。

Output

`ara/level2_report.json` + 一句话总结(grade + 最该关注的 finding),交付用户。

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The complete research orchestration system for AI-native science. What It Does Design Philosophy Architecture (v3.2.2) Quick Start Configuration Roadmap License DARE is not a tool that helps you do research. It is the researcher.

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