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ara-rigor-review

The ara-rigor-review skill executes a six-dimensional semantic rigor assessment on ARA (Anthropocentric Research Artifact) documents in the ../ara/ directory using an external rigor-reviewer tool, evaluating evidence relevance, falsifiability quality, scope calibration, argument coherence, exploration integrity, and methodological rigor. Use this when validating the epistemological soundness and logical closure of research artifacts before finalizing or publishing them.

View source Repository: de-anthropocentric-research-engine

Install in Claude Code

Copy

git clone --depth 1 https://github.com/yogsoth-ai/de-anthropocentric-research-engine /tmp/ara-rigor-review && cp -r /tmp/ara-rigor-review/skills/ara-rigor-review ~/.claude/skills/ara-rigor-review

Then start a new Claude Code session; the skill loads automatically.

Definition

SKILL.md

# 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。