/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
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill ara-rigor-review --agent claude-codeHow 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.mdname: 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。
Read more
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。
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.
Repo: yogsoth-ai/de-anthropocentric-research-engine
Other skills on de-anthropocentric-research-engine.
- /formated-results
Closing skill for the research-executor, loaded as the last step of formated-specs. Summarize the design just produced into one research-result JSON fenced block in your reply. Do not execute the research.
Open skill - /formated-specs
Spec-slot skill for the research-executor. Emit the 4-layer DARE orchestration of the assigned topic as one research-graph JSON fenced block in your reply. Replaces the generic spec-writing step.
Open skill - /injection-fidelity
Loss-1 judge (codex role). Given one sample's de-identified dialogue and its PolicyCard, decide axis-by-axis whether the user-simulator enacted the card's per-axis pressure. Judge enactment of the card, never whether the research is good.
Open skill - /ladder-quality-order
Loss-2 judge (codex role). Over one topic's 6 shuffled research-design samples, pairwise-rank by quality using the D1–D5 standard. Emit the pairwise log; the harness computes the order and the ladder verdicts. Judge quality difference, never against academic standards.
Open skill - /optimization-loop
The optimizer brain for the ladder-foundry pretraining loop. Runs the two-level nested batch loop, delegates gating to gate_eval, attributes a failing batch to one weight (attribute-first), and recovers from disk after compaction. Control flow is fully scripted; only the
Open skill - /acu-nugget-recall
Tactic: Extract atomic units from one paper and score how much of a caller-supplied summary covers. Use for ACU-style binary or Nugget-style ternary recall checks; cannot run without a target summary.
Open skill

