Campaign: ARA From Context
SkillAI & modelsCampaign: Compile a context/ research record into an ARA (Agent-Native Research Artifact) and run a Level-2 epistemic review — no LaTeX, no narrative paper
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Campaign: ARA From Context skill
What this skill tells your AI
The instructions your AI receives, as published by yogsoth-ai/de-anthropocentric-research-engine in skills/ara-from-context/SKILL.md and read by ahel’s review.
What this is: DARE 流水线最末端的"成文"环节。吃前面研究循环
(research ↔ experiment-execution 反复迭代)沉淀在 context/ 里的全部产物,
编译成一份 ARA(机器可执行的四层知识包),并做认识论审查。不写 LaTeX /
叙事论文 —— ARA 刻意反对 storytelling,要的是逻辑弧在结构上闭合。
Source of truth: 所有素材来自 context/。核心 = 末次 EE 的最终 report +
全程迭代轨迹 + 研究产出的图片。
Flow
Skillload context-review —— 回顾context/,分三类素材,对齐大方向, 产出投喂计划。Skillload compile-and-review —— 一次 inline 跑外部 compiler 得../ara/, 再跑 rigor-reviewer 得level2_report.json。
External dependency
运行需 ARA 的 compiler + rigor-reviewer skill 在位
(npx @ara-commons/ara-skills)。见本 repo README。
Output
ara/(logic/ src/ trace/ evidence/ PAPER.md)+ ara/level2_report.json。
Available Tactics
Optional, no fixed order; the final leaf is always a sop.
| Tactic | When to use |
|---|---|
| compile-and-review | Tactic: Compile the feeding plan into an ARA via the external compiler, then run Level-2 rigor review over it |
| context-review | Tactic: Review a context/ directory — sort material into ARA types, locate and align the north-star, and produce a feeding plan for the compiler |
Available SOPs
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use |
|---|---|
| ara-compile | SOP: Turn the feeding plan into the compiler's $ARGUMENTS and run the external ARA compiler once inline to produce ../ara/ |
| 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 |
| context-exploring | SOP: Read context/INDEX.md and sort the whole directory into three ARA material types (report line, process line, images), locate the north-star file, and draft a feeding plan for the ARA compiler |
| north-star-align | SOP: Deep-read the original north-star context, distill this ARA's overall direction, and align it with the user via the reused present-and-ask / present-candidates dialogue SOPs |
Signals
- GitHub stars
- 469
- Forks
- 37
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
ara-from-context- Source
- github.com/yogsoth-ai/de-anthropocentric-research-engine