ara-from-context
ara-from-context compiles research outputs accumulated in a context directory into an ARA (Agent-Native Research Artifact), a machine-executable four-layer knowledge structure, then performs Level-2 epistemic review. Use this skill when you have completed iterative research and experiment cycles whose results need conversion from narrative form into logically closed, non-storytelling structured knowledge suitable for agent consumption and rigorous validation.
git clone --depth 1 https://github.com/yogsoth-ai/de-anthropocentric-research-engine /tmp/ara-from-context && cp -r /tmp/ara-from-context/skills/ara-from-context ~/.claude/skills/ara-from-contextSKILL.md
# Campaign: ARA From Context **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 1. `Skill` load **context-review** —— 回顾 `context/`,分三类素材,对齐大方向, 产出投喂计划。 2. `Skill` load **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`。 <!-- BEGIN available-tables (generated) --> ## 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 | <!-- END available-tables (generated) -->
Experiment-specific - summarize the DARE executor's research design into a clean research_result report, forced to write back into the spec file produced by formated-specs.
Experiment-specific - replaces writing-specs, emits DARE's 4-layer call plan as a clean research_graph schema. Last step forces load formated-result.
loss-1 judge - read a sample's full dialogue and decide whether the user simulator semantically enacted its Policy Card. check-blind.
loss-2 judge - pairwise quality comparison across the n rungs within one topic; decide monotonicity and endpoint separation. check-blind, D1-D5 only.
Strategy: Inference to the best explanation in the face of anomalies
Remove components one by one, observe system changes to reveal hidden
Map system architecture to ablatable units for ablation studies
Design ablation studies to isolate component contributions in ML systems