From gaia
Generates browsable Obsidian wiki vault from Gaia knowledge package: skeleton structure, rewritten rich claim/section pages, cross-reference audits.
npx claudepluginhub siliconeinstein/gaia --plugin gaiaThis skill uses the workspace's default tool permissions.
Generate a rich Obsidian vault (`gaia-wiki/`) from a Gaia knowledge package.
Generates complete README for Gaia knowledge packages: fills narrative from reasoning graph skeleton, beliefs, and outline, then pushes to GitHub.
Maintains Obsidian-based LLM-driven wiki: ingests research papers/sources, compiles knowledge, manages topics/milestones/cross-references, queries wiki, runs lint checks.
Builds and maintains persistent Obsidian wiki vaults using AI for source ingestion, knowledge querying, note linting, and autonomous research.
Share bugs, ideas, or general feedback.
Generate a rich Obsidian vault (gaia-wiki/) from a Gaia knowledge package.
gaia-wiki/
├── claims/
│ ├── holes/ Leaf premises — reasoning chain endpoints
│ ├── intermediate/ Derived but not exported
│ ├── conclusions/ Exported claims ★ + questions
│ └── context/ Settings, background, structural
├── sections/ Narrative chapters (DSL module order)
│ ├── 01 - Introduction.md
│ ├── ...
│ ├── 07 - Weak Points.md
│ └── 08 - Open Questions.md
├── meta/ beliefs table, holes list
├── _index.md Claim Index + Sections + Reading Path
├── overview.md Simplified Mermaid
└── .obsidian/
aliases bridges them.Step 1: gaia compile + gaia infer
Step 2: gaia render --target obsidian → skeleton
Step 3: Read inputs (IR, beliefs, DSL, artifacts/)
Step 4: Rewrite every page
Step 5: Cross-reference audit
cat .gaia/ir.json
cat .gaia/beliefs.json
cat src/<package>/*.py
ls artifacts/
Read artifacts/ cover-to-cover before writing any page.
Core principle: Faithful reproduction. Each page replaces reading the paper for its topic.
Language: Follow user's preference. Frontmatter/wikilinks/Mermaid stay English.
claims/{holes,intermediate,conclusions,context}/*.md)Each claim is a self-contained article. #XX number = position in reasoning chain.
Section ordering:
#XX prefix.artifacts/. What problem? Prior work? Gap? Embed figures with ![[file]] + italic caption.[[label|#XX label]] for cross-referencesbeliefs.json and priors.py:
**Prior**: 0.95**Justification**: omega_D/E_F ~ 0.005; Migdal theorem validated.**Belief**: 0.71Depth by claim type:
| Type | Depth |
|---|---|
| Conclusions (★) | Most detailed — full derivation chain, multiple paragraphs per section |
| Holes | Focus on source provenance — where does this evidence come from? Method, precision, limitations |
| Intermediate | Full derivation of this step in the chain |
| Context | Brief — what it establishes and why it's assumed |
sections/*.md)Sections are narrative chapters that tell the paper's story. Claims within each section are sorted by topological order (evidence → derivation → conclusion).
Goal: A reader who reads sections 01 through 06 in order should understand the paper's complete argument without ever opening the original paper. Each section is a self-contained chapter of a "textbook rewrite" of the paper.
Page structure (from top to bottom):
Title — Descriptive narrative title in user's language. Keep number prefix.
Overview (10%) — 2-3 paragraphs setting up the section's question, approach, and key result.
Per-section Mermaid — Keep as-is.
Claims narrative (70% of the page — THIS IS THE MAIN BODY) — For EVERY claim in topo order, write a ### heading + 1-3 paragraphs. This is NOT optional. Every claim listed in the skeleton MUST appear with its narrative.
CRITICAL: This section is the bulk of the page. Do NOT skip it. The skeleton has ### [[label|#XX title]] entries — the agent must expand EACH ONE into a full narrative paragraph.
For each claim:
### [[label|#XX title]] heading (keep the wikilink)Exported conclusions should be highlighted:
### [[downfolded_bse|#43 下折叠 BSE]] ★
> [!IMPORTANT] 核心结论
> 完整的动量-频率 BSE 可以严格化简为仅依赖频率的一维积分方程,
> 误差仅 0.2%。
这是本章最重要的结果...
Chapter summary (10%) — 本章建立了什么,为下一章准备了什么。
Full section page example (showing the required structure):
# 03 - 从微观推导下折叠 Bethe-Salpeter 方程
## 概述
(2-3 paragraphs: question, approach, key result)
(Mermaid graph)
## 推理链
### [[pair_propagator_decomposition|#18 配对传播子分解]]
配对传播子 $GG$ 可以精确分解为低能相干部分 $\Pi_{\mathrm{BCS}}$
和高能非相干余项 $\phi$。这不是一个近似——而是一个数学恒等式。
相干部分携带 Cooper 对数 $\ln(\omega_c/T)$,定义了低能配对通道。
论文选择在双电子通道(而非传统的粒子-空穴通道)引入能量尺度
分离,这是一个关键创新——传统方案会导致低能区域库仑相互作用
失去屏蔽。这一选择为下面的交叉项压制论证奠定了基础。
### [[cross_term_suppressed|#19 交叉项压制]]
有了配对传播子分解,关键问题是:库仑和声子通道的交叉项是否
会破坏可分离性?论文利用等离子体极子模型给出了严格的上界估计:
交叉项被压制在 $O(\omega_c^2/\omega_p^2) \leq 1\%$。
这是整条推理链中最脆弱的一环——belief 仅 0.50,反映了 1% 这个
边界条件的不确定性。如果交叉项实际上更大,整个下折叠理论的
精度保证就会失效。
### [[downfolded_bse|#43 下折叠 BSE]] ★
> [!IMPORTANT] 核心结论
> 频率-only 下折叠 BSE:$\Lambda_\omega = \eta_\omega + \pi T
> \sum (\lambda - \mu_{\omega_c}) z^{ph}_{\omega'}/|\omega'| \Lambda_{\omega'}$
结合配对传播子分解和交叉项压制,完整 BSE 化简为仅含频率的
一维积分方程。$\mu^*$ 和 $\lambda$ 获得了精确的微观定义...
(... more claims ...)
## 本章小结
本章从微观出发严格推导了下折叠 BSE,为 $\mu^*$ 和 $\lambda$
提供了精确定义。这为第四章通过 vDiagMC 计算 $\mu^*$ 和第五章
验证 DFPT $\lambda$ 的可靠性奠定了理论基础。
DO NOT write a section page with only the overview and Mermaid — the claims narrative is the main content that readers come here to read.
Goal: A reader should understand WHERE the argument is weakest, WHY it's weak, and WHAT could fix it. This is a critical assessment, not a data dump.
The skeleton provides a table of the 10 lowest-belief claims. Agent should rewrite into a structured analysis:
Executive summary (1 paragraph) — The single most important takeaway. What is the weakest link in the entire reasoning chain? If you had to bet on which claim will fail, which one and why?
Structural analysis — Group weak points by their position in the reasoning graph:
For each major weak point (top 3-5), write a full paragraph:
Comparison with the paper's own assessment — Does the paper acknowledge these weaknesses? Does the reasoning graph reveal weaknesses the paper doesn't discuss?
Goal: A reader should know exactly what work remains to be done, prioritized by impact. This is a research roadmap derived from the reasoning graph.
The skeleton lists holes and questions. Agent should rewrite into:
Overview (1-2 paragraphs) — The big picture: what would make this knowledge package "complete"? What's the most impactful single improvement?
Open questions from the paper — If the IR has type: question nodes, explain each:
Evidence gaps (grouped by theme):
Experimental gaps:
Computational gaps:
Theoretical gaps:
Impact analysis — For each gap, trace forward through the reasoning graph:
Suggested next steps — Prioritized list of 3-5 actionable research directions, each with:
beliefs.md: intro + full belief table. holes.md: intro + leaf premises table.Faithful reproduction, not summarization. If the paper devotes 3 pages to a derivation, reproduce them in readable form. Include appendix material.
Every page must include:
[[label|#XX label]]Figure embeds — every ![[file]] must have italic caption:
![[8_0.jpg]]
*图 4:vDiagMC 计算的 μ_EF(r_s)。改编自 Cai et al.*