Borrowing it
Nothing to install: this file belongs to Lambenthan/empiricalwiki. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Lambenthan/empiricalwiki/main/.claude/skills/theory-ingest/SKILL.mdgit clone --depth 1 https://github.com/Lambenthan/empiricalwikiWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/lambenthan/empiricalwiki/theory-ingest)<a href="https://agentmods.dev/skills/lambenthan/empiricalwiki/theory-ingest"><img src="https://agentmods.dev/badge/skills/lambenthan/empiricalwiki/theory-ingest/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/lambenthan/empiricalwiki/theory-ingest"><img src="https://agentmods.dev/badge/skills/lambenthan/empiricalwiki/theory-ingest.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00054 | $0.01804 |
| Opus 5 | $0.00027 | $0.00902 |
| Sonnet 5 | $0.00011 | $0.00361 |
| Haiku 4.5 | $0.00005 | $0.00180 |
Grade A, and why
theory-ingest scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/theory-ingest
把一篇理论建模论文拆成可复用的研究资产。对着「理论建模通用骨架」的 6 个固定槽位抽取:环境/原语、解概念、结果、论证、比较静态、可检验推论。形式化陈述逐字引用,禁止释义。
Inputs
source: 本地.pdf、.tex,或/init预处理后的raw/tmp/...路径。--topic可选:当前项目主题,如“管理者短视与耐心资本”。
Outputs
wiki/papers/{slug}.md(paper_kind: theory)- 按需新建或更新:
wiki/assumptions/*.mdwiki/propositions/*.mdwiki/hypotheses/*.md(模型导出的可检验推论)wiki/mechanisms/*.md(填## Theoretical Logic,与实证层共享)wiki/foundations/*.md(解概念、证明框架等可复用规范知识)
wiki/index.md、wiki/log.mdwiki/graph/edges.jsonl
Workflow
Step 1: Resolve Source
确认工作目录是项目根目录,包含 wiki/、raw/、tools/。
优先使用 .venv:
if [ -x .venv/bin/python ]; then PYTHON_BIN=.venv/bin/python; else PYTHON_BIN=python3; fi
理论论文公式密集,.tex 优先:有 .tex 源码就用源码,公式不会被 PDF 解析毁掉。只有 PDF 时先人工读第一页标题:
"$PYTHON_BIN" - "<source>" <<'PY'
import sys, fitz
doc = fitz.open(sys.argv[1])
print(doc[0].get_text("text")[:2000])
PY
然后运行:
"$PYTHON_BIN" tools/prepare_paper_source.py --raw-root raw --source <source> --title "<confident-title>"
把返回的 prepared_path 作为正文读取入口。
Step 2: Extract Along the 6-Slot Skeleton
先打开 docs/runtime-theory-skeleton.zh.md,对着 6 个固定槽位抽取。没有明确证据写“未报告”,不猜:
- 环境 / 原语:参与人、行动/策略空间、偏好/支付函数、信息结构、时序、技术/约束
- 解概念:均衡概念(Nash / 子博弈精炼 / 贝叶斯精炼 / 竞争均衡 …)
- 结果:每个命题/定理/引理的形式化陈述 + 成立条件
- 论证:每个结果的证明技术
- 比较静态:内生量随参数怎么动
- 可检验推论:模型导出的、可拿去实证的符号关系
理论论文没有 variables/datasets/identification/robustness——这些不是填“未报告”,而是整块不存在。这是与 /empirical-ingest 的根本分流。
Step 3: Write Pages
打开 docs/runtime-page-templates.zh.md,按模板写页面。
papers/{slug}.md 设 paper_kind: theory,填 theory block,正文用理论段落:
## 研究问题
## 模型环境
## 核心假设
## 解概念
## 命题与证明
## 比较静态
## 可检验推论
## 对实证的启发
## Related
- 每条核心假设 →
assumptions/,formal_statement逐字引用原文 - 每个命题/定理 →
propositions/,formal_statement逐字引用,conditions写清成立条件,proof_technique查不到写“未报告” - 每条可检验推论 →
hypotheses/(status: literature_supported) - 模型形式化的经济机制 → 查重后更新
mechanisms/的## Theoretical Logic段;不要新造与实证侧重复的机制页 - 解概念、证明框架等可复用规范知识 →
foundations/(终端节点,不写反向链接)
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 140 lines · 54 tokens per session scan A 620b965c9c70
theory-ingest is a skill published in the GitHub repository Lambenthan/empiricalwiki (82 stars, last pushed 2mo ago), licensed MIT. It adds 54 tokens to every session and 1,804 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
audit-reproducibility
Enforce the replication-protocol.md rule by cross-checking numeric claims in a manuscript against the actual R / Stata / Python outputs. Report PASS/FAIL per claim against tolerance thresholds. Use before submission and before releasing a replication package.
review-paper
Comprehensive manuscript review with three modes: single-pass (default), --adversarial critic-fixer loop, and --peer [journal] simulated peer-review pipeline (editor + 2 dispositioned referees + editorial decision, calibrated to a target journal). R&R continuation via --peer --r2/--r3; hostile-editor stress test via…
capture-environment
Snapshot the computational environment for a replication package — detects the analysis stack (R / Stata / Python) and emits the right lockfiles (renv.lock + sessionInfo.txt, requirements.txt / environment.yml / uv.lock, Stata version + ado package list), records seeds and RNG kind, optionally writes a pinning…
data-management-plan
Draft a funder-compliant Data Management Plan (NSF DMP, NIH DMS Policy 2023, ERC, Horizon Europe) by composing the confidential-data and environment-capture primitives. Sections cover data description, formats/metadata, storage/backup, access/sharing, preservation/archiving, and roles. Use when user says "data…
grant-proposal
Scaffold a research grant proposal (NSF, NIH, ERC, or foundation) by composing existing primitives — pulls identification strategy from an /interview-me spec, delegates the data-management plan to /data-management-plan and the facilities statement to /capture-environment, and emits a funder-requirements checklist. Use…
power-analysis
Compute statistical power, required sample size, and minimum detectable effect (MDE) for a study design, then write a registry-ready power section. Handles two-arm RCTs (with clustering / ICC and unequal allocation), multiple-arm corrections, and a simulation-based power option for non-standard designs…