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/prefill/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/prefill)<a href="https://agentmods.dev/skills/lambenthan/empiricalwiki/prefill"><img src="https://agentmods.dev/badge/skills/lambenthan/empiricalwiki/prefill.svg" alt="Measured on agentmods" 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 | $0.00028 | $0.01721 |
| Opus 5 | $0.00014 | $0.00860 |
| Sonnet 5 | $0.00006 | $0.00344 |
| Haiku 4.5 | $0.00003 | $0.00172 |
Grade A, and why
prefill 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 8d 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 — 161 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/prefill
将领域基础知识(奠基性方法、common practice、标准架构)作为终端页面沉淀到
wiki/foundations/。 Foundations 设计上单向:其他页面可以链接到 foundation,foundation 不写反向链接。
Trigger
手动:/prefill [domain] 或 /prefill --add "概念名"。
Inputs
domain(位置参数,可选):研究领域 —general/NLP/CV/ML Systems/Robotics之一。未指定时从wiki/topics/标签推断;若 topics 为空则提示用户。--add "<概念>":跳过 catalog,直接为单个概念建立 foundation。
Outputs
wiki/foundations/{slug}.md— 每个种子一页- 更新的
wiki/index.md(由rebuild-index重生成 foundations 段落) wiki/log.md一条记录
Wiki Interaction
读取
wiki/topics/*.md— 用于推断 domain(未指定时)wiki/foundations/*.md— 跳过已存在的 foundation(幂等).claude/skills/prefill/foundations-catalog.yaml— 种子列表
写入
wiki/foundations/{slug}.md(仅新建,从不覆盖)wiki/index.md(通过tools/research_wiki.py rebuild-index)wiki/log.md(通过tools/research_wiki.py log)
Workflow
前置条件:当前目录包含 wiki/、tools/、.claude/。WIKI_ROOT=wiki/。
Step 1: 确定 domain
- 用户传入
domain→ 直接使用。 - 否则若处于
--add模式 → 默认general,除非用户额外指定。 - 否则:读取
wiki/topics/*.md的 frontmattertags;若能识别出主导 domain 则用之,否则提示用户。
Step 2: 加载种子
- Catalog 模式:读取
.claude/skills/prefill/foundations-catalog.yaml,取domains.{domain}下所有条目,并叠加domains.general的全部条目(general foundations 适用所有领域)。 --add模式:构造单一种子{slug: <slugified concept>, title: <concept>, summary: ""}。Slug 用python3 tools/research_wiki.py slug "<concept>"生成。
对每个种子,若 wiki/foundations/{slug}.md 已存在则跳过(不覆盖、不警告)。
Step 3: 从 Wikipedia 拉取背景
对每个剩余种子调用 tools/fetch_wikipedia.py:
python3 tools/fetch_wikipedia.py summary "<title>"
python3 tools/fetch_wikipedia.py sections "<title>"
python3 tools/fetch_wikipedia.py section "<title>" --index <N> # 拉取相关章节
- summary 调用返回
{title, extract, url}。 - sections 调用返回
[{index, line, level}, ...]— 选取line包含Variants/Types/Architecture/History/Limitations/Applications的章节(大小写不敏感子串匹配)。 - 任意调用退出码为
2表示页面不存在 — 该种子回退到 LLM 知识,生成的 frontmatter 中source_url: ""。
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 8d ago First seen · 161 lines · 28 tokens per session scan A 20a3c6395b78
prefill is a skill published in the GitHub repository Lambenthan/empiricalwiki (82 stars, last pushed 2mo ago), licensed MIT. It adds 28 tokens to every session and 1,721 once invoked, about $0.0001 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
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…
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.
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…
preregister
Draft a structured preregistration document (OSF, AsPredicted, or AEA RCT Registry style) from a research spec or free-form study description. Output is a Markdown file with hypotheses, design, sampling plan, analysis plan, exclusions, and inference criteria — annotated with MUST / SHOULD / MAY clarity flags. Use when…
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…