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/setup/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/setup)<a href="https://agentmods.dev/skills/lambenthan/empiricalwiki/setup"><img src="https://agentmods.dev/badge/skills/lambenthan/empiricalwiki/setup/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/setup"><img src="https://agentmods.dev/badge/skills/lambenthan/empiricalwiki/setup.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 | $0.00034 | $0.02472 |
| Opus 5.5 | $0.00014 | $0.00989 |
| Sonnet 5.5 | $0.00007 | $0.00494 |
| Haiku 4.5 | $0.00003 | $0.00247 |
Grade B, and why
setup scanned grade B with 2 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 1mo 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.
Sends data to an external URLmediumData exfiltration
A POST to an outside endpoint may be telemetry or may be exfiltration; either way the mod talks to somewhere, and you should know where.
resp = requests.post('https://data.rag.ac.cn/api/register/sdk', json=payload, timeout=30) Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
resp = requests.post('https://data.rag.ac.cn/api/register/sdk', json=payload, timeout=30) How it starts
The opening of the file, as written. The whole thing — 280 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/setup
引导你完成 ΩmegaWiki 的可选 API key 配置。 读取当前
.env,展示已配置和未配置的内容,并帮助你逐步设置每个 key, 包括清晰解释每个 key 的作用和获取方式。 可随时重新运行,只更新你选择配置的 key。
Inputs
- 不需要任何参数
- 读取:
.env(当前配置状态) - 读取:
config/setup-guide.md(每个 key 的参考说明)
Outputs
- 更新后的
.env(包含新配置的 key) - 当前配置状态总结
Wiki Interaction
Reads
- 无(setup 在 wiki 创建之前运行)
Writes
- 无(不修改 wiki)
Workflow
Step 1:读取配置参考文档
读取 config/setup-guide.md,加载所有可配置 key 的完整参考信息,
包括每个 key 的作用、使用它的 skill、获取方式以及未配置时的降级行为。
Step 2:检测当前环境
运行以下命令检查已配置的内容:
python3 -c "
import sys, os
sys.path.insert(0, 'tools')
try:
import _env
except Exception:
pass
keys = {
'SEMANTIC_SCHOLAR_API_KEY': 'Semantic Scholar',
'DEEPXIV_TOKEN': 'DeepXiv',
'LLM_API_KEY': 'Review LLM(API key)',
'LLM_BASE_URL': 'Review LLM(base URL)',
'LLM_MODEL': 'Review LLM(模型名)',
}
for k, label in keys.items():
v = os.environ.get(k, '').strip()
print(f'SET:{k}' if v else f'UNSET:{k}')
"
同时检测 Python 环境和 .venv 状态:
ls .venv/ 2>/dev/null && echo "venv:present" || echo "venv:absent"
python3 --version
Step 3:展示配置状态
向用户展示清晰的状态总结,按状态分组:
ΩmegaWiki 配置状态
================================
✓ ANTHROPIC_API_KEY — 由 Claude Code 管理(claude login)
推荐配置:
✗ Semantic Scholar — 未配置(引用链扩展速度慢 3 倍,建议配置免费 key)
可选:
✗ DeepXiv — 未配置(语义搜索不可用)
✗ Review LLM — 未配置(跨模型 review 不可用)
询问用户:"您想配置哪些?(可以跳过任意一个或全部)"
Step 4:配置各 Key(用户决定)
对用户想要配置的每个 key,按以下子流程处理。
写入 .env 前必须向用户确认。
4a:Semantic Scholar API Key
解释:"Semantic Scholar 提供论文引用数据和检索功能。 被 /ingest、/init、/novelty、/ideate 使用。免费获取。 推荐配置 — 不配置的话,/init 速度慢 3 倍,引用链扩展效率大幅下降。"
引导获取:"访问 https://www.semanticscholar.org/product/api, 点击 'Get API Key',免费申请。"
询问:"您是否有 Semantic Scholar API key?(粘贴,或输入 'skip' 跳过)"
如果提供了 key,写入 .env:
使用 Edit 工具更新 .env:
- 若已有
SEMANTIC_SCHOLAR_API_KEY=(即使为空),替换该行 - 否则追加
SEMANTIC_SCHOLAR_API_KEY=<值>
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.
- 1mo ago First seen · 280 lines · 34 tokens per session scan B f851b9ab388c
setup is a skill published in the GitHub repository Lambenthan/empiricalwiki (96 stars, last pushed 4mo ago), licensed MIT. It adds 34 tokens to every session and 2,472 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it B with 2 findings (sends data to an external url, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
paper-workflow
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accounting-software-selection
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accounting-audit-system-builder
Routes an accounting or audit request to the right module skill, from software selection through monthly closing, asking only what is missing. Use for books of accounts or audit files.
patent-map
A tool for making a visual map of stored patent case notes in an Obsidian vault. Obsidian is a note-taking application that stores linked text files.
chinese-git-workflow
A reference for configuring Git with Chinese code-hosting services such as Gitee, Coding.net, GitLab China, and CNB, including SSH, HTTPS, credentials, CI, and repository mirroring.
chinese-documentation
A Chinese technical-documentation style guide covering spacing, punctuation, numbers, terminology, and links when Chinese and English appear together.