Getting it into your agent
There is no command for this one: it runs only inside a plugin, and the catalogue could not identify which plugin ships it. The source is linked below.
Wrote 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/ask)<a href="https://agentmods.dev/skills/lambenthan/empiricalwiki/ask"><img src="https://agentmods.dev/badge/skills/lambenthan/empiricalwiki/ask.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 | $0.00020 | $0.02720 |
| Opus 5 | $0.00010 | $0.01360 |
| Sonnet 5 | $0.00004 | $0.00544 |
| Haiku 4.5 | $0.00002 | $0.00272 |
Grade A, and why
ask 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 6d 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 — 211 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/ask
对 wiki 知识库提问。LLM 读取 context_brief.md 获取全局上下文,检索相关页面, 综合回答并附带引用。好的回答可以 crystallize 回 wiki——写入 outputs/ 或创建新的 concept/claim 页面,让探索成果像 ingest 一样持续积累。
Inputs
question:自然语言问题(如 "LoRA 和 Adapter 的核心区别是什么?")--crystallize(可选):若指定,将回答 crystallize 回 wiki(默认仅回答不写入)--format(可选):输出格式,默认markdown,可选table/timeline/bullets
Outputs
- 始终:终端输出综合回答(含
[[slug]]引用) - 若 crystallize:
wiki/outputs/{query-slug}.md— 查询结果页面(默认 crystallize 目标)- 或
wiki/concepts/{slug}.md— 若回答揭示了新的跨论文概念 - 或
wiki/claims/{slug}.md— 若回答发现了可验证的新断言 - 更新的
wiki/graph/edges.jsonl(crystallize 产生的关系) - 更新的
wiki/index.md和wiki/log.md
Wiki Interaction
Reads
wiki/graph/context_brief.md— 全局压缩上下文(claims, gaps, failed ideas, papers, edges)wiki/index.md— 页面目录,用于定位相关页面wiki/graph/open_questions.md— 开放问题,辅助判断问题是否涉及已知知识缺口wiki/papers/*.md— 与问题相关的论文页面wiki/concepts/*.md— 与问题相关的概念页面wiki/claims/*.md— 与问题相关的 claim 页面wiki/topics/*.md— 与问题相关的 topic 页面wiki/people/*.md— 若问题涉及特定研究者wiki/ideas/*.md— 若问题涉及研究想法或 failed ideaswiki/experiments/*.md— 若问题涉及实验结果wiki/Summary/*.md— 若问题涉及领域全景
Writes(仅 crystallize 模式)
wiki/outputs/{query-slug}.md— CREATE(查询结果页面)wiki/concepts/{slug}.md— CREATE(新发现概念)或 EDIT(补充已有概念)wiki/claims/{slug}.md— CREATE(新发现断言)或 EDIT(补充 evidence)wiki/graph/edges.jsonl— APPEND(crystallize 产生的关系)wiki/graph/context_brief.md— REBUILD(若 crystallize 创建了新页面)wiki/graph/open_questions.md— REBUILD(若 crystallize 创建了新页面)wiki/index.md— EDIT(若 crystallize 创建了新页面)wiki/log.md— APPEND
Graph edges created(仅 crystallize)
output → paper:derived_from(回答引用的论文)output → concept:derived_from(回答引用的概念)output → claim:derived_from(回答引用的 claim)concept → paper:supports(若新概念从论文中归纳)claim → paper:supports(若新 claim 从论文中提取)
Workflow
前置:确认工作目录为 wiki 项目根(包含 wiki/、raw/、tools/ 的目录)。
设 WIKI_ROOT=wiki/。
Step 1: 加载全局上下文
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.
- 6d ago First seen · 211 lines · 20 tokens per session scan A b5d9de181628
ask is a skill published in the GitHub repository Lambenthan/empiricalwiki (82 stars, last pushed 2mo ago), licensed MIT. It adds 20 tokens to every session and 2,720 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.
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