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/discover/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/discover)<a href="https://agentmods.dev/skills/lambenthan/empiricalwiki/discover"><img src="https://agentmods.dev/badge/skills/lambenthan/empiricalwiki/discover/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/discover"><img src="https://agentmods.dev/badge/skills/lambenthan/empiricalwiki/discover.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 | $0.00106 | $0.02786 |
| Opus 5.5 | $0.00042 | $0.01114 |
| Sonnet 5.5 | $0.00021 | $0.00557 |
| Haiku 4.5 | $0.00011 | $0.00279 |
Grade A, and why
discover 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 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.
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 — 169 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/discover
从三种 seed 模式之一产出一份排好序的候选论文 shortlist,附带每条候选的 rationale,呈现给用户或调用方 skill。
/discover绝不自动 ingest —— 它只负责提出候选,实际动作由/ingest负责。
按需打开下列本地参考文件:
references/seed-modes.md—— 如何把用户的措辞映射到 anchor / topic / wiki 模式,以及三者的选择规则references/ranking-signals.md——tools/discover.py的打分依据,以及为什么 discovery 不共享/init的 survey 偏好references/wiki-dedup.md—— 候选如何被过滤掉已 ingest 的论文,以及 dedup 边界
Inputs
--anchor <id>(可重复):一或多个 anchor 论文 ID(优先 arXiv ID,也接受 S2 paperId)。驱动 anchor 模式 —— 主要使用场景,包括/ingest后的 "接下来该读什么"。--negative <id>(可重复,可选):希望推开的论文 ID。只在配合--anchor时有意义。--topic "<str>":topic / query 字符串。驱动 topic 模式 —— 相对/initplanner 更轻量的替代。--from-wiki:自动从 wiki 最近修改过的论文页派生 seed。驱动 wiki 模式。--limit N(可选,默认 10):shortlist 最大长度。
--anchor、--topic、--from-wiki 三者必须恰好选一。
Outputs
.checkpoints/discover-{seed-slug}-{YYYY-MM-DD}.json—— 完整 shortlist payload,机器可读;seed slug 基于首个 anchor 或 topic 派生- 给用户的 markdown 摘要,包含每条候选的 rationale
wiki/log.md—— 通过tools/research_wiki.py log追加一行
/discover 除了 log.md 外不向 wiki/ 写入任何内容,也不触碰 raw/。是否把候选拉进 wiki 是调用方的决定(之后的 /ingest)。
Wiki Interaction
Reads
wiki/papers/*.md—— frontmatter 中的arxiv(或旧版arxiv_id),用于与已 ingest 的论文做 dedupwiki/papers/*.md修改时间 —— 用于--from-wiki模式下 anchor 选取
Writes
wiki/log.md—— 通过tools/research_wiki.py logAPPEND
Graph edges created
- 无。图变更属于
/ingest,不属于/discover。
Workflow
前置条件:工作目录包含 wiki/、raw/、tools/。一次解析 Python 解释器路径并复用:
if [ -x .venv/bin/python ]; then
PYTHON_BIN=.venv/bin/python
elif [ -x .venv/Scripts/python.exe ]; then
PYTHON_BIN=.venv/Scripts/python.exe
else
PYTHON_BIN=python3
fi
export PYTHON_BIN
Step 1: 选定 seed 模式
把用户请求映射到 from-anchors、from-topic 或 from-wiki 之一。决策规则见 references/seed-modes.md,简版:
- 用户指明了一或多篇具体论文,或者这是
/ingest --discover的后续 → anchors - 用户给的是 topic / 方向 / 关键词 → topic
- 用户问开放式 "接下来读什么",没有 anchor 也没有 topic → wiki
What ships with it
3 files 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.
- 1mo ago First seen · 169 lines · 106 tokens per session scan A 465a44b67f93
discover is a skill published in the GitHub repository Lambenthan/empiricalwiki (96 stars, last pushed 4mo ago), licensed MIT. It adds 106 tokens to every session and 2,786 once invoked, about $0.0004 per session on Opus 5.5. 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
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.