Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add AkaliKong/PaperClaw --skill paper-seed-initgit clone --depth 1 https://github.com/AkaliKong/PaperClawWrote 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/akalikong/paperclaw/paper-seed-init)<a href="https://agentmods.dev/skills/akalikong/paperclaw/paper-seed-init"><img src="https://agentmods.dev/badge/skills/akalikong/paperclaw/paper-seed-init/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/akalikong/paperclaw/paper-seed-init"><img src="https://agentmods.dev/badge/skills/akalikong/paperclaw/paper-seed-init.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 | $0.00055 | $0.01034 |
| Opus 5 | $0.00028 | $0.00517 |
| Sonnet 5 | $0.00011 | $0.00207 |
| Haiku 4.5 | $0.00006 | $0.00103 |
Grade A, and why
paper-seed-init 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 12d 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 — 114 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Paper Seed Init — 核心论文目录初始化
概述
本 Skill 管理核心论文目录(Seed Papers),它是整个论文阅读 Pipeline 的学术品味锚点。核心论文为搜索去重、打分校准、精读参考和 Idea 生成提供基准。
⚠️ 关键规则
- 首次使用必须先初始化:Pipeline 运行前,必须确保
seed_papers.json已生成。 - 增量更新:当用户修改
profile.yaml中的seed_papers列表时,脚本只拉取新增论文,不会重新拉取已有论文。 - 自动注册 seen_papers.json:所有核心论文 ID 会自动注册到去重表,避免被搜索模块重复抓取。
工作流程
初始化核心论文 — "初始化核心论文"
执行初始化脚本:
python $PAPER_AGENT_ROOT/scripts/seed_init.py
脚本将:
- 读取
profile.yaml中的seed_papersarXiv ID 列表 - 对每个 arXiv ID 调用
ArxivSearcher拉取元数据(title、authors、abstract、url、categories、comments) - 合并已有
seed_papers.json中的手动 JSON 条目(方式 B) - 为每篇论文提供可编辑的字段:
user_note、role(foundational/benchmark/inspiring/my_work)、sub_field、key_concepts - 将所有核心论文 ID 注册到
seen_papers.json(标记source: "seed") - 输出/更新
seed_papers.json
增量更新 — "更新种子论文"
python $PAPER_AGENT_ROOT/scripts/seed_init.py --update
脚本将:
- 对比
profile.yaml中的seed_papers与现有seed_papers.json - 仅拉取新增论文的元数据
- 保留已有论文的手动标注(user_note、role 等)
输出文件
seed_papers.json
每条记录格式:
{
"arxiv_id": "2305.05065",
"title": "Recommender Systems with Generative Retrieval",
"authors": ["Shashank Rajput", "..."],
"abstract": "...",
"url": "https://arxiv.org/abs/2305.05065",
"published_date": "2023-05-08",
"categories": ["cs.IR", "cs.AI"],
"comments": "Accepted at NeurIPS 2023",
"user_note": "",
"role": "foundational",
"sub_field": "generative_rec",
"key_concepts": ["Semantic ID", "RQ-VAE", "autoregressive retrieval"],
"has_card": false,
"card_path": ""
}
seen_papers.json 注册
每篇核心论文自动注册:
{
"2305.05065": {
"source": "seed",
"first_seen_date": "2026-03-01",
"first_seen_run_id": "seed_init"
}
}
核心论文角色(role)说明
| 角色 | 说明 | 用途 |
|---|---|---|
foundational |
奠基性论文(见 seed_papers.json) | few-shot 打分示例(≤3 篇) |
benchmark |
重要对比基线(如 SASRec、P5) | 实验规划参考 |
inspiring |
启发性论文 | Idea 碰撞素材 |
my_work |
用户自己的论文 | 知识库锚定 |
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.
- 12d ago First seen · 114 lines · 55 tokens per session scan A 3c4901d4c468
paper-seed-init is a skill published in the GitHub repository AkaliKong/PaperClaw (22 stars, last pushed 6mo ago), licensed MIT. It adds 55 tokens to every session and 1,034 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
instrument-data-to-allotrope
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full…
matlab
Build, review, migrate, and safely plan MATLAB or GNU Octave numerical workflows, including arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.
exploratory-data-analysis
Perform bounded, local exploratory analysis of explicitly supported scientific files. Use for redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation sensitivity; and rigorous EDA report scaffolds. Other domain…
phylogenetics
Build and analyze phylogenetic trees using MAFFT (multiple alignment), IQ-TREE 2 (maximum likelihood), and FastTree (fast NJ/ML). Visualize with ETE3 or FigTree. For evolutionary analysis, microbial genomics, viral phylodynamics, protein family analysis, and molecular clock studies.
research-engineer
An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.
mapping-to-snomed
Maps clinical concept spans extracted by OpenMed to SNOMED CT concepts through a USER-SUPPLIED terminology server (the user's own Ontoserver, Snowstorm, or UMLS/UTS), never a bundled vocabulary. Use when the user wants to code findings, disorders, procedures, body structures, or substances to SNOMED CT, run an ECL…