ScienceClaw is an agent system for scientific work that completes tasks as typed, executable workflows and turns replay-verified improvements into reusable skills and operators. It is intended for AI-assisted research across natural and social science disciplines. The catalogue entries are its skills and instruction for use with the system.
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 beita6969/ScienceClaw --skill feishu-docgit clone --depth 1 https://github.com/beita6969/ScienceClawWrote 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/beita6969/scienceclaw/feishu-doc)<a href="https://agentmods.dev/skills/beita6969/scienceclaw/feishu-doc"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/feishu-doc/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/beita6969/scienceclaw/feishu-doc"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/feishu-doc.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00027 | $0.01283 |
| Opus 5.5 | $0.00011 | $0.00513 |
| Sonnet 5.5 | $0.00005 | $0.00257 |
| Haiku 4.5 | $0.00003 | $0.00128 |
Grade A, and why
feishu-doc 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.
Copies of this mod
4 near-identical copies found in the catalogue:
- feishu-doc — 100% identical, 0 lines differ
- feishu-doc — 100% identical, 0 lines differ
- feishu-doc — 100% identical, 0 lines differ
- feishu-doc — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 212 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Feishu Document Tool
Single tool feishu_doc with action parameter for all document operations, including table creation for Docx.
Token Extraction
From URL https://xxx.feishu.cn/docx/ABC123def → doc_token = ABC123def
Actions
Read Document
{ "action": "read", "doc_token": "ABC123def" }
Returns: title, plain text content, block statistics. Check hint field - if present, structured content (tables, images) exists that requires list_blocks.
Write Document (Replace All)
{ "action": "write", "doc_token": "ABC123def", "content": "# Title\n\nMarkdown content..." }
Replaces entire document with markdown content. Supports: headings, lists, code blocks, quotes, links, images ( auto-uploaded), bold/italic/strikethrough.
Limitation: Markdown tables are NOT supported.
Append Content
{ "action": "append", "doc_token": "ABC123def", "content": "Additional content" }
Appends markdown to end of document.
Create Document
{ "action": "create", "title": "New Document", "owner_open_id": "ou_xxx" }
With folder:
{
"action": "create",
"title": "New Document",
"folder_token": "fldcnXXX",
"owner_open_id": "ou_xxx"
}
Important: Always pass owner_open_id with the requesting user's open_id (from inbound metadata sender_id) so the user automatically gets full_access permission on the created document. Without this, only the bot app has access.
List Blocks
{ "action": "list_blocks", "doc_token": "ABC123def" }
Returns full block data including tables, images. Use this to read structured content.
Get Single Block
{ "action": "get_block", "doc_token": "ABC123def", "block_id": "doxcnXXX" }
Update Block Text
{
"action": "update_block",
"doc_token": "ABC123def",
"block_id": "doxcnXXX",
"content": "New text"
}
Delete Block
{ "action": "delete_block", "doc_token": "ABC123def", "block_id": "doxcnXXX" }
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.
- 1mo ago First seen · 212 lines · 27 tokens per session scan A 6df9c4a747e1
feishu-doc is a skill published in the GitHub repository beita6969/ScienceClaw (1,019 stars, last pushed 10d ago), licensed MIT. It adds 27 tokens to every session and 1,283 once invoked, about $0.0001 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
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venue-templates
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box
Box manages cloud files, sharing, search, and metadata.
pdf-toolkit
Structured .pdf operations: extract text/tables, merge pages from multiple PDFs, split a PDF by page ranges, fill PDF form fields, and generate fresh PDFs from JSON. Trigger when the user wants programmatic PDF work without natural-language rewriting — examples: pull tables from a report, combine three PDFs, extract…
treatment-plans
Generate concise (3-4 page), focused medical treatment plans in LaTeX/PDF format for all clinical specialties. Supports general medical treatment, rehabilitation therapy, mental health care, chronic disease management, perioperative care, and pain management. Includes SMART goal frameworks, evidence-based…