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 patsnap/mcp --skill patsnap-company-deal-intelligencegit clone --depth 1 https://github.com/patsnap/mcpWrote 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/patsnap/mcp/patsnap-company-deal-intelligence)<a href="https://agentmods.dev/skills/patsnap/mcp/patsnap-company-deal-intelligence"><img src="https://agentmods.dev/badge/skills/patsnap/mcp/patsnap-company-deal-intelligence/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/patsnap/mcp/patsnap-company-deal-intelligence"><img src="https://agentmods.dev/badge/skills/patsnap/mcp/patsnap-company-deal-intelligence.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 | $0.00050 | $0.00703 |
| Opus 5 | $0.00025 | $0.00351 |
| Sonnet 5 | $0.00010 | $0.00141 |
| Haiku 4.5 | $0.00005 | $0.00070 |
Grade A, and why
patsnap-company-deal-intelligence 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 13d 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 — 77 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Setup
Get your API Key at https://open.patsnap.com
Patsnap Company & Deal Intelligence
This skill connects your AI agent to Patsnap's Company & Deal Intelligence MCP server — providing professional-grade life sciences intelligence.
Intangible asset and commercial intelligence archive tool for pharmaceutical enterprises, covering organization profiles, R&D pipelines, deal records, and financial report evidence queries.
Prerequisites
This skill requires the Patsnap Company & Deal Intelligence MCP server to be configured in your environment:
{
"mcpServers": {
"company_deal_intelligence": {
"url": "https://connect.patsnap.com/1f8934/logic-mcp?apikey=YOUR_API_KEY",
"type": "streamableHttp"
}
}
}
Get your API key at open.patsnap.com. For the full list of available tools and input parameters, refer to the official MCP server documentation: https://open.patsnap.com/marketplace/mcp-servers/company-deal-intelligence
Instructions for AI Agents
Step 1: Normalize Entities First
Before executing any search or fetch operation, normalize targets, drugs, diseases, companies, and clinical trial IDs to Patsnap internal IDs when possible. This improves retrieval accuracy.
Step 2: Choose the Right Tool
Select search tools for discovery and corresponding _fetch tools for full records. Use vector search tools for natural-language evidence queries.
Step 3: Fetch Full Records
Search tools return summary results with IDs. Follow up with the appropriate _fetch tool when the user needs complete details.
Step 4: Synthesize and Structure Output
Lead with key findings, cite sources, highlight data gaps, and use tables for comparisons.
Example Workflows
Company Profile
- Use
organization_fetchto retrieve company details. - Fetch pipeline with
organization_pipeline_fetch. - Search
drug_deal_searchfor recent BD activity.
Deal Benchmarking
- Search
drug_deal_searchby target or disease area. - Fetch full deal terms with
drug_deal_fetch. - Compare deal values and structures across assets.
What ships with it
2 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.
- 13d ago First seen · 77 lines · 50 tokens per session scan A bb641b328f9e
patsnap-company-deal-intelligence is a skill published in the GitHub repository patsnap/mcp (111 stars, last pushed 23d ago), licensed Apache-2.0. It adds 50 tokens to every session and 703 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
usfiscaldata
Query the U.S. Treasury Fiscal Data REST API for federal financial data. No API key required. Use for national debt (Debt to the Penny), Daily Treasury Statements, Monthly Treasury Statements, Treasury securities auctions, interest rates, foreign exchange rates, savings bonds, or U.S. government revenue and spending…
glycoengineering
Analyze and engineer protein glycosylation. Scan sequences for canonical N-glycosylation sequons (N-X-S/T with X not proline, including overlapping sites), predict O-GalNAc hotspots, read glycan notation, and reach the curated external tooling (NetNGlyc, NetOGlyc, GlycoShield, GlycoWorkbench, GlyTouCan, GlyConnect).…
molecular-dynamics
Run and analyze molecular dynamics simulations with OpenMM and MDAnalysis. Set up protein and protein-ligand systems with PDBFixer, choose force fields and water models (AMBER14, CHARMM36m, ff19SB, GAFF2, TIP3P), solvate and add ions, run energy minimization, NVT/NPT equilibration and production MD on GPU, then…
molfeat
Molecular featurization hub with one consistent interface over 100+ featurizers. Fingerprints (ECFP/Morgan, MACCS, atom pair, topological torsion, Avalon, RDKit, ERG), RDKit and Mordred descriptor sets, pharmacophore and 3D shape descriptors, scaffold keys, and pretrained embeddings (ChemBERTa, ChemGPT, MolT5, GIN…
rowan
Rowan is a cloud-native molecular modeling and medicinal-chemistry workflow platform with a Python API. Use for pKa and macropKa prediction, conformer and tautomer ensembles, docking and analogue docking, protein-ligand cofolding, MSA generation, molecular dynamics, permeability, descriptor workflows, and related…
autodock-vina
Structure-based docking with AutoDock Vina, Vinardo, and AutoDock4 through the Meeko toolchain. Use this skill to define a docking box, prepare receptors and ligands as PDBQT, run single or batch docking, rescore, and interpret affinities, poses, and ligand efficiency. Covers box definition from a reference ligand or…