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 PKU-YuanGroup/OpenAI4S --skill bio-chemoinformatics-protac-degradersgit clone --depth 1 https://github.com/PKU-YuanGroup/OpenAI4SWrote 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/pku-yuangroup/openai4s/bio-chemoinformatics-protac-degraders)<a href="https://agentmods.dev/skills/pku-yuangroup/openai4s/bio-chemoinformatics-protac-degraders"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-chemoinformatics-protac-degraders/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/pku-yuangroup/openai4s/bio-chemoinformatics-protac-degraders"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-chemoinformatics-protac-degraders.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 | $0.00126 | $0.04657 |
| Opus 5 | $0.00063 | $0.02329 |
| Sonnet 5 | $0.00025 | $0.00931 |
| Haiku 4.5 | $0.00013 | $0.00466 |
Grade A, and why
bio-protac-degraders 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 9d 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.
This is a copy
95% identical to bio-protac-degraders — 12 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 279 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: PRosettaC (web service), DeepTernary research code, AlphaFold3, Boltz-1 / Boltz-2, RDKit 2024.09+, OpenMM 8.1+ (for ternary MD).
Before using code patterns, verify installed versions match. If versions differ:
- Python:
pip show <package>thenhelp(module.function)to check signatures
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
PROTAC and Bivalent Degrader Design
Design bifunctional molecules (PROTACs) that recruit an E3 ubiquitin ligase to a target protein, inducing target ubiquitination and proteasomal degradation. PROTACs differ from traditional drugs: a productive ternary complex (target + PROTAC + E3) is required, not just target binding. The modality has produced clinical programs, but their development and regulatory status changes rapidly and must be checked from current sources. PROTAC design balances target ligand binding, E3 ligand binding, linker geometry (length, rigidity, chemistry), cooperativity, dose-dependent ternary-complex formation, and cell permeability. Negative cooperativity and the high-concentration hook effect are distinct phenomena, although cooperativity can influence the dose-response profile.
For target ligand design, see chemoinformatics/virtual-screening and chemoinformatics/admet-prediction. For linker-only enumeration, see chemoinformatics/reaction-enumeration. For generative linker design, see chemoinformatics/generative-design.
E3 Ligase Choice
| Recruited UPS component | Ligand series | Published design context | Limitations |
|---|---|---|---|
| VHL | VL-269 (Gechijian et al. 2018) | Published VHL-recruiting degraders | Expression and productive geometry are system-dependent |
| CRBN (cereblon) | thalidomide, pomalidomide | Extensively used recruiter series | Neosubstrate liabilities depend on recruiter and context |
| IAP (XIAP, cIAP1) | SMAC-mimetic-derived recruiters | Published IAP-recruiting degraders | Target scope and cellular effects require validation |
| MDM2 | nutlin-derived recruiters | Published MDM2-recruiting degraders | Target diversity and pathway effects require validation |
| KEAP1 | KEAP1-directed recruiters | CUL3-KEAP1 recruitment studies | Specialized use and limited comparative validation |
| DCAF15 | Aryl sulfonamides such as E7820 | DDB1-CUL4 / DCAF15 systems | Molecular-glue and degrader mechanisms require careful distinction |
| RNF114 | Nimbolide, EN219 | Covalent RNF114 recruitment | Limited tooling |
| RNF4 | CCW16 | Covalent RNF4 recruitment | Limited tooling |
| UBE2D (E2, not E3) | EN450 | Covalent molecular-glue mechanism involving NFKB1 | Do not classify as an E3-ligase recruiter |
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.
- 9d ago First seen · 279 lines · 126 tokens per session scan A c9fb1bb7d9cb
bio-protac-degraders is a skill published in the GitHub repository PKU-YuanGroup/OpenAI4S (407 stars, last pushed yesterday), licensed MIT. It adds 126 tokens to every session and 4,657 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to bio-protac-degraders, differing in 12 lines, and is treated as a copy.
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