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-covalent-designgit 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-covalent-design)<a href="https://agentmods.dev/skills/pku-yuangroup/openai4s/bio-chemoinformatics-covalent-design"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-chemoinformatics-covalent-design/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-covalent-design"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-chemoinformatics-covalent-design.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 | $0.00152 | $0.04344 |
| Opus 5 | $0.00076 | $0.02172 |
| Sonnet 5 | $0.00030 | $0.00869 |
| Haiku 4.5 | $0.00015 | $0.00434 |
Grade A, and why
bio-covalent-design 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.
This is a copy
98% identical to bio-covalent-design — 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 — 284 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: RDKit 2024.09+, OpenEye / AutoDock Vina 1.2+ (for covalent extensions), GOLD (commercial), DOCKovalent (web service), HCovDock 1.0+.
Before using code patterns, verify installed versions match. If versions differ:
- Python:
pip show rdkitthenhelp(rdkit.Chem)to check signatures - CLI: check version output of each docking tool
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
Covalent Inhibitor Design
Design molecules that form covalent bonds with target protein residues. Clinically validated targeted covalent inhibitors include KRAS G12C inhibitors (sotorasib, adagrasib), BTK inhibitors (ibrutinib), and EGFR inhibitors (osimertinib). Covalent design requires balancing intrinsic reactivity (must form bond) vs selectivity (only the intended residue), reversibility (irreversible vs reversible covalent), and drug-likeness (warheads can hurt PK).
For warhead substructure filtering (in non-covalent contexts), see chemoinformatics/substructure-search. For non-covalent docking, see chemoinformatics/virtual-screening. For pose validation, see chemoinformatics/pose-validation.
Reactive Residue Taxonomy
| Residue | Nucleophile | Example compatible warheads | Design note |
|---|---|---|---|
| Cysteine | Thiol / thiolate | Acrylamide, haloacetamide, nitrile | Commonly targeted; local pKa and geometry control reactivity |
| Lysine | Amine | Sulfonyl fluoride, aldehyde | Aldehydes can form reversible imines with amines |
| Serine | Alcohol / alkoxide | β-lactam, boronate | Often requires catalytic activation |
| Threonine | Alcohol / alkoxide | Boronate | Context-dependent and less commonly targeted |
| Tyrosine | Phenol / phenolate | Sulfonyl fluoride, fluorosulfate | Local environment strongly affects reaction |
| Aspartate/Glutamate | Carboxylate | Residue-specific electrophiles require experimental validation | Do not infer aldehyde Schiff-base formation with carboxylates |
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 · 284 lines · 152 tokens per session scan A 5265d1ef1ede
bio-covalent-design is a skill published in the GitHub repository PKU-YuanGroup/OpenAI4S (407 stars, last pushed yesterday), licensed MIT. It adds 152 tokens to every session and 4,344 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 0 findings. It is 98% identical to bio-covalent-design, differing in 12 lines, and is treated as a copy.
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