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 agentmods add skills/synthetic-sciences/openscience/binding-affinitynpx skills add synthetic-sciences/openscience --skill binding-affinitygit clone --depth 1 https://github.com/synthetic-sciences/openscienceWhat 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 | $0.00038 | $0.02604 |
| Opus 5 | $0.00019 | $0.01302 |
| Sonnet 5 | $0.00008 | $0.00521 |
| Haiku 4.5 | $0.00004 | $0.00260 |
Grade A, and why
binding-affinity 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 2d 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 — 296 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Binding Affinity Prediction
Overview
This skill predicts protein-ligand binding affinity from docked poses — converting structural information into estimated ΔG (kcal/mol), pKd, and Kd (nM). It complements the molecular-docking skill's interaction analysis (score.py) which counts contacts but does NOT predict binding strength in energy units.
Key capabilities:
- Empirical scoring: descriptor + contact-based affinity prediction using RDKit and BioPython
- MM/GBSA rescoring: physics-based energy decomposition with OpenMM (or RDKit fallback)
- Consensus scoring: combine multiple scoring methods with rank-based normalization
- Batch virtual screening: efficiently score large compound libraries
Validation Warning
All predictions from this skill are computational estimates, NOT experimentally validated measurements.
- Empirical scoring (predict.py): typical error is 1-2 log units pKd (~10-100x in Kd)
- MM/GBSA rescoring: useful for relative ranking, not absolute binding energies
- Use for prioritizing compounds for experimental testing, not for making clinical claims
The scripts include uncertainty ranges and confidence flags to help calibrate expectations.
Output Integrity
Script outputs are RAW computational estimates. The agent MUST NOT:
- Apply "calibration" or scaling to raw pKd/Kd values
- Adjust values to match known experimental data
- Add fields like "calibrated_pKd" not produced by the script
- Present approximate methods (simplified MM/GBSA) as full implementations
The raw output IS the prediction. Report it exactly as produced.
All script invocations are automatically logged to _script_manifest.jsonl. The critique
agent uses this manifest to verify that every number in the final report traces to a real
script output.
When to Use This Skill
Use the binding-affinity skill when you need to:
- Predict how tightly a ligand binds to a protein (after docking)
- Rank docked poses by estimated binding affinity
- Rescore poses using physics-based MM/GBSA energy decomposition
- Screen compound libraries for binding potential
- Combine multiple scoring methods into a consensus ranking
What ships with it
6 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.
- 2d ago First seen · 296 lines · 38 tokens per session scan A 1567d5359e00
binding-affinity is a skill published in the GitHub repository synthetic-sciences/openscience (3,362 stars, last pushed 2d ago), licensed Apache-2.0. It adds 38 tokens to every session and 2,604 once invoked, about $0.0002 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.
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