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/learningmatter-mit/atomisticskills/drug-pocket-detectionnpx skills add learningmatter-mit/AtomisticSkills --skill drug-pocket-detectiongit clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkillsWrote 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/learningmatter-mit/atomisticskills/drug-pocket-detection)<a href="https://agentmods.dev/skills/learningmatter-mit/atomisticskills/drug-pocket-detection"><img src="https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/drug-pocket-detection.svg" alt="Measured on agentmods" 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 | $0.00111 | $0.04057 |
| Opus 5 | $0.00056 | $0.02028 |
| Sonnet 5 | $0.00022 | $0.00811 |
| Haiku 4.5 | $0.00011 | $0.00406 |
Grade A, and why
drug-pocket-detection 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 5d 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 — 241 lines — stays where its author put it; the contents beside it link to each section on GitHub.
drug-pocket-detection
Goal
Take a protein structure (experimental or predicted) and produce a ranked list of candidate ligandable pockets, each described by:
- A unique pocket id and rank
- A geometric center (x, y, z in Angstroms)
- An estimated volume (A^3)
- A druggability score (fpocket: logistic-regression model from Schmidtke & Barril 2010, layered on top of fpocket's own PLS-derived pocket score from Le Guilloux et al. 2009; P2Rank: a calibrated per-pocket ligandability probability)
- The lining residues (chain, resnum, resname, one-letter)
- Backend-specific raw metrics (hydrophobicity, polarity, alpha-sphere counts, etc.) preserved for provenance
This skill does not perform docking. Once you have selected a pocket, feed its center into drug-binding-site-definition to produce a docking box, then run drug-docking-vina.
Choosing a Backend
| Backend | When to use | Strengths | Weaknesses |
|---|---|---|---|
| fpocket (default) | First pass on any structure; lightweight (no Java, no large model file) | Fast; well-cited logistic-regression druggability score (Schmidtke & Barril 2010) layered on the underlying PLS pocket score (Le Guilloux et al. 2009); deterministic given the same parameters but slightly sensitive to floating-point details across builds | Pure geometry; misses cryptic pockets that lack a clear cavity in the input conformation |
| P2Rank | Independent ML pocket prediction, especially when geometry alone is ambiguous (shallow / surface pockets) or for predicted structures via the alphafold profile |
Often higher Top-1 accuracy on benchmarks (Krivak & Hoksza 2018); residue-aware ML; reports adjacent residues directly | Heavier install (separate Java runtime + downloaded model); does not report pocket volume |
Run both if a decision is load-bearing (e.g., you only get one shot at MD). Compare the top-3 of each; consensus picks are stronger.
What ships with it
8 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.
- examples/hiv1-protease/1HSG_protein.pdb 117 KB
- examples/hiv1-protease/binding_site_from_pocket.json 401 B
- examples/hiv1-protease/pockets_fpocket.json 28 KB
- examples/hiv1-protease/pockets_visualization.png 297 KB
- examples/hiv1-protease/README.md 5.3 KB
- scripts/detect_pockets.py 23 KB runs code
- scripts/pocket_to_box.py 4.6 KB runs code
- scripts/visualize_pockets.py 4.2 KB runs code
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.
- 5d ago First seen · 241 lines · 111 tokens per session scan A cd5906ac1d15
drug-pocket-detection is a skill published in the GitHub repository learningmatter-mit/AtomisticSkills (160 stars, last pushed 2d ago), licensed MIT. It adds 111 tokens to every session and 4,057 once invoked, about $0.0006 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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