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-protein-prepnpx skills add learningmatter-mit/AtomisticSkills --skill drug-protein-prepgit 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-protein-prep)<a href="https://agentmods.dev/skills/learningmatter-mit/atomisticskills/drug-protein-prep"><img src="https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/drug-protein-prep.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 | $0.00041 | $0.01003 |
| Opus 5 | $0.00020 | $0.00502 |
| Sonnet 5 | $0.00008 | $0.00201 |
| Haiku 4.5 | $0.00004 | $0.00100 |
Grade A, and why
drug-protein-prep 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 — 129 lines — stays where its author put it; the contents beside it link to each section on GitHub.
protein-prep
Goal
To prepare protein (and optionally nucleic acid) receptor structures for molecular docking (e.g., AutoDock Vina) by:
- retrieving coordinates from RCSB PDB (optional),
- fixing common structural issues (missing atoms, nonstandard residues),
- adding hydrogens at a target pH.
Note: This skill handles structure cleanup and protonation. To convert the result to PDBQT for docking, use the
mcp_drugdisc_convert_to_pdbqttool.
Instructions
1. Prepare a receptor to PDB (Cleanup + Hydrogens)
This script manages missing atoms, nonstandard residues, and protonation.
# Env: drugdisc-agent
python .agents/skills/drug-protein-prep/scripts/prepare_protein.py \
--pdb_id 1iep \
--chains A \
--ph 7.0 \
--heterogens none \
--missing_residues ignore \
--output_dir protein_prep/
2. Convert to PDBQT (for AutoDock Vina)
Use the MCP tool to convert the prepared PDB to PDBQT format.
mcp_drugdisc_convert_to_pdbqt(
input_data="protein_prep/1IEP_prepared.pdb",
output_path="protein_prep/1IEP.pdbqt",
input_type="pdb"
)
3. Keep cofactors/metal ions
# Env: drugdisc-agent
python .agents/skills/drug-protein-prep/scripts/prepare_protein.py \
--pdb_id 1iep \
--chains A \
--heterogens non-water \
--delete_resname SO4 GOL \
--output_dir protein_prep_keep_cofactors/
4. Use a biological assembly (recommended when oligomerization matters)
# Env: drugdisc-agent
python .agents/skills/drug-protein-prep/scripts/prepare_protein.py \
--pdb_id 1iep \
--assembly 1 \
--chains A \
--output_dir protein_prep_assembly1/
5. Prepare from a local structure file
# Env: drugdisc-agent
python .agents/skills/drug-protein-prep/scripts/prepare_protein.py \
--pdb_file receptor.pdb \
--heterogens none \
--output_dir protein_prep_local/
6. Validate the output (strongly recommended)
After preparation:
- Inspect the JSON summary for missing residues, nonstandard residue replacements, and atoms added.
- Visually inspect the binding site and check for:
- correct oligomeric state,
- retained/removed cofactors and metal ions,
- sensible protonation (especially histidines),
- alternate locations resolved appropriately.
What ships with it
5 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.
- 5d ago First seen · 129 lines · 41 tokens per session scan A 9e7186f2803c
drug-protein-prep is a skill published in the GitHub repository learningmatter-mit/AtomisticSkills (160 stars, last pushed yesterday), licensed MIT. It adds 41 tokens to every session and 1,003 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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