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-redocking-rmsdnpx skills add learningmatter-mit/AtomisticSkills --skill drug-redocking-rmsdgit 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-redocking-rmsd)<a href="https://agentmods.dev/skills/learningmatter-mit/atomisticskills/drug-redocking-rmsd"><img src="https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/drug-redocking-rmsd.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 | $0.00031 | $0.02328 |
| Opus 5 | $0.00015 | $0.01164 |
| Sonnet 5 | $0.00006 | $0.00466 |
| Haiku 4.5 | $0.00003 | $0.00233 |
Grade A, and why
drug-redocking-rmsd 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 — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
drug-redocking-rmsd
Goal
To quantitatively validate a docking protocol by computing the symmetry-corrected in-place heavy-atom RMSD between docked poses and the crystallographic reference ligand. A top-scored pose (pose 1) RMSD below 2.0 A is the standard threshold for a successful self-docking control.
Self-docking is a necessary, not sufficient, check. It verifies that your receptor preparation, box definition, and scoring function can recover a known pose in its own binding site. It does not verify that the protocol will work on new compounds. For a production virtual screen, complement self-docking with cross-docking into different receptor conformations when available (see the HTVS workflow Stage 3), and pair this RMSD check with drug-pose-validation to catch poses that are geometrically near-native but physically implausible (internal clashes, strained torsions).
Instructions
1. Compute RMSD from a crystal PDB reference
When the reference ligand is extracted from a PDB (HETATM records, no bond orders), provide the SMILES so the script can assign bond orders via template matching:
# Env: drugdisc-agent
python .agents/skills/drug-redocking-rmsd/scripts/compute_rmsd.py \
--docked docking/ligand_docked.pdbqt \
--reference crystal_ligand.pdb \
--smiles "NS(=O)(=O)c1ccc(Nc2nc3[nH]cnc3c(OCC3CCCCC3)n2)cc1" \
--output_dir validation/
2. Compute RMSD from an SDF reference
When the reference ligand is an SDF with proper bond orders (e.g., from a database or ligand-prep), no SMILES is needed:
# Env: drugdisc-agent
python .agents/skills/drug-redocking-rmsd/scripts/compute_rmsd.py \
--docked docking/ligand_docked.pdbqt \
--reference crystal_ligand.sdf \
--output_dir validation/
3. Tune the pass/fail threshold
The default threshold is 2.0 A, which is the classical success criterion from the original docking validation literature. Modern docking programs often do substantially better, and the threshold should scale with ligand size and flexibility:
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 · 120 lines · 31 tokens per session scan A 5d982e124099
drug-redocking-rmsd is a skill published in the GitHub repository learningmatter-mit/AtomisticSkills (160 stars, last pushed 2d ago), licensed MIT. It adds 31 tokens to every session and 2,328 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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