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-trajectory-analysisnpx skills add learningmatter-mit/AtomisticSkills --skill drug-trajectory-analysisgit 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-trajectory-analysis)<a href="https://agentmods.dev/skills/learningmatter-mit/atomisticskills/drug-trajectory-analysis"><img src="https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/drug-trajectory-analysis.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 | $0.00039 | $0.01297 |
| Opus 5 | $0.00019 | $0.00648 |
| Sonnet 5 | $0.00008 | $0.00259 |
| Haiku 4.5 | $0.00004 | $0.00130 |
Grade A, and why
drug-trajectory-analysis 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 4d 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 — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.
drug-trajectory-analysis
Goal
To extract quantitative binding-mode descriptors from a protein-ligand MD trajectory, producing:
- Ligand heavy-atom RMSD (pose stability)
- Ligand center-of-mass drift
- Binding-pocket residue RMSF (pocket flexibility)
- Hydrogen bond persistence
- Key contact occupancy
- Protein-ligand interaction fingerprints (IFPs) over time
These outputs feed directly into go/no-go decisions about pose validity and can be used to compare refinement trajectories across compounds.
Instructions
1. Prepare inputs
Required:
- Trajectory: DCD file from drug-protein-ligand-md
- Topology: the solvated complex PDB used as the MD input
2. Run trajectory analysis
# Env: drugmd-agent
python .agents/skills/drug-trajectory-analysis/scripts/analyze_trajectory.py \
--topology md/system/complex_solvated.pdb \
--trajectory md/run/production.dcd \
--ligand_resname UNL \
--pocket_cutoff 5.0 \
--output_dir md/analysis/
Key parameters:
--ligand_resname: residue name of the ligand in the topology (default:UNL). Check the solvated PDB if unsure.--pocket_cutoff: distance cutoff in Angstroms for defining pocket residues around the ligand in the first frame (default: 5.0).--skip_frames: skip the first N frames as equilibration (default: 0).--snapshots: render PyMOL binding pocket snapshots at 4 timepoints (requirespymol-open-source).
3. Output files
The script produces:
md/analysis/ligand_rmsd.csv: per-frame ligand heavy-atom RMSD (Angstroms)md/analysis/ligand_com.csv: per-frame ligand COM relative to protein backbone COMmd/analysis/pocket_rmsf.csv: per-residue RMSF of pocket residues (Angstroms)md/analysis/hbonds.csv: hydrogen bond donor-acceptor pairs and occupancy fractionsmd/analysis/contacts.csv: residue-level contact occupancy fractionsmd/analysis/interaction_fingerprints.csv: per-frame binary IFP matrix (requires ProLIF)md/analysis/analysis_summary.json: summary statisticsmd/analysis/plots/: directory with PNG plots (RMSD time series, COM drift, RMSF bar chart, contact occupancy, PyMOL binding pocket snapshots)
What ships with it
14 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/analysis/analysis_summary.json 322 B
- examples/hiv1-protease/analysis/contacts.csv 194 B
- examples/hiv1-protease/analysis/hbonds.csv 1.8 KB
- examples/hiv1-protease/analysis/interaction_fingerprints.csv 307 B
- examples/hiv1-protease/analysis/ligand_com.csv 618 B
- examples/hiv1-protease/analysis/ligand_rmsd.csv 164 B
- examples/hiv1-protease/analysis/plots/binding_snapshots.png 411 KB
- examples/hiv1-protease/analysis/plots/contacts.png 37 KB
- examples/hiv1-protease/analysis/plots/ligand_com.png 49 KB
- examples/hiv1-protease/analysis/plots/ligand_rmsd.png 42 KB
- examples/hiv1-protease/analysis/plots/pocket_rmsf.png 26 KB
- examples/hiv1-protease/analysis/pocket_rmsf.csv 203 B
- examples/hiv1-protease/README.md 2.2 KB
- scripts/analyze_trajectory.py 23 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.
- 4d ago First seen · 113 lines · 39 tokens per session scan A c507f38921d4
drug-trajectory-analysis is a skill published in the GitHub repository learningmatter-mit/AtomisticSkills (158 stars, last pushed yesterday), licensed MIT. It adds 39 tokens to every session and 1,297 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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