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 skills add InternScience/MolClaw --skill molclaw-pack-sidechainsgit clone --depth 1 https://github.com/InternScience/MolClawWrote 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/internscience/molclaw/molclaw-pack-sidechains)<a href="https://agentmods.dev/skills/internscience/molclaw/molclaw-pack-sidechains"><img src="https://agentmods.dev/badge/skills/internscience/molclaw/molclaw-pack-sidechains/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/internscience/molclaw/molclaw-pack-sidechains"><img src="https://agentmods.dev/badge/skills/internscience/molclaw/molclaw-pack-sidechains.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 5.1 | $0.00055 | $0.00808 |
| Opus 5.5 | $0.00022 | $0.00323 |
| Sonnet 5.5 | $0.00011 | $0.00162 |
| Haiku 4.5 | $0.00006 | $0.00081 |
Grade A, and why
molclaw-pack-sidechains 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 1mo 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 — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AttnPacker Sidechain Packing
Invocation
Use the exact tool name and parameter schema exposed by the current harness. Tool names in SDK examples below are raw MCP names, not model-facing aliases. Local files must be uploaded with molclaw-file-transfer before a server tool can use them. Repair PDB inputs first when the workflow requires it.
Usage
1. Protein Sidechain Packing
The description of tool pack_sidechains.
Predict full-atom sidechain conformations from backbone PDBs for protein structure preparation workflows.
Args:
input_pdb (str): Input PDB file path, required.
device (str|None): Compute device such as cuda:0, default None (auto by source script).
chunk_size (int): Inference chunk size for long proteins, default 500.
no_post_process (bool): Skip rotamer post-processing for faster runtime, default False.
max_optim_iters (int): Maximum optimization iterations in post-process, default 250.
steric_wt (float): Steric clash penalty weight, default 1.0.
optim_repeats (int): Post-process optimization repeats, default 2.
dry_run (bool): Create a traceable run directory without running inference, default False.
Return:
status (str): 'success', 'error', or 'partial_success'.
msg (str): Human-readable execution message.
input_pdb (str): Input PDB path used for this run.
output_dir (str): Unique run directory under tool_result/pack_sidechains_result.
output_pdb (str): Expected or generated output PDB path.
device (str|None): Device value used for execution.
chunk_size (int): Chunk size used.
no_post_process (bool): Whether post-process was skipped.
max_optim_iters (int): Max optimization iterations used.
steric_wt (float): Steric weight used.
optim_repeats (int): Optimization repeats used.
dry_run (bool): Whether dry-run mode was used.
error_type (str, optional): Exception type when status is 'error'.
traceback (str, optional): Python traceback when status is 'error'.
How to use tool pack_sidechains :
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.
- 1mo ago Changed · -1 lines · +24 tokens per session 57b98daaf2fa
- 1mo ago First seen · 92 lines · 31 tokens per session scan A e865724e3707
molclaw-pack-sidechains is a skill published in the GitHub repository InternScience/MolClaw (38 stars, last pushed 1mo ago), licensed MIT. It adds 55 tokens to every session and 808 once invoked, about $0.0002 per session on Opus 5.5. 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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