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-karmadock-toolgit 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-karmadock-tool)<a href="https://agentmods.dev/skills/internscience/molclaw/molclaw-karmadock-tool"><img src="https://agentmods.dev/badge/skills/internscience/molclaw/molclaw-karmadock-tool/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-karmadock-tool"><img src="https://agentmods.dev/badge/skills/internscience/molclaw/molclaw-karmadock-tool.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 | $0.00026 | $0.01122 |
| Opus 5 | $0.00013 | $0.00561 |
| Sonnet 5 | $0.00005 | $0.00224 |
| Haiku 4.5 | $0.00003 | $0.00112 |
Grade A, and why
molclaw-karmadock-tool 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 8d 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 — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
KarmaDock Virtual Screening
Note:
- Local files are not directly accessible by the server. Please upload them to the server using
molclaw-file-transferbefore execution. - For PDB file inputs, it is recommended to preprocess them using
molclaw-pdbfixerbefore execution. - Please refer to skill
molclaw-scp-serverto complete tool invocation.
Usage
1. KarmaDock Virtual Screening
The description of tool karmadock_tool.
Performs protein-ligand virtual screening with KarmaDock for batch ranking and optional pose export workflows.
Args:
ligand_smi (str): Ligand SMILES input file path, required.
protein_file (str): Protein PDB file path, required.
crystal_ligand_file (str): Crystal ligand MOL2 file for pocket localization, required.
score_threshold (float): Score threshold used for pose export mask, default 70.0.
batch_size (int): Inference batch size, default 64.
random_seed (int): Random seed for reproducibility, default 2020.
dry_run (bool): Validate inputs and create tracked output directory without execution, default False.
Return:
status (str): success, partial_success, or error execution status.
msg (str): Human-readable execution summary.
output_dir (str): Run-specific directory under tool_result/karmadock_tool_result.
ligand_smi (str): Resolved ligand SMILES file absolute path.
protein_file (str): Resolved protein PDB file absolute path.
crystal_ligand_file (str): Resolved crystal ligand MOL2 absolute path.
score_threshold (float): Effective score threshold used in this run.
batch_size (int): Effective batch size used.
random_seed (int): Effective random seed used.
out_init (bool): Always True in wrapper.
out_uncorrected (bool): Always True in wrapper.
out_corrected (bool): Always True in wrapper.
dry_run (bool): Effective dry-run flag.
return_code (int | None): Delegated process return code.
pose_export_hint (str | None): Diagnostic message when SDF export is requested but missing.
key_files (dict): Key output files including score_csv and pose_sdf_files.
metrics (dict): Summary metrics including num_ligands_scored and karma score extrema.
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.
- 8d ago First seen · 112 lines · 26 tokens per session scan A 999bba002448
molclaw-karmadock-tool is a skill published in the GitHub repository InternScience/MolClaw (33 stars, last pushed 1mo ago), licensed MIT. It adds 26 tokens to every session and 1,122 once invoked, about $0.0001 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.
Other skills, from other repositories
instrument-data-to-allotrope
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full…
exploratory-data-analysis
Perform bounded, local exploratory analysis of explicitly supported scientific files. Use for redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation sensitivity; and rigorous EDA report scaffolds. Other domain…
matlab
Build, review, migrate, and safely plan MATLAB or GNU Octave numerical workflows, including arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.
phylogenetics
Build and analyze phylogenetic trees using MAFFT (multiple alignment), IQ-TREE 2 (maximum likelihood), and FastTree (fast NJ/ML). Visualize with ETE3 or FigTree. For evolutionary analysis, microbial genomics, viral phylodynamics, protein family analysis, and molecular clock studies.
research-engineer
An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.
mapping-to-snomed
Maps clinical concept spans extracted by OpenMed to SNOMED CT concepts through a USER-SUPPLIED terminology server (the user's own Ontoserver, Snowstorm, or UMLS/UTS), never a bundled vocabulary. Use when the user wants to code findings, disorders, procedures, body structures, or substances to SNOMED CT, run an ECL…