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-evobind-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-evobind-tool)<a href="https://agentmods.dev/skills/internscience/molclaw/molclaw-evobind-tool"><img src="https://agentmods.dev/badge/skills/internscience/molclaw/molclaw-evobind-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-evobind-tool"><img src="https://agentmods.dev/badge/skills/internscience/molclaw/molclaw-evobind-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 | $0.00055 | $0.01028 |
| Opus 5.5 | $0.00022 | $0.00411 |
| Sonnet 5.5 | $0.00011 | $0.00206 |
| Haiku 4.5 | $0.00006 | $0.00103 |
Grade A, and why
molclaw-evobind-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 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 — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.
EvoBind2 Peptide Binder Design
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. EvoBind2 Binder Design
The description of tool evobind_tool.
Design linear or cyclic peptide binders from a receptor sequence using EvoBind2 in structure-guided screening workflows.
Args:
fasta (str): Receptor FASTA file path.
peptide_length (int): Binder peptide length, default 10.
num_designs (int): Number of independent design rounds, default 10.
num_iterations (int): Monte Carlo iterations per round, default 100.
max_recycles (int): AlphaFold2 recycle count, default 1.
model_name (str): AlphaFold2 model in {model_1, model_2, model_3, model_4, model_5}, default model_1.
target_residues (str): Receptor target residues as comma-separated 1-indexed positions or all, default all.
cyclic (bool): Whether to enable cyclic peptide design, default False.
msa_file (str|None): Optional precomputed MSA .a3m file path, default None.
dry_run (bool): Whether to print planned commands without executing design rounds, default False.
skip_env_check (bool): Whether to skip source workflow environment checks, default False.
Return:
status (str): success, error, or partial_success execution status.
msg (str): Human-readable execution summary.
output_dir (str): Unique run directory under tool_result/evobind_tool_result.
fasta (str): Resolved absolute FASTA input path.
peptide_length (int): Effective peptide length used in this run.
num_designs (int): Effective number of design rounds used in this run.
num_iterations (int): Effective number of iterations used in this run.
max_recycles (int): Effective recycle count used in this run.
model_name (str): Effective model name used in this run.
target_residues (str): Effective target residue specification used in this run.
cyclic (bool): Effective cyclic flag used in this run.
dry_run (bool): Effective dry-run flag used in this run.
skip_env_check (bool): Effective environment-check skip flag used in this run.
output_files (dict): Key output file paths including run logs and summary artifacts when available.
metrics (dict): Parsed summary metrics such as candidate count and top-ranked scores when available.
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 · +25 tokens per session a0ed15dd03f2
- 1mo ago First seen · 108 lines · 30 tokens per session scan A 5a93562a57bd
molclaw-evobind-tool 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 1,028 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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