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 beita6969/ScienceClaw --skill protein-structure-predictiongit clone --depth 1 https://github.com/beita6969/ScienceClawWrote 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/beita6969/scienceclaw/protein-structure-prediction)<a href="https://agentmods.dev/skills/beita6969/scienceclaw/protein-structure-prediction"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/protein-structure-prediction/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/beita6969/scienceclaw/protein-structure-prediction"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/protein-structure-prediction.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Prompt Injection · line 6 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
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 | $0.00010 | $0.00488 |
| Opus 5 | $0.00005 | $0.00244 |
| Sonnet 5 | $0.00002 | $0.00098 |
| Haiku 4.5 | $0.00001 | $0.00049 |
Grade A, and why
protein-structure-prediction 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 9d 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.
What it actually says
name: 'protein-structure-prediction' description: 'Predicts 3D protein structures from amino acid sequences using ESMFold or AlphaFold3 (mock).' measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools:
- read_file
- run_shell_command
Protein Structure Prediction (ESMFold/AF3)
The Protein Structure Prediction Skill provides an interface to state-of-the-art folding models. It takes an amino acid sequence and returns a PDB file or structure metrics (pLDDT).
When to Use This Skill
- When you have a protein sequence and need its 3D coordinates.
- To check if a designed sequence folds into a stable structure.
- To prepare a receptor for docking simulations.
Core Capabilities
- Folding: Generates atomic coordinates (PDB format).
- Confidence Scoring: Returns pLDDT scores per residue.
- Visualization: (Optional) Generates a static view of the structure.
Workflow
- Input: Amino acid sequence (FASTA string).
- Process: Sends sequence to ESMFold API (or local inference).
- Output: Saves
.pdbfile and returns confidence metrics.
Example Usage
User: "Fold this sequence: MKTIIALSY..."
Agent Action:
python3 Skills/Drug_Discovery/Protein_Structure/esmfold_client.py \
--sequence "MKTIIALSYIFCLVFDYDY" \
--output structure.pdb
What ships with it
2 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.
- 9d ago First seen · 64 lines · 10 tokens per session scan A 554fcb99b216
protein-structure-prediction is a skill published in the GitHub repository beita6969/ScienceClaw (898 stars, last pushed 3mo ago), licensed MIT. It adds 10 tokens to every session and 488 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 2026-09-03.
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structure-prediction
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biomcp
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biomcp-research
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Expert-level biology, biotechnology, genetics, bioinformatics, and computational biology. Use when the user mentions biology, biotechnology, genetics, bioinformatics, or genomics, or when the task involves Molecular Biology, Genomics & Bioinformatics, Systems Biology, or Data Analysis.