tooluniverse-protein-structural-annotation-pdb

tooluniverse-protein-structural-annotation-pdb is a skill for Claude Code from mims-harvard/ToolUniverse. It costs 110 tokens per session (1,978 once invoked), scanned A, original, Apache-2.0.

A per-residue annotation workflow for a protein structure from the Protein Data Bank, a repository of experimentally determined 3D structures. It identifies residues at binding interfaces, ligand pockets, buried cores, surfaces, and optionally secondary-structure elements.

In plain words
What is it for?
Use it to annotate a selected protein chain, map binding or pocket residues, classify buried and exposed positions, and provide tracks for a deep-mutational-scanning heatmap.
Why use it?
It adds biological and physical context to individual amino-acid positions, which is difficult to infer from a structure alone. The annotations can help explain whether a mutation or model feature lies in an important region.

Skill for Claude Code

Written for Claude Code: disable-model-invocation in frontmatter.

Part of the tooluniverse plugin — 162 skills, 8 commands, 1 agent, 1 hook, 1 MCP server shipped together

Good fit Use it to annotate a selected protein chain, map binding or pocket residues, classify buried and exposed positions, and provide tracks for a deep-mutational-scanning heatmap.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/mims-harvard/tooluniverse/tooluniverse-protein-structural-annotation-pdb
About the project

ToolUniverse is a collection of tools, interfaces, and supporting components for building AI systems that perform scientific work. It is for developers creating AI scientist agents that use APIs, databases, machine-learning tools, and domain-specific utilities. The catalogue includes skills, commands, an MCP server, an agent, and a hook for working with the ecosystem.

mims-harvard/ToolUniverse · 1,744 stars · on GitHub · aiscientist.tools

Install

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.

Any agent
npx skills add mims-harvard/ToolUniverse --skill tooluniverse-protein-structural-annotation-pdb
Clone the repo
git clone --depth 1 https://github.com/mims-harvard/ToolUniverse

Made for: Claude Code.

Or install tooluniverse, the plugin that ships this one along with the rest of its 162 skills, 8 commands, 1 agent, 1 hook, 1 MCP server.

Wrote 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.

agentmods badge for tooluniverse-protein-structural-annotation-pdb

README.md
[![agentmods](https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/tooluniverse-protein-structural-annotation-pdb/github.svg)](https://agentmods.dev/skills/mims-harvard/tooluniverse/tooluniverse-protein-structural-annotation-pdb)
Your own site
<a href="https://agentmods.dev/skills/mims-harvard/tooluniverse/tooluniverse-protein-structural-annotation-pdb"><img src="https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/tooluniverse-protein-structural-annotation-pdb/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.

agentmods 80×15 button for tooluniverse-protein-structural-annotation-pdb

Your own site · 80×15
<a href="https://agentmods.dev/skills/mims-harvard/tooluniverse/tooluniverse-protein-structural-annotation-pdb"><img src="https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/tooluniverse-protein-structural-annotation-pdb.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 110 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,978 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00110 $0.01978
Opus 5.5 $0.00044 $0.00791
Sonnet 5.5 $0.00022 $0.00396
Haiku 4.5 $0.00011 $0.00198

Measured 1mo ago against content hash bae9628a653c, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

tooluniverse-protein-structural-annotation-pdb 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.

plugin/skills/tooluniverse-protein-structural-annotation-pdb/SKILL.md · 180 lines

How it starts

The opening of the file, as written. The whole thing — 180 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Protein structural annotation from a PDB

For each residue of a target protein chain, classify whether it sits at a binding interface, in a ligand pocket, is buried vs solvent-exposed, and (optionally) which secondary-structure element it belongs to. This is the annotation track that anchors any DMS heatmap or per-residue interpretation to the protein's actual physical context.


When to use this skill

  • Building a per-residue annotation track for a DMS heatmap
  • Deciding whether a variant of interest is in an interface, in a pocket, or in the core — context that often distinguishes plausible mechanisms
  • Reading SAE feature drops at a position against the protein's known biology (a feature drop at a ligand-pocket residue means something different from a drop at a surface residue)

Not for:

  • Multi-conformer ensembles or NMR structure comparisons → use tooluniverse-computational-biophysics
  • Whole-structure pocket detection without a known ligand → use a docking tool (e.g. SwissDock) or PDBe's pre-computed pockets

Required inputs

Input Format Example
PDB ID 4 characters 6VJJ (KRAS-RAF1-GTP analogue)
Target chain single character A
Partner chain(s) list of chain IDs ["B"]
Ligand resnames 3-letter PDB names ["GNP", "MG"]

Optional:

  • distance_cutoff (default 5.0 Å)
  • core_rsa_cutoff (default 0.25)
  • include_secondary_structure (default false; uses PDBe REST if true)
  • pdb_content instead of pdb_id for local / predicted structures

Workflow

Step 1 (optional): Find the relevant PDB

If you only have a UniProt accession or gene symbol, pick a structure first:

# PDBe's curated UniProt→PDB mapping (recommended; ranks by coverage + resolution)
PDBeSIFTS_get_best_structures(uniprot_accession="P01116")
# Returns a ranked list of PDB IDs for KRAS with chain mapping

# Or full list (unranked)
PDBeSIFTS_get_all_structures(uniprot_accession="P01116")

# RCSB advanced search (free-text, when you don't have a UniProt yet)
RCSBAdvSearch_search_structures(query="KRAS GTP complex")

Read the full file on GitHub · 180 lines

Changes

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.

  1. 1mo ago First seen · 180 lines · 110 tokens per session scan A bae9628a653c

Subscribe to this mod's changes

tooluniverse-protein-structural-annotation-pdb is a skill published in the GitHub repository mims-harvard/ToolUniverse (1,744 stars, last pushed yesterday), licensed Apache-2.0. It adds 110 tokens to every session and 1,978 once invoked, about $0.0004 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 2026-09-03.

Related

Other skills, from other repositories

ai-agent-tool-routing

Use when designing or reviewing an AI agent tool registry or MCP router: document parameter schemas, caller authorization, execution limits, replay-safe retries and verification evidence.

sickn33/agentic-awesome-skills · 36 tokens

nanoresearch-writing

Draft a LaTeX research paper from all previous stage outputs.

OpenRaiser/NanoResearch · 17 tokens

construct-toy-examples

Generate and analyze simpler examples that satisfy both the assumptions and the conclusion of a theorem statement or subgoal. Use when you are stuck in reasoning and need simpler examples to regain traction, or when you want to see where the assumptions take effect and gain intuition.

frenzymath/Danus · 57 tokens

obtain-immediate-conclusions

Derive immediate mathematical consequences from a theorem statement or subgoal. Use when starting a new problem, branch, or subgoal, or when cheap progress or a cleaner reformulation is needed before deeper proof search.

frenzymath/Danus · 49 tokens

clinical-research-analysis-framework

Guided workflow for statistical and ML analysis of clinical data. Use when planning or executing research analyses on MIMIC, eICU, or similar EHR data. Ensures methodological rigor through structured consultation, assumption checking, and stepwise execution with audit trails.

hannesill/m4 · 59 tokens

apache-iv-score

Calculate APACHE IV (Acute Physiology and Chronic Health Evaluation IV) score for ICU mortality prediction. Use for severity assessment, hospital mortality prediction, ICU benchmarking, or case-mix adjustment. eICU has pre-computed scores; MIMIC-IV requires custom implementation with diagnosis mapping challenges.

hannesill/m4 · 65 tokens