Agent skill

Protein Blast Search

by InternScience in InternScience/scp

Search for similar protein sequences in UniProt Swiss-Prot database using BLAST to identify homologous proteins and functional relationships.

MITAuto-check passedResearch & Science

Install Protein Blast Search

skills CLI
$ npx skills add InternScience/scp --skill protein-blast-search -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install InternScience/scp protein-blast-search --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/InternScience/scp.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/protein-blast-search .claude/skills/protein-blast-search && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
protein-blast-search
GitHub stars
170
Used in
1 other repo
Token cost
~1.7k tokens
SKILL.md length
324 words
Files
1
Skills in repo
73
Repo updated
First seen
Licence
MIT

At a glance

Search for similar protein sequences in UniProt Swiss-Prot database using BLAST to identify homologous proteins and functional relationships.

  • Works in 2 steps: MCP Server Definition → Protein BLAST Search Workflow
  • Tasks that involve Bioinformatics
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Protein Blast Search is an agent skill from InternScience/scp. Search for similar protein sequences in UniProt Swiss-Prot database using BLAST to identify homologous proteins and functional relationships.

Its SKILL.md is about 1.7k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Research & Science, covering Bioinformatics. It works with UniProt. The licence is MIT.

When your agent uses it

  • Tasks that involve Bioinformatics

Example prompts

  • “/protein-blast-search”

Requirements

  • Python 3

Workflow steps

2 steps, taken from the step headings in SKILL.md.

  1. MCP Server Definition
  2. Protein BLAST Search Workflow

What it can do on your machine

Read from SKILL.md and the folder at commit cea5398. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are python).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Protein Blast Search loads about 1.7k tokens when it runs. Until then it costs about 41 tokens; SKILL.md has 324 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~41
When it runs · the whole SKILL.md, loaded when a task matches
~1.7k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from InternScience/scp at commit cea5398, republished under its MIT licence (© InternScience). 324 words, ~1,724 tokens.

Download SKILL.mdSave it as .claude/skills/protein-blast-search/SKILL.md (or your agent's skills folder).
name
protein-blast-search
description
Search for similar protein sequences in UniProt Swiss-Prot database using BLAST to identify homologous proteins and functional relationships.
license
MIT license
metadata.skill-author
PJLab

Usage

1. MCP Server Definition
python
import asyncio
import json
from mcp.client.streamable_http import streamablehttp_client
from mcp import ClientSession

class BioInfoToolsClient:
    """BioInfo-Tools MCP Client"""

    def __init__(self, server_url: str, api_key: str):
        self.server_url = server_url
        self.api_key = api_key
        self.session = None

    async def connect(self):
        """Establish connection and initialize session"""
        print(f"server url: {self.server_url}")
        try:
            self.transport = streamablehttp_client(
                url=self.server_url,
                headers={"SCP-HUB-API-KEY": self.api_key}
            )
            self.read, self.write, self.get_session_id = await self.transport.__aenter__()

            self.session_ctx = ClientSession(self.read, self.write)
            self.session = await self.session_ctx.__aenter__()

            await self.session.initialize()
            session_id = self.get_session_id()

            print(f"✓ connect success")
            return True

        except Exception as e:
            print(f"✗ connect failure: {e}")
            import traceback
            traceback.print_exc()
            return False

    async def disconnect(self):
        """Disconnect from server"""
        try:
            if self.session:
                await self.session_ctx.__aexit__(None, None, None)
            if hasattr(self, 'transport'):
                await self.transport.__aexit__(None, None, None)
            print("✓ already disconnect")
        except Exception as e:
            print(f"✗ disconnect error: {e}")

    def parse_result(self, result):
        """Parse MCP tool call result"""
        try:
            if hasattr(result, 'content') and result.content:
                content = result.content[0]
                if hasattr(content, 'text'):
                    return json.loads(content.text)
            return str(result)
        except Exception as e:
            return {"error": f"parse error: {e}", "raw": str(result)}
2. Protein BLAST Search Workflow

This workflow searches for similar protein sequences in the UniProt Swiss-Prot database using BLAST, identifying homologous proteins and their functional relationships.

Workflow Steps:

  1. Validate Input - Ensure protein sequence is in valid amino acid format
  2. Execute BLAST Search - Query UniProt Swiss-Prot database for similar sequences
  3. Parse Results - Extract matching proteins with identity, E-value, and organism information

Implementation:

python
from datetime import timedelta

## Initialize client
client = BioInfoToolsClient(
    "https://scp.intern-ai.org.cn/api/v1/mcp/17/BioInfo-Tools",
    "<your-api-key>"
)

if not await client.connect():
    print("connection failed")
    exit()

## Input: Protein sequence to search
protein_sequence = """
MVHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPKVKAHGKKVLGAFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFGKEFTPPVQAAYQKVVAGVANALAHKYH
"""

## Step 1 & 2: Execute BLAST search against UniProt Swiss-Prot
result = await client.session.call_tool(
    "blast_search",
    arguments={
        "sequence": protein_sequence.strip(),
        "sequence_id": "HBB_HUMAN",  # Optional identifier
        "evalue": 0.01,              # E-value threshold (default: 0.01)
        "max_hits": 50               # Maximum number of hits to return
    },
    read_timeout_seconds=timedelta(seconds=300)  # Allow up to 5 minutes
)

## Step 3: Parse and display results
result_data = client.parse_result(result)

if result_data.get("success"):
    print(f"✅ BLAST search completed successfully")
    print(f"Execution time: {result_data.get('time_seconds', '?')} seconds")
    print(f"Total hits found: {result_data.get('total_hits', 0)}\n")

    hits = result_data.get("hits", [])

    # Display top matches
    for i, hit in enumerate(hits[:10], 1):
        print(f"{i}. {hit['uniprot_id']} - {hit.get('organism', 'N/A')}")
        print(f"   Description: {hit['description']}")
        print(f"   Identity: {hit['identity_percent']:.1f}%")
        print(f"   E-value: {hit['evalue']:.2e}")
        print(f"   Alignment length: {hit['alignment_length']} aa\n")
else:
    print(f"❌ BLAST search failed: {result_data.get('error', 'Unknown error')}")

await client.disconnect()
Tool Descriptions

BioInfo-Tools Server:

  • blast_search: Search for similar protein sequences in UniProt Swiss-Prot database
    • Args:
      • sequence (str): Protein sequence in amino acid single-letter code
      • sequence_id (str, optional): Identifier for the query sequence
      • evalue (float, optional): E-value threshold (default: 0.01)
      • max_hits (int, optional): Maximum number of hits to return (default: 50)
    • Returns:
      • success (bool): Whether search completed successfully
      • total_hits (int): Number of matching sequences found
      • hits (list): List of matching proteins with details
      • time_seconds (float): Execution time
Input/Output

Input:

  • sequence: Protein sequence (amino acid single-letter code)
  • sequence_id: Optional identifier for the query
  • evalue: E-value threshold (lower = more stringent, default: 0.01)
  • max_hits: Maximum number of results to return (default: 50)

Output:

  • List of similar proteins, each containing:
    • uniprot_id: UniProt accession number
    • description: Protein description and name
    • organism: Species/organism name
    • identity_percent: Sequence identity percentage (0-100)
    • evalue: E-value (statistical significance, lower is better)
    • alignment_length: Length of sequence alignment
    • query_coverage: Percentage of query sequence covered
E-value Interpretation
  • E-value < 1e-10: Highly significant match, very likely homologous
  • E-value < 1e-5: Significant match, likely homologous
  • E-value < 0.01: Potentially homologous (default threshold)
  • E-value > 0.01: May be spurious matches
Use Cases
  • Identify protein function by homology
  • Find evolutionarily related proteins
  • Discover orthologs and paralogs across species
  • Annotate unknown protein sequences
  • Study protein evolution and phylogeny
Performance Notes
  • Typical execution time: 10-90 seconds depending on sequence length and max_hits
  • Shorter sequences (<50 aa): May return more non-specific matches
  • Longer sequences (>500 aa): May take longer but provide more specific matches
  • Timeout recommendation: Set to at least 300 seconds (5 minutes) for reliability

© InternScience, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/protein-blast-search of InternScience/scp.

Open the folder on GitHubat commit cea5398

Used in 2 other repositories

We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in InternScience/scp, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Protein Blast Search next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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External API ChangeGuyTeichman/RNAlysis139—~1.8kAutomated safety check: PassMIT
UniProt Database Accessdavila7/claude-code-templates33k14 repos~1.7kAutomated safety check: PassMIT
Bioservicesdavila7/claude-code-templates33k10 repos~2.5kAutomated safety check: PassMIT
Ggetdavila7/claude-code-templates33k10 repos~6.3kAutomated safety check: PassMIT

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Works with

Questions about Protein Blast Search

What does Protein Blast Search do?

Search for similar protein sequences in UniProt Swiss-Prot database using BLAST to identify homologous proteins and functional relationships. Protein Blast Search is an agent skill from InternScience/scp. Search for similar protein sequences in UniProt Swiss-Prot database using BLAST to identify homologous proteins and functional relationships.

When should I use Protein Blast Search?

Protein Blast Search fits situations like: tasks that involve Bioinformatics.

How do I install Protein Blast Search in Claude Code?

Run `npx skills add InternScience/scp --skill protein-blast-search -a claude-code`. Or copy the skill folder (skills/protein-blast-search in InternScience/scp) into .claude/skills/protein-blast-search in your project. Claude Code loads it when a task matches its description.

How do I install Protein Blast Search in Codex?

Run `npx skills add InternScience/scp --skill protein-blast-search -a codex`. Or copy the skill folder (skills/protein-blast-search in InternScience/scp) into .agents/skills/protein-blast-search in your project. Codex loads it when a task matches its description.

Can I use Protein Blast Search in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add InternScience/scp --skill protein-blast-search -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/protein-blast-search, .gemini/skills/protein-blast-search, .github/skills/protein-blast-search and .opencode/skills/protein-blast-search in your project.

What does Protein Blast Search need to run?

SKILL.md names no scripts, command-line tools or credentials: Protein Blast Search is instructions for the agent only. Our summary lists: Python 3.

Does Protein Blast Search access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Protein Blast Search safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Protein Blast Search use?

Protein Blast Search is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Protein Blast Search use?

About 1.7k tokens (SKILL.md is roughly 6.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Protein Blast Search?

Skills that share tags, products or a category with Protein Blast Search: Biomarker Database Analysis (aws-samples/amazon-bedrock-agents-healthcare-lifesciences, 274 stars), External API Change (GuyTeichman/RNAlysis, 139 stars), UniProt Database Access (davila7/claude-code-templates, 33k stars) and Bioservices (davila7/claude-code-templates, 33k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Protein Blast Search?

InternScience (a GitHub organization) maintains it in InternScience/scp, which has 170 GitHub stars. The repository holds 73 skills in this directory. The repository was last updated on June 3, 2026.

Source: InternScience/scp on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.