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Bioproject Search

Community
ssinder
bioproject-search

Retrieves NCBI BioProject metadata — title, description, organism, submitter and release dates — for a batch of BioProject accessions, using the official NCBI E-utilities API rather than scraping the BioProject web pages. Supports keyword filtering over titles and descriptions, CSV/JSON export, and requires a contact email as the NCBI usage policy mandates. This skill should be used when looking up what a BioProject or PRJNA/PRD accession contains, retrieving sequencing project descriptions, or collecting BioProject metadata in bulk is requested.

Overview

Publisherssinder
Repositorybioinformatics-agent-skills
Skill namebioproject-search
Stars
75
Forks
2
Bundled files
5
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by ssinder on GitHub. Read the source before you install it.

Installation

Install the Bioproject Search AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/ssinder/bioinformatics-agent-skills/tree/main/skills/bioproject-search
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/ssinder/bioinformatics-agent-skills.git /tmp/bioinformatics-agent-skills
mkdir -p .claude/skills
cp -r /tmp/bioinformatics-agent-skills/skills/bioproject-search .claude/skills/bioproject-search
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Bioproject Search in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Bioproject Search on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Bioproject Search is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

BioProject Metadata Retrieval

Overview

This skill retrieves NCBI BioProject records through the official E-utilities API and exports them as CSV/JSON.

The alternative — scraping the BioProject web pages — is fragile in a way that is easy to underestimate. Page-scraping code ends up with selectors like div:nth-child(5) that encode the current number of wrapper elements in the page. When NCBI adjusts its layout, the selector silently stops matching and the description column comes back empty, which is indistinguishable from "this project has no description".

E-utilities returns structured XML with stable field names. The same query that works today keeps working after a website redesign.

The skill also follows NCBI's usage policy properly: a contact email is required (via --email or the BIOPIPE_EMAIL environment variable). NCBI uses the email to identify callers and to contact them when problems occur; fabricating placeholder addresses defeats that purpose and can get your IP rate-limited.

When to Use This Skill

  • Finding out what a specific BioProject / PRJNA / PRJEB accession contains
  • Collecting titles, descriptions and organisms for a list of projects
  • Screening projects by keyword before deciding which ones to download
  • Building a project catalogue for a methods or dataset section
  • Looking up submitter and release dates for citation purposes

Quick Start

bash
export PYTHONPATH="$PWD/packages"
export BIOPIPE_EMAIL="you@your-institution.edu"

# 从文件读取 accession
python scripts/search_bioprojects.py --ids prj_ids.txt --output-dir results/

# 直接给 accession
python scripts/search_bioprojects.py --ids PRJNA172563 PRJNA212358 --output-dir results/

# 只保留描述里提到 CRISPR 的项目
python scripts/search_bioprojects.py --ids prj_ids.txt --output-dir results/ --filter CRISPR

# 有 NCBI API key 时可提高限速(10 req/s)
python scripts/search_bioprojects.py --ids prj_ids.txt --output-dir results/ --api-key $NCBI_API_KEY

Workflow

  1. Collect accessions — --ids accepts accession strings or paths to text files (one per line, # for comments). Entries that do not look like BioProject accessions are reported and skipped rather than silently dropped.
  2. Resolve to UIDs — esearch translates accessions into NCBI's internal UIDs. The query uses the [Project Accession] field qualifier, without which an accession string can match unrelated records.
  3. Fetch details from two endpoints — esummary and efetch are called together and their results merged. This is not redundancy: no single endpoint carries every field. esummary is the only source for the structured organism name, taxonomy ID, project type and submitter organisation; efetch is the only source for the release date and the full description text. Calling only one of them silently drops about half the metadata without raising an error.
  4. Parse — the merged records carry accession, title, name, description, organism, taxonomy ID, project type, submitter, registration date, release date and last-update date. All three NCBI date formats are normalised to YYYY-MM-DD.
  5. Filter (optional) — --filter keeps records whose title, name or description contains the keyword (case-insensitive). --drop-noisy removes descriptions that look like page navigation rather than content.
  6. Export — bioproject_data.csv and bioproject_data.json are written to the output directory.

Parameters

ParameterDefaultDescription
--idsrequiredAccession list or path(s) to text files containing accessions
--output-dirbioproject_resultsOutput directory for the CSV/JSON results
--email$BIOPIPE_EMAILContact email (required by NCBI's usage policy)
--api-key$BIOPIPE_API_KEYNCBI API key; raises the rate limit from 3 to 10 req/s
--filternoneKeep only records whose title/name/description contains this keyword
--drop-noisyoffDrop descriptions that look like page navigation
--timeout60Per-request timeout in seconds

Outputs

FileContent
bioproject_data.csvOne row per project: accession, title, name, description, organism, taxid, study type, submitter, registration date, release date, last-update date, UID
bioproject_data.jsonSame content as nested JSON

Validation

  • A description should be a paragraph, not a sentence fragment. Empty or one-word descriptions usually mean the project record genuinely lacks one — some submitters provide only a title. Cross-check with the linked BioSample or SRA records.
  • The accession prefix indicates the archive — PRJNA (NCBI), PRJEB (EBI/ENA), PRJDB (DDBJ). A project submitted to ENA may be reachable through ENA's own portal rather than NCBI's.
  • Organism names come from the submitter and are not normalised. A project may list several organisms, in which case the field contains a comma-separated list rather than a single taxon — do not treat it as a single taxid. The organism field always holds a name; the numeric identifier belongs in taxid.
  • An empty date means "not recorded", not "zero". NCBI uses the placeholder 1/01/01 00:00 for "date unknown"; it is deliberately not converted into a real date, so the field stays empty. Do not interpret an empty registration_date as a bug — check whether the project is dbGaP-backed (those records have their submitter details stripped).
  • Release date may be in the future or absent for embargoed projects. An embargoed project returns its metadata to authorised submitters only; the public record will be sparse.
  • Search term semantics — [Project Accession] matches the accession field exactly. To search descriptions by keyword across all projects rather than a known list, use NCBI Entrez directly with bioproject[Filter] and a keyword term.
  • Search term semantics — [Project Accession] matches the accession field exactly. To search descriptions by keyword across all projects rather than a known list, use NCBI Entrez directly with bioproject[Filter] and a keyword term.

References

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Bioproject Search AI skill do?

Retrieves NCBI BioProject metadata — title, description, organism, submitter and release dates — for a batch of BioProject accessions, using the official NCBI E-utilities API rather than scraping the BioProject web pages. Supports keyword filtering over titles and descriptions, CSV/JSON export, and requires a contact email as the NCBI usage policy mandates. This skill should be used when looking up what a BioProject or PRJNA/PRD accession contains, retrieving sequencing project descriptions, or collecting BioProject metadata in bulk is requested.

Why use Bioproject Search on TypingMind?

Because you install it once and use it with any model. Bioproject Search is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Bioproject Search in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ssinder/bioinformatics-agent-skills/tree/main/skills/bioproject-search. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Bioproject Search?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Bioproject Search?

As many as you like. As long as a model supports skills, you can use Bioproject Search with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Bioproject Search AI skill free?

Yes. It is published on GitHub by ssinder under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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