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Agent skill

Biopython Sequence Io

by aipoch in aipoch/medical-research-skills

Use Biopython to read/write/convert biological sequence files (FASTA/GenBank/FASTQ, etc.) and perform basic sequence operations; use when you need reliable sequence I/O, lightweight sequence…

MITAuto-check passedResearch & Science

Install Biopython Sequence Io

skills CLI
$ npx skills add aipoch/medical-research-skills --skill biopython-sequence-io -a claude-code

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

GitHub CLI
$ gh skill install aipoch/medical-research-skills biopython-sequence-io --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/aipoch/medical-research-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/'scientific-skills/Data Analysis/biopython-sequence-io' .claude/skills/biopython-sequence-io && 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
biopython-sequence-io
GitHub stars
2k
Token cost
~2.1k tokens
SKILL.md length
640 words
Files
4 (incl. references)
Skills in repo
567
Repo updated
First seen
Licence
MIT

At a glance

Use Biopython to read/write/convert biological sequence files (FASTA/GenBank/FASTQ, etc.) and perform basic sequence operations; use when you need reliable sequence I/O, lightweight sequence…

  • Works in 4 steps: Validate the request against the skill… → Select the documented execution path and… → Produce the expected output using the… → …
  • You need reliable sequence I/O
  • SKILL.md covers When to Use, Key Features, Dependencies and Example Usage, plus 8 more sections
  • Calls python

What it does

Biopython Sequence Io is an agent skill from aipoch/medical-research-skills. Use Biopython to read/write/convert biological sequence files (FASTA/GenBank/FASTQ, etc.) and perform basic sequence operations; use when you need reliable sequence I/O, lightweight sequence manipulation, or scalable processing of large sequence datasets.

Its SKILL.md is about 2.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including reference files (for example `biopython-sequence-io_audit_result_v2.json`, `config/task_config.json` and `references/sequence_io.md`).

It sits in Research & Science, covering Bioinformatics. It works with Biopython and NCBI. The repository describes itself as: Hundreds of agent skills for medical research, including protocol design, data analysis, evidence insights, and academic writing. The licence is MIT.

When your agent uses it

  • You need reliable sequence I/O
  • Lightweight sequence manipulation
  • Scalable processing of large sequence datasets

Example prompts

  • “/biopython-sequence-io”

Requirements

  • Python 3

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  1. Validate the request against the skill boundary and confirm all required inputs are present.
  2. Select the documented execution path and prefer the simplest supported command or procedure.
  3. Produce the expected output using the documented file format, schema, or narrative structure.
  4. Run a final validation pass for completeness, consistency, and safety before returning the result.

What it can do on your machine

Read from SKILL.md and the folder at commit 686e09d. 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

    Shell commands in SKILL.md call:

    • 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

Biopython Sequence Io loads about 2.1k tokens when it runs, and up to ~4k if it reads all its reference files. Until then it costs about 69 tokens; SKILL.md has 640 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~69
When it runs · the whole SKILL.md, loaded when a task matches
~2.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~4k

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 aipoch/medical-research-skills at commit 686e09d, republished under its MIT licence (© aipoch). 640 words, ~2,063 tokens.

Download SKILL.mdSave it as .claude/skills/biopython-sequence-io/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
biopython-sequence-io
description
Use Biopython to read/write/convert biological sequence files (FASTA/GenBank/FASTQ, etc.) and perform basic sequence operations; use when you need reliable sequence I/O, lightweight sequence manipulation, or scalable processing of large sequence datasets.
license
MIT
author
AIPOCH

Source: https://github.com/aipoch/medical-research-skills

biopython-sequence-io

When to Use

  • Converting between common sequence formats (e.g., FASTA ↔ GenBank, FASTQ → FASTA) while preserving identifiers and annotations.
  • Reading and writing sequence datasets for downstream pipelines (alignment, assembly, annotation) with consistent parsing and output.
  • Performing basic sequence operations (reverse complement, translation, slicing) without implementing custom parsers.
  • Processing large sequence files efficiently via streaming iteration or indexed access instead of loading everything into memory.
  • Computing simple sequence statistics and filtering records (length, GC content, ambiguous bases) during ingestion.

Key Features

  • Sequence objects and basic operations using Bio.Seq.Seq (slicing, reverse complement, transcription/translation).
  • Robust sequence I/O via Bio.SeqIO for parsing and writing FASTA/GenBank/FASTQ and other supported formats.
  • Format conversion by reading records in one format and writing them in another.
  • Scalable processing with iterator-based parsing and optional indexed access (SeqIO.index) for large files.
  • Common filtering/statistics patterns (length thresholds, GC%, quality-aware handling for FASTQ).

Dependencies

  • biopython>=1.80
  • numpy>=1.21

Example Usage

Create config/task_config.json:

json
{
  "input_path": "data/input.fasta",
  "input_format": "fasta",
  "output_path": "data/output.gb",
  "output_format": "genbank",
  "min_length": 200,
  "max_ambiguous": 0,
  "index_db_path": "data/index.sqlite"
}

Run:

bash
python scripts/sequence_io.py

scripts/sequence_io.py (runnable end-to-end):

python
import json
from pathlib import Path

import numpy as np
from Bio import SeqIO

def gc_fraction(seq: str) -> float:
    s = seq.upper()
    if not s:
        return 0.0
    return float((s.count("G") + s.count("C")) / len(s))

def ambiguous_count(seq: str) -> int:
    # Treat anything outside A/C/G/T/U as ambiguous for simple filtering.
    allowed = set("ACGTU")
    return sum(1 for ch in seq.upper() if ch not in allowed)

def main() -> None:
    config_path = Path("config/task_config.json")
    with config_path.open("r", encoding="utf-8") as f:
        cfg = json.load(f)

    input_path = Path(cfg["input_path"])
    input_format = cfg["input_format"]
    output_path = Path(cfg["output_path"])
    output_format = cfg["output_format"]

    min_length = int(cfg.get("min_length", 0))
    max_ambiguous = int(cfg.get("max_ambiguous", 10**9))

    output_path.parent.mkdir(parents=True, exist_ok=True)

    kept = 0
    lengths = []

    # Stream records to avoid loading the entire file into memory.
    with output_path.open("w", encoding="utf-8") as out_handle:
        for record in SeqIO.parse(str(input_path), input_format):
            seq_str = str(record.seq)

            if len(seq_str) < min_length:
                continue
            if ambiguous_count(seq_str) > max_ambiguous:
                continue

            # Example: attach simple stats as annotations (useful for GenBank output).
            record.annotations["gc_fraction"] = gc_fraction(seq_str)

            SeqIO.write(record, out_handle, output_format)
            kept += 1
            lengths.append(len(seq_str))

    summary = {
        "input_path": str(input_path),
        "output_path": str(output_path),
        "kept_records": kept,
        "length_min": int(np.min(lengths)) if lengths else 0,
        "length_max": int(np.max(lengths)) if lengths else 0,
        "length_mean": float(np.mean(lengths)) if lengths else 0.0,
    }

    Path("config").mkdir(parents=True, exist_ok=True)
    with Path("config/summary.json").open("w", encoding="utf-8") as f:
        json.dump(summary, f, ensure_ascii=False, indent=2)

if __name__ == "__main__":
    main()

Implementation Details

  • Configuration convention

    • Store runtime configuration in config/task_config.json as an intermediate artifact.
    • Invoke scripts uniformly with python scripts/<task_name>.py.
    • Avoid stacking many CLI -- parameters; prefer config files for reproducibility.
    • All file I/O must specify encoding="utf-8". JSON output must use ensure_ascii=False.

  • Parsing and writing

    • Use SeqIO.parse(path, format) for streaming iteration over records.
    • Use SeqIO.write(records_or_record, handle, format) to serialize records.
    • For format conversion, parse in the source format and write in the target format; ensure the target format supports the fields you expect (e.g., GenBank requires richer metadata than FASTA).

  • Large-file strategies

    • Prefer iterator-based parsing for one-pass processing.
    • For random access by record ID, use SeqIO.index(input_path, format) (creates an on-disk index depending on backend); this avoids loading all sequences into memory.

  • Filtering/statistics

    • Typical filters include min_length, maximum ambiguous characters, and quality-based criteria for FASTQ.
    • GC fraction is computed as (count(G)+count(C))/length on an uppercased sequence string; handle empty sequences safely.

  • Reference

    • See references/sequence_io.md for additional notes and format-specific behaviors.

When Not to Use

  • Do not use this skill when the required source data, identifiers, files, or credentials are missing.
  • Do not use this skill when the user asks for fabricated results, unsupported claims, or out-of-scope conclusions.
  • Do not use this skill when a simpler direct answer is more appropriate than the documented workflow.

Required Inputs

  • A clearly specified task goal aligned with the documented scope.
  • All required files, identifiers, parameters, or environment variables before execution.
  • Any domain constraints, formatting requirements, and expected output destination if applicable.
Show full SKILL.md (243 more words)Show less
  1. Validate the request against the skill boundary and confirm all required inputs are present.
  2. Select the documented execution path and prefer the simplest supported command or procedure.
  3. Produce the expected output using the documented file format, schema, or narrative structure.
  4. Run a final validation pass for completeness, consistency, and safety before returning the result.

Output Contract

  • Return a structured deliverable that is directly usable without reformatting.
  • If a file is produced, prefer a deterministic output name such as biopython_sequence_io_result.md unless the skill documentation defines a better convention.
  • Include a short validation summary describing what was checked, what assumptions were made, and any remaining limitations.

Validation and Safety Rules

  • Validate required inputs before execution and stop early when mandatory fields or files are missing.
  • Do not fabricate measurements, references, findings, or conclusions that are not supported by the provided source material.
  • Emit a clear warning when credentials, privacy constraints, safety boundaries, or unsupported requests affect the result.
  • Keep the output safe, reproducible, and within the documented scope at all times.

Failure Handling

  • If validation fails, explain the exact missing field, file, or parameter and show the minimum fix required.
  • If an external dependency or script fails, surface the command path, likely cause, and the next recovery step.
  • If partial output is returned, label it clearly and identify which checks could not be completed.

Quick Validation

Run this minimal verification path before full execution when possible:

text
No local script validation step is required for this skill.

Expected output format:

text
Result file: biopython_sequence_io_result.md
Validation summary: PASS/FAIL with brief notes
Assumptions: explicit list if any

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

Files

SKILL.md and 3 other files (references) in scientific-skills/Data Analysis/biopython-sequence-io of aipoch/medical-research-skills.

  • SKILL.md
  • biopython-sequence-io_audit_result_v2.json
  • config/task_config.json
  • references/sequence_io.md

Open the folder on GitHubat commit 686e09d

Compare with similar skills

Biopython Sequence Io 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.

Biopython Sequence Io compared with similar skills
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Biopython Bioinformaticsaiming-lab/AutoResearchClaw15k—~810Automated safety check: PassMIT
Bio Write SequencesGPTomics/bioSkills1.2k3 repos~2.1kAutomated safety check: PassMIT
Biopythondavila7/claude-code-templates32k13 repos~3.4kAutomated safety check: PassMIT
BiopythonK-Dense-AI/scientific-agent-skills48k1 repos~4.3kAutomated safety check: NotesMIT

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Works with

Categories

Questions about Biopython Sequence Io

What does Biopython Sequence Io do?

Use Biopython to read/write/convert biological sequence files (FASTA/GenBank/FASTQ, etc.) and perform basic sequence operations; use when you need reliable sequence I/O, lightweight sequence…. Biopython Sequence Io is an agent skill from aipoch/medical-research-skills.) and perform basic sequence operations; use when you need reliable sequence I/O, lightweight sequence manipulation, or scalable processing of large sequence datasets.

When should I use Biopython Sequence Io?

Biopython Sequence Io fits situations like: you need reliable sequence I/O; lightweight sequence manipulation; scalable processing of large sequence datasets.

How do I install Biopython Sequence Io in Claude Code?

Run `npx skills add aipoch/medical-research-skills --skill biopython-sequence-io -a claude-code`. Or copy the skill folder (scientific-skills/Data Analysis/biopython-sequence-io in aipoch/medical-research-skills) into .claude/skills/biopython-sequence-io in your project. Claude Code loads it when a task matches its description.

How do I install Biopython Sequence Io in Codex?

Run `npx skills add aipoch/medical-research-skills --skill biopython-sequence-io -a codex`. Or copy the skill folder (scientific-skills/Data Analysis/biopython-sequence-io in aipoch/medical-research-skills) into .agents/skills/biopython-sequence-io in your project. Codex loads it when a task matches its description.

Can I use Biopython Sequence Io 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 aipoch/medical-research-skills --skill biopython-sequence-io -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/biopython-sequence-io, .gemini/skills/biopython-sequence-io, .github/skills/biopython-sequence-io and .opencode/skills/biopython-sequence-io in your project.

What does Biopython Sequence Io need to run?

Going by SKILL.md and its folder, Biopython Sequence Io needs the command-line tools its instructions call (python). Our summary lists: Python 3.

Does Biopython Sequence Io 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 Biopython Sequence Io 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 Biopython Sequence Io use?

Biopython Sequence Io 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 Biopython Sequence Io use?

About 2.1k tokens (SKILL.md is roughly 8.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.9k tokens, read only when the agent opens those files.

What are the alternatives to Biopython Sequence Io?

Skills that share tags, products or a category with Biopython Sequence Io: Bio Restriction Mapping (GPTomics/bioSkills, 1.2k stars), Biopython Bioinformatics (aiming-lab/AutoResearchClaw, 15k stars), Bio Write Sequences (GPTomics/bioSkills, 1.2k stars) and Biopython (davila7/claude-code-templates, 32k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Biopython Sequence Io?

aipoch (a GitHub organization) maintains it in aipoch/medical-research-skills, which has 1,974 GitHub stars. The repository holds 567 skills in this directory. The repository was last updated on September 17, 2026.

Source: aipoch/medical-research-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.