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Agent skill

Scrna Cell Type Annotator

by aipoch in aipoch/medical-research-skills

Auto-annotate cell clusters from single-cell RNA data using marker genes.

MITAuto-check passedResearch & Science

Install Scrna Cell Type Annotator

skills CLI
$ npx skills add aipoch/medical-research-skills --skill scrna-cell-type-annotator -a claude-code

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

GitHub CLI
$ gh skill install aipoch/medical-research-skills scrna-cell-type-annotator --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/scrna-cell-type-annotator' .claude/skills/scrna-cell-type-annotator && 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
scrna-cell-type-annotator
GitHub stars
2k
Token cost
~1.7k tokens
SKILL.md length
782 words
Files
4 (incl. scripts)
Skills in repo
567
Repo updated
First seen
Licence
MIT

At a glance

Auto-annotate cell clusters from single-cell RNA data using marker genes.

  • Works in 4 steps: Confirm the user input, output path, and… → Edit the in-file CONFIG block or… → Run python scripts/main.py with the… → …
  • Tasks that involve Bioinformatics
  • SKILL.md covers When to Use, Key Features, Dependencies and Example Usage, plus 17 more sections
  • Runs Python scripts from its folder; calls python

What it does

Scrna Cell Type Annotator is an agent skill from aipoch/medical-research-skills. Auto-annotate cell clusters from single-cell RNA data using marker genes.

Its SKILL.md is about 1.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including scripts (for example `scripts/main.py` and `scrna-cell-type-annotator_audit_result_v2.json`).

It sits in Research & Science, covering Bioinformatics. 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

  • Tasks that involve Bioinformatics

Example prompts

  • “/scrna-cell-type-annotator”

Requirements

  • Python 3

Workflow steps

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

  1. Confirm the user input, output path, and any required config values.
  2. Edit the in-file CONFIG block or documented parameters if the script uses fixed settings.
  3. Run python scripts/main.py with the validated inputs.
  4. Review the generated output and return the final artifact with any assumptions called out.

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

    Ships 1 file in scripts/ (Python), which the agent can run.

    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

Scrna Cell Type Annotator loads about 1.7k tokens when it runs. Until then it costs about 25 tokens; SKILL.md has 782 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~25
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); the scripts in this folder are not scanned.

SKILL.md

The full file from aipoch/medical-research-skills at commit 686e09d, republished under its MIT licence (© aipoch). 782 words, ~1,690 tokens.

Download SKILL.mdSave it as .claude/skills/scrna-cell-type-annotator/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
scrna-cell-type-annotator
description
Auto-annotate cell clusters from single-cell RNA data using marker genes.
license
MIT
author
AIPOCH

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

ScRNA Cell Type Annotator

Single-cell cluster identification.

When to Use

  • Use this skill when the task needs Auto-annotate cell clusters from single-cell RNA data using marker genes.
  • Use this skill for data analysis tasks that require explicit assumptions, bounded scope, and a reproducible output format.
  • Use this skill when you need a documented fallback path for missing inputs, execution errors, or partial evidence.

Key Features

  • Scope-focused workflow aligned to: Auto-annotate cell clusters from single-cell RNA data using marker genes.
  • Packaged executable path(s): scripts/main.py.
  • Structured execution path designed to keep outputs consistent and reviewable.

Dependencies

See ## Prerequisites above for related details.

  • Python: 3.10+. Repository baseline for current packaged skills.
  • pandas: unspecified. Declared in requirements.txt.

Example Usage

bash
cd "20260318/scientific-skills/Data Analytics/scrna-cell-type-annotator"
python -m py_compile scripts/main.py
python scripts/main.py --help

Example run plan:

  1. Confirm the user input, output path, and any required config values.
  2. Edit the in-file CONFIG block or documented parameters if the script uses fixed settings.
  3. Run python scripts/main.py with the validated inputs.
  4. Review the generated output and return the final artifact with any assumptions called out.

Implementation Details

See ## Workflow above for related details.

  • Execution model: validate the request, choose the packaged workflow, and produce a bounded deliverable.
  • Input controls: confirm the source files, scope limits, output format, and acceptance criteria before running any script.
  • Primary implementation surface: scripts/main.py.
  • Parameters to clarify first: input path, output path, scope filters, thresholds, and any domain-specific constraints.
  • Output discipline: keep results reproducible, identify assumptions explicitly, and avoid undocumented side effects.

Quick Check

Use this command to verify that the packaged script entry point can be parsed before deeper execution.

bash
python -m py_compile scripts/main.py

Audit-Ready Commands

Use these concrete commands for validation. They are intentionally self-contained and avoid placeholder paths.

bash
python -m py_compile scripts/main.py
python scripts/main.py --help

Workflow

  1. Confirm the user objective, required inputs, and non-negotiable constraints before doing detailed work.
  2. Validate that the request matches the documented scope and stop early if the task would require unsupported assumptions.
  3. Use the packaged script path or the documented reasoning path with only the inputs that are actually available.
  4. Return a structured result that separates assumptions, deliverables, risks, and unresolved items.
  5. If execution fails or inputs are incomplete, switch to the fallback path and state exactly what blocked full completion.

Use Cases

  • Post-clustering annotation
  • Novel cell type discovery
  • Cross-study comparison
  • Atlas construction

Parameters

  • cluster_markers: DEG per cluster
  • tissue_type: Organ context
  • species: Human/mouse

Returns

  • Cell type predictions
  • Marker gene support
  • Confidence levels
  • Alternative suggestions

Example

Cluster 1: IL2RA, CD3D → CD4 T cells

Risk Assessment

Risk IndicatorAssessmentLevel
Code ExecutionPython/R scripts executed locallyMedium
Network AccessNo external API callsLow
File System AccessRead input files, write output filesMedium
Instruction TamperingStandard prompt guidelinesLow
Data ExposureOutput files saved to workspaceLow
Show full SKILL.md (335 more words)Show less

Security Checklist

  • No hardcoded credentials or API keys
  • No unauthorized file system access (../)
  • Output does not expose sensitive information
  • Prompt injection protections in place
  • Input file paths validated (no ../ traversal)
  • Output directory restricted to workspace
  • Script execution in sandboxed environment
  • Error messages sanitized (no stack traces exposed)
  • Dependencies audited

Prerequisites

text

# Python dependencies
pip install -r requirements.txt

Evaluation Criteria

Success Metrics
  • Successfully executes main functionality
  • Output meets quality standards
  • Handles edge cases gracefully
  • Performance is acceptable
Test Cases
  1. Basic Functionality: Standard input → Expected output
  2. Edge Case: Invalid input → Graceful error handling
  3. Performance: Large dataset → Acceptable processing time

Lifecycle Status

  • Current Stage: Draft
  • Next Review Date: 2026-03-06
  • Known Issues: None
  • Planned Improvements:
    • Performance optimization
    • Additional feature support

Output Requirements

Every final response should make these items explicit when they are relevant:

  • Objective or requested deliverable
  • Inputs used and assumptions introduced
  • Workflow or decision path
  • Core result, recommendation, or artifact
  • Constraints, risks, caveats, or validation needs
  • Unresolved items and next-step checks

Error Handling

  • If required inputs are missing, state exactly which fields are missing and request only the minimum additional information.
  • If the task goes outside the documented scope, stop instead of guessing or silently widening the assignment.
  • If scripts/main.py fails, report the failure point, summarize what still can be completed safely, and provide a manual fallback.
  • Do not fabricate files, citations, data, search results, or execution outcomes.

Input Validation

This skill accepts requests that match the documented purpose of scrna-cell-type-annotator and include enough context to complete the workflow safely.

Do not continue the workflow when the request is out of scope, missing a critical input, or would require unsupported assumptions. Instead respond:

scrna-cell-type-annotator only handles its documented workflow. Please provide the missing required inputs or switch to a more suitable skill.

Response Template

Use the following fixed structure for non-trivial requests:

  1. Objective
  2. Inputs Received
  3. Assumptions
  4. Workflow
  5. Deliverable
  6. Risks and Limits
  7. Next Checks

If the request is simple, you may compress the structure, but still keep assumptions and limits explicit when they affect correctness.

© 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 (scripts) in scientific-skills/Data Analysis/scrna-cell-type-annotator of aipoch/medical-research-skills.

  • SKILL.md
  • requirements.txt
  • scripts/main.py
  • scrna-cell-type-annotator_audit_result_v2.json

Open the folder on GitHubat commit 686e09d

Compare with similar skills

Scrna Cell Type Annotator 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.

Scrna Cell Type Annotator compared with similar skills
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Metabolic Study Planneraiming-lab/AutoResearchClaw15k—~1.9kAutomated safety check: PassMIT
13C Metabolic Flux AnalysisK-Dense-AI/scientific-agent-skills48k1 repos~3.2kAutomated safety check: PassMIT
Alphagenome Single Variant Analysisgoogle-deepmind/science-skills3.2k2 repos~3kAutomated safety check: NotesApache-2.0
MFA Pipeline Orchestratoraiming-lab/AutoResearchClaw15k—~923Automated safety check: PassMIT

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Categories

Questions about Scrna Cell Type Annotator

What does Scrna Cell Type Annotator do?

Auto-annotate cell clusters from single-cell RNA data using marker genes. Scrna Cell Type Annotator is an agent skill from aipoch/medical-research-skills. Auto-annotate cell clusters from single-cell RNA data using marker genes.

When should I use Scrna Cell Type Annotator?

Scrna Cell Type Annotator fits situations like: tasks that involve Bioinformatics.

How do I install Scrna Cell Type Annotator in Claude Code?

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

How do I install Scrna Cell Type Annotator in Codex?

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

Can I use Scrna Cell Type Annotator 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 scrna-cell-type-annotator -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/scrna-cell-type-annotator, .gemini/skills/scrna-cell-type-annotator, .github/skills/scrna-cell-type-annotator and .opencode/skills/scrna-cell-type-annotator in your project.

What does Scrna Cell Type Annotator need to run?

Going by SKILL.md and its folder, Scrna Cell Type Annotator needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.

Does Scrna Cell Type Annotator 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 Scrna Cell Type Annotator 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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Scrna Cell Type Annotator use?

Scrna Cell Type Annotator 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 Scrna Cell Type Annotator use?

About 1.7k tokens (SKILL.md is roughly 6.8k 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 Scrna Cell Type Annotator?

Skills that share tags, products or a category with Scrna Cell Type Annotator: Dbsnp Database (google-deepmind/science-skills, 3.2k stars), Metabolic Study Planner (aiming-lab/AutoResearchClaw, 15k stars), 13C Metabolic Flux Analysis (K-Dense-AI/scientific-agent-skills, 48k stars) and Alphagenome Single Variant Analysis (google-deepmind/science-skills, 3.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Scrna Cell Type Annotator?

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.