universal-single-cell-annotator
- Repo stars 330
- License MIT
- Author updated Live
- Author repo claude-skill-registry
- Domain
- AI
- Compatible agents
-
- Claude Code
- Cursor
- Cline
- Codex
- Windsurf
- Gemini CLI
- +20
- Trust score
- 94 / 100 · audit passed
- Author / version / license
- @majiayu000 · MIT
- Token usage
- Lean
- Setup complexity
- Plug-and-play
- External API key
- Not required
- Operating systems
- Unspecified (assume cross-platform)
- Runtime requirements
- Python
- Permissions
-
- Read-only
- Write / modify
- Network behavior
- Local-only
- Install commands
- 26 variants
Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.
Heads up: 未限定 allowed-tools，默认拥有全部工具权限。
---
name: universal-single-cell-annotator
description: A unified interface for annotating single-cell RNA-seq data using Marker Genes, Deep Learning (C…
category: ai
runtime: Python
---
# universal-single-cell-annotator output preview
## PART A: Task fit
- Use case: A unified interface for annotating single-cell RNA-seq data using Marker Genes, Deep Learning (CellTypist), or LLMs. This skill wraps multiple cell type annotation strategies into a single Python class. It allows agents to flexibly choose between rule-based (markers), data-driven (CellTypist), or reasoning-based (LLM) approaches depending on the context. ….
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “When to Use This Skill / Core Capabilities / Workflow” and do not present inference as author intent.
## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “A unified interface for annotating single-cell RNA-seq data using Marker Genes, Deep Learning (CellTypist), or LLMs. This skill wraps multiple cell type annotation strategies into a single Python class. It allows agents to flexibly choose between rule-based (markers), data-driven (CellTypist), or reasoning-based (LLM) approaches depending on the context. …”.
- **02** When the source has headings, the agent prioritizes “When to Use This Skill / Core Capabilities / Workflow” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files; mostly runs locally; usually needs no extra API key.
## Running Rules
- read files, write/modify files; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options. Choose the trigger
The source does not require a stable slash command. After installation, invoke the skill by name and describe the task.
Provide inputs and boundaries
Name target files or source material, expected output, forbidden changes, and whether network or shell access is allowed. Permission fingerprint: read files, write/modify files.
Validate with a small run
Start with a small task and check whether the result follows “When to Use This Skill / Core Capabilities / Workflow”. Inspect diffs, logs, previews, or tests before expanding scope.
Review before delivery
Confirm the final output includes a concrete result, evidence, and next action. If it stays generic, tighten inputs, boundaries, and acceptance criteria.
---
name: universal-single-cell-annotator
description: A unified interface for annotating single-cell RNA-seq data using Marker Genes, Deep Learning (C…
category: ai
source: majiayu000/claude-skill-registry
---
# universal-single-cell-annotator
## When to use
- A unified interface for annotating single-cell RNA-seq data using Marker Genes, Deep Learning (CellTypist), or LLMs. T…
- Use it when the task has clear inputs, repeatable steps, and validation criteria.
## What to provide
- Target material, scope, expected result, and forbidden changes.
- Whether network, commands, file writes, or external services are allowed.
## Execution rules
- Organize steps around “When to Use This Skill / Core Capabilities / Workflow” and keep inference separate from source facts.
- read files, write/modify files; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding the task.
## Output requirements
- Return the deliverable, key evidence, validation method, and next action.
- Mark missing information as unknown; do not invent commands, platforms, or dependencies. Evidence boundary and execution chain
The author source anchors workflow facts; repository files anchor sources and commands; Fluxly only adds fit, limitations, and quality judgment.
skill "universal-single-cell-annotator" {
input -> user goal + target files + boundaries + acceptance criteria
context -> When to Use This Skill / Core Capabilities / Workflow
rules -> SKILL.md triggers / order / output contract
runtime -> Python | read files, write/modify files | mostly runs locally
guardrails -> usually needs no extra API key + small-sample validation + diff/log review
output -> copyable result + checklist + next iteration
}Universal Single-Cell Annotator
This skill wraps multiple cell type annotation strategies into a single Python class. It allows agents to flexibly choose between rule-based (markers), data-driven (CellTypist), or reasoning-based (LLM) approaches depending on the context.
When to Use This Skill
- Initial Analysis: When processing raw AnnData objects.
- Validation: When cross-referencing automated labels with known markers.
- Discovery: When identifying rare cell types using LLM reasoning on marker lists.
Core Capabilities
- Marker-Based Scoring: Scores cells based on provided gene lists (e.g., "T-cell": ["CD3D", "CD3E"]).
- Deep Learning Reference: Wraps
celltypistto transfer labels from massive atlases. - LLM Reasoning: Extracts top markers per cluster and constructs prompts for LLM interpretation.
Workflow
- Load Data: Ensure data is in
AnnDataformat (standard for Scanpy). - Choose Strategy:
- Use Markers if you have a known gene panel.
- Use CellTypist for broad immune/tissue profiling.
- Use LLM for novel clusters.
- Annotate: Run the corresponding method.
- Inspect: Check
adata.obsfor the new annotation columns.
Example Usage
User: "Annotate this dataset looking for T-cells and B-cells."
Agent Action:
from universal_annotator import UniversalAnnotator
import scanpy as sc
adata = sc.read_h5ad('data.h5ad')
annotator = UniversalAnnotator(adata)
markers = {
'T-cell': ['CD3D', 'CD3E', 'CD8A'],
'B-cell': ['CD79A', 'MS4A1']
}
annotator.annotate_marker_based(markers)
# Results in adata.obs['predicted_cell_type']
Decide Fit First
Design Intent
How To Use It
Boundaries And Review