Agent skill

Singlecell Qc

by xuzhougeng in xuzhougeng/wisp-science

A skill your agent uses when designing, reviewing, or implementing single-cell RNA-seq QC in Python or R with a human-in-the-loop, data-driven approach.

AGPL-3.0Auto-check passedResearch & Science

Install Singlecell Qc

skills CLI
$ npx skills add xuzhougeng/wisp-science --skill singlecell-qc -a claude-code

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

GitHub CLI
$ gh skill install xuzhougeng/wisp-science singlecell-qc --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/xuzhougeng/wisp-science.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/singlecell-qc .claude/skills/singlecell-qc && 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
singlecell-qc
GitHub stars
1k
Token cost
~1.6k tokens
SKILL.md length
535 words
Files
14 (incl. scripts, references, assets)
Skills in repo
25
Repo updated
First seen
Licence
AGPL-3.0

At a glance

A skill your agent uses when designing, reviewing, or implementing single-cell RNA-seq QC in Python or R with a human-in-the-loop, data-driven approach.

  • Works in 8 steps: Inspect before acting — matrix type,… → Pilot samples first — default 1–3… → Metrics before filters — run… → …
  • Implementing single-cell RNA-seq QC in Python
  • SKILL.md covers Overview, When To Use, Operating Rules (Human-First) and First Pass (Always), plus 8 more sections
  • Runs Python and R scripts from its folder; calls rg and python

What it does

Singlecell Qc is an agent skill from xuzhougeng/wisp-science. Use when designing, reviewing, or implementing single-cell RNA-seq QC in Python or R with a human-in-the-loop, data-driven approach. Trigger for scRNA QC metrics, per-sample diagnosis, threshold discussion, mitochondrial/ambient/doublet assessment, MAD vs fixed cutoffs, or refactoring automated merge-first QC. The analyst confirms key decisions at each step—agents must inspect data, propose options, and wait for approval before filtering, doublet removal, or merging. Not a turnkey pipeline skill.

Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 17 other files, including scripts, reference files and assets (for example `assets/qc_thresholds.example.yaml`, `references/filtering-strategies.md` and `references/human-in-the-loop.md`).

It sits in Research & Science, covering Bioinformatics and Human-in-the-loop approvals. It works with Python. The repository describes itself as: Open-source, local-first desktop AI research workbench for scientific computing with Python/R, MCP bioinformatics tools, SSH/WSL/GPU runtimes, and OpenAI/Anthropic models. The licence is AGPL-3.0.

When your agent uses it

  • Implementing single-cell RNA-seq QC in Python
  • R with a human-in-the-loop
  • Data-driven approach
  • ScRNA QC metrics

Example prompts

  • “/singlecell-qc”

Requirements

  • Python 3

Workflow steps

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

  1. Inspect before acting — matrix type, species, sample metadata, existing checkpoints.
  2. Pilot samples first — default 1–3 samples; expand only after user OK.
  3. Metrics before filters — run 01-calculate_metrics; stop and report.
  4. Propose thresholds, never silently apply — show expected cell loss per sample.
  5. Ask at gates — which metrics next? which thresholds? proceed to filter? merge?
  6. No silent heavy steps — no full-cohort filter, Scrublet, decontX, or merge without explicit approval.
  7. Reversible checkpoints — pre-filter metadata/counts stay intact; filtering writes new files.
  8. Scripts = one stage — owner-editable; thresholds visible at top of filter scripts.

What it can do on your machine

Read from SKILL.md and the folder at commit 2ba143b. 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 3 files in scripts/ (Python and R), which the agent can run.

    Shell commands in SKILL.md call:

    • rg
    • 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

Singlecell Qc loads about 1.6k tokens when it runs, and up to ~7.2k if it reads all its reference files. Until then it costs about 129 tokens; SKILL.md has 535 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~129
When it runs · the whole SKILL.md, loaded when a task matches
~1.6k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~7.2k

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 xuzhougeng/wisp-science at commit 2ba143b, republished under its AGPL-3.0 licence (© xuzhougeng). 535 words, ~1,579 tokens.

Download SKILL.mdSave it as .claude/skills/singlecell-qc/SKILL.md (or your agent's skills folder). This skill also uses 13 other files; get the full folder from GitHub.
name
singlecell-qc
description
Use when designing, reviewing, or implementing single-cell RNA-seq QC in Python or R with a human-in-the-loop, data-driven approach. Trigger for scRNA QC metrics, per-sample diagnosis, threshold discussion, mitochondrial/ambient/doublet assessment, MAD vs fixed cutoffs, or refactoring automated merge-first QC. The analyst confirms key decisions at each step—agents must inspect data, propose options, and wait for approval before filtering, doublet removal, or merging. Not a turnkey pipeline skill.

Single-Cell QC

Overview

Use this skill for data-driven, human-centered single-cell QC. The analyst inspects distributions and confirms decisions; code computes metrics and executes only what was agreed.

text
inspect data → compute metrics → human reviews → confirm thresholds → small action → re-inspect

Not a one-click pipeline. Do not chain calculate → filter → doublet → merge unless the user explicitly requests full execution after reviewing pilot results.

Follow analysis-workflow for module and script layout. Match the user's language.

Read first: references/human-in-the-loop.md

When To Use

  • "帮我看看这个样本 QC"
  • "算一下 QC 指标,阈值我来定"
  • "逐样本诊断,先别过滤"
  • "这个 merge-first QC 太粗,怎么改成人工确认"
  • "参考 GZL metrics 脚本,但要分步做"

Do not use for integration/Harmony, annotation, or spatial QC unless only expression-matrix QC is needed.

Operating Rules (Human-First)

  1. Inspect before acting — matrix type, species, sample metadata, existing checkpoints.
  2. Pilot samples first — default 1–3 samples; expand only after user OK.
  3. Metrics before filters — run 01-calculate_metrics; stop and report.
  4. Propose thresholds, never silently apply — show expected cell loss per sample.
  5. Ask at gates — which metrics next? which thresholds? proceed to filter? merge?
  6. No silent heavy steps — no full-cohort filter, Scrublet, decontX, or merge without explicit approval.
  7. Reversible checkpoints — pre-filter metadata/counts stay intact; filtering writes new files.
  8. Scripts = one stage — owner-editable; thresholds visible at top of filter scripts.

Full gate definitions: references/human-in-the-loop.md

First Pass (Always)

bash
find <project_root> -maxdepth 4 -type f \( -name '*.py' -o -name '*.R' -o -name '*.h5ad' -o -name '*.md' \) | head -60
rg -n "filter_cells|calculate_qc|metadata|mito|n_genes" <project_root>/scripts 2>/dev/null | head -30

Report to the user:

  • input matrix type (raw / filtered / EmptyDrops / h5ad);
  • species; sample count;
  • whether per-sample or merge-first QC exists;
  • recommended next single step (not full pipeline).

Then ask which samples to pilot and which metrics matter for this tissue.

Staged Workflow (Default)

Each stage ends with human confirmation.

StageScript / actionAgent stops until user confirms
AInput inspectionsample list, matrix, species
B01-calculate_metrics (pilot)metric scope (core / hbb / doublet / …)
C02-qc_diagnosis figuresfigures match expectations
DThreshold proposal (table + loss estimate)per-sample cutoffs
E03-filter_cellsfilter summary acceptable
Foptional doublet / ambientmethod and aggressiveness
G04-merge_qc_passedall samples signed off

Stages D–G are skipped until the user says proceed.

Show full SKILL.md (222 more words)Show less
Metric tiers (choose with user)
TierMetricsAsk when
Coren_genes, n_UMIs, mito_frac, pct_counts_rbalways unless h5ad already has them
Recommendedhbb_score, doublet_score, cell cycletissue-dependent
ExtendedchrY_frac, ambient_frac, nuclear_fracmetadata / STARsolo available

Details: references/metrics-catalog.md

Project Layout

Optional scaffold — create only stages the user needs:

text
scripts/01-qc/
  01-calculate_metrics.py|R   # metrics only
  02-qc_diagnosis.py|R        # figures from metadata
  03-filter_cells.py|R        # runs only after threshold sign-off
result/01-qc/ ...
figure/01-qc/ ...

references/project-layout.md

Bundled Tools (Not a Pipeline)

ToolRole
scripts/calculate_metrics.pycore metrics → metadata.tsv
scripts/calculate_metrics.Rsame, R/Matrix
scripts/inspect_qc_metadata.pyread-only cohort summary
assets/gene_sets/*hbb / chrY gene lists
assets/qc_thresholds.example.yamltemplate for user-edited thresholds

--run-scrublet on Python script: ask before using.

bash
# Typical pilot — metrics only
python .../calculate_metrics.py \
  --matrix-dir <dir> --sample-id PILOT --species human \
  --output-dir result/01-qc/01-calculate_metrics/PILOT

After Metrics: Report Template

Use the template in references/human-in-the-loop.md:

  • observations (numbers);
  • flags (sex mismatch, high hbb, depth outlier);
  • questions for the user (numbered);
  • optional threshold table with estimated loss — label as not yet applied.

Language Choice

ContextReference
scanpy / h5adreferences/python-scanpy.md
Seuratreferences/r-seurat.md
threshold methodsreferences/filtering-strategies.md

Pick one canonical metadata schema across languages (n_genes, n_UMIs, mito_frac, …).

Anti-Patterns

  • Running full cohort filter + merge in one agent turn
  • Picking thresholds without showing per-sample distributions
  • Treating bundled scripts as end-to-end QC
  • Hiding cutoffs inside opaque helpers
  • Merge-first global QC without per-sample review (legacy atlas reproduction excepted)

Deliverables (Stage-Dependent)

Only produce what the current confirmed stage needs:

After stageDeliverable
Bmetadata.tsv, metrics_summary.json
Cdiagnosis PDFs/PNGs
Dthreshold proposal table (no filter yet)
Efiltered checkpoint + filter_summary
Sign-offQC_summary.tsv + documented per-sample decisions

External References

  • Rich metrics example (R): <project-root>/scripts/calculate_metrics_extended.R
  • Legacy contrast (avoid as default): spatial_data/.../run_merging_samples_and_QC.py

© xuzhougeng, AGPL-3.0. 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 13 other files (scripts, references, assets) in skills/singlecell-qc of xuzhougeng/wisp-science.

  • SKILL.md
  • assets/gene_sets/chrY_genes_human.txt
  • assets/gene_sets/hbb_genes_human.txt
  • assets/gene_sets/hbb_genes_mouse.txt
  • assets/qc_thresholds.example.yaml
  • references/filtering-strategies.md
  • references/human-in-the-loop.md
  • references/metrics-catalog.md
  • references/project-layout.md
  • references/python-scanpy.md
  • references/r-seurat.md
  • scripts/calculate_metrics.R
  • scripts/calculate_metrics.py
  • scripts/inspect_qc_metadata.py

Open the folder on GitHubat commit 2ba143b

Compare with similar skills

Singlecell Qc 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.

Singlecell Qc compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Singlecell Qc this skillxuzhougeng/wisp-science1k—~1.6kAutomated safety check: PassAGPL-3.0
Spatial TranscriptomicsQING1105/ezST101—~1.4kAutomated safety check: PassMIT
Alphagenome Single Variant Analysisgoogle-deepmind/science-skills3.2k2 repos~3kAutomated safety check: NotesApache-2.0
13C Metabolic Flux AnalysisK-Dense-AI/scientific-agent-skills48k1 repos~3.2kAutomated safety check: PassMIT
Trackplotygidtu/trackplot109—~1.9kAutomated safety check: PassBSD-3-Clause
UniProt Database Accessdavila7/claude-code-templates33k14 repos~1.7kAutomated safety check: PassMIT

Similar skills

  • End-to-end 10x Visium spatial transcriptomics analysis workflow with staged execution and human review gates.

    101 GitHub stars~1.4k tokensUpdated 1 mo ago
    Research & ScienceAuto-check passed
  • Alphagenome Single Variant Analysis

    google-deepmind/science-skills

    Analyzes genetic variant effects on gene expression (RNA-seq), chromatin accessibility (DNASE), histone marks (ChIP), and transcription factors using the AlphaGenome API.

    3.2k GitHub starsUsed in 2 repos~3k tokens
    Research & ScienceAuto-check: notes
  • 13C Metabolic Flux Analysis

    K-Dense-AI/scientific-agent-skills

    Estimates reaction fluxes inside cells from steady-state carbon-13 labeling data with a bundled mfapy-based solver, and reports which fluxes the data pin down.

    48k GitHub starsUsed in 1 repo~3.2k tokens
    Research & ScienceAuto-check passed
  • Trackplot

    ygidtu/trackplot

    Generate sashimi-style genome visualization plots (coverage, line, heatmap, IGV read-by-read, HiC, circRNA, motif) from BAM/bigWig/depth/HiC inputs.

    109 GitHub stars~1.9k tokensUpdated 14 days ago
    Research & ScienceAuto-check passed
  • UniProt Database Access

    davila7/claude-code-templates

    Queries the UniProt REST API directly to search proteins, fetch FASTA sequences, map IDs between databases and read Swiss-Prot and TrEMBL entries.

    33k GitHub starsUsed in 14 repos~1.7k tokens
    Research & ScienceAuto-check passed
  • deepTools NGS Toolkit

    davila7/claude-code-templates

    Guides use of deepTools on sequencing data: BAM to bigWig conversion, QC, sample correlation, and heatmaps or profiles around TSS and peaks for ChIP-seq, RNA-seq and ATAC-seq.

    33k GitHub starsUsed in 12 repos~4.5k tokens
    Research & ScienceAuto-check passed

More from xuzhougeng/wisp-science

All 25 skills in this repo
  • Research Integrity Audit

    xuzhougeng/wisp-science

    学术审查 / research-integrity screening of a manuscript's figures and reported numbers.

    1k GitHub stars~2.6k tokensUpdated today
    Auto-check passed
  • Distill Concept Books

    xuzhougeng/wisp-science

    将概念、理论或分析方法类图书蒸馏为证据可追溯、经人工门禁审核且不暴露书名、作者、出版社等来源身份的任务型 Skill 候选。用于新建或恢复图书蒸馏、以本地 Tesseract 扫描 DOCX 全部内嵌图像或 Poppler 渲染的扫描 PDF 全页、建立 source map 与 evidence/claim/relation/capability…

    1k GitHub stars~2.3k tokensUpdated today
    Auto-check passed
  • Skill Creator

    xuzhougeng/wisp-science

    Create, update, validate, and evaluate Wisp skills. An agent skill from xuzhougeng/wisp-science.

    1k GitHub stars~1.3k tokensUpdated today
    Auto-check passed
  • Word Zotero Citations

    xuzhougeng/wisp-science

    Build, audit, authorize, recover, or finalize dynamic Zotero citations and bibliographies in Microsoft Word DOCX files with a protected-source, digest-bound workflow.

    1k GitHub stars~3.7k tokensUpdated today
    Auto-check passed
  • Compute Env Setup

    xuzhougeng/wisp-science

    Set up and validate a reproducible Python or R environment on a Wisp execution context.

    1k GitHub stars~1.1k tokensUpdated today
    Auto-check passed
  • Indication Dossier

    xuzhougeng/wisp-science

    Build a sourced research dossier for one therapeutic indication — patient population, epidemiology, disease biology, standard of care, regulatory path, and landmark trials.

    1k GitHub stars~1k tokensUpdated today
    Auto-check passed

Works with

Questions about Singlecell Qc

What does Singlecell Qc do?

A skill your agent uses when designing, reviewing, or implementing single-cell RNA-seq QC in Python or R with a human-in-the-loop, data-driven approach. Singlecell Qc is an agent skill from xuzhougeng/wisp-science. Use when designing, reviewing, or implementing single-cell RNA-seq QC in Python or R with a human-in-the-loop, data-driven approach.

When should I use Singlecell Qc?

Singlecell Qc fits situations like: implementing single-cell RNA-seq QC in Python; R with a human-in-the-loop; data-driven approach; scRNA QC metrics.

How do I install Singlecell Qc in Claude Code?

Run `npx skills add xuzhougeng/wisp-science --skill singlecell-qc -a claude-code`. Or copy the skill folder (skills/singlecell-qc in xuzhougeng/wisp-science) into .claude/skills/singlecell-qc in your project. Claude Code loads it when a task matches its description.

How do I install Singlecell Qc in Codex?

Run `npx skills add xuzhougeng/wisp-science --skill singlecell-qc -a codex`. Or copy the skill folder (skills/singlecell-qc in xuzhougeng/wisp-science) into .agents/skills/singlecell-qc in your project. Codex loads it when a task matches its description.

Can I use Singlecell Qc 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 xuzhougeng/wisp-science --skill singlecell-qc -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/singlecell-qc, .gemini/skills/singlecell-qc, .github/skills/singlecell-qc and .opencode/skills/singlecell-qc in your project.

What does Singlecell Qc need to run?

Going by SKILL.md and its folder, Singlecell Qc needs Python and R for the scripts in its folder and the command-line tools its instructions call (rg and python). Our summary lists: Python 3.

Does Singlecell Qc 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 Singlecell Qc 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 Singlecell Qc use?

Singlecell Qc is published under the AGPL-3.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Singlecell Qc use?

About 1.6k tokens (SKILL.md is roughly 6.3k 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 5.6k tokens, read only when the agent opens those files.

What are the alternatives to Singlecell Qc?

Skills that share tags, products or a category with Singlecell Qc: Spatial Transcriptomics (QING1105/ezST, 101 stars), Alphagenome Single Variant Analysis (google-deepmind/science-skills, 3.2k stars), 13C Metabolic Flux Analysis (K-Dense-AI/scientific-agent-skills, 48k stars) and Trackplot (ygidtu/trackplot, 109 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Singlecell Qc?

xuzhougeng (a GitHub user) maintains it in xuzhougeng/wisp-science, which has 1,026 GitHub stars. The repository holds 25 skills in this directory. The repository was last updated on October 10, 2026.

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