Spatial Transcriptomics
QING1105/ezST
End-to-end 10x Visium spatial transcriptomics analysis workflow with staged execution and human review gates.
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.
$ npx skills add xuzhougeng/wisp-science --skill singlecell-qc -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install xuzhougeng/wisp-science singlecell-qc --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ 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-srcUse ~/.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/
Install the "singlecell-qc" agent skill from https://github.com/xuzhougeng/wisp-science/tree/main/skills/singlecell-qc into .claude/skills/singlecell-qc/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "singlecell-qc", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/xuzhougeng/wisp-science/tree/main/skills/singlecell-qcType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add xuzhougeng/wisp-science --skill singlecell-qc -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install xuzhougeng/wisp-science singlecell-qc --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/xuzhougeng/wisp-science.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/singlecell-qc .agents/skills/singlecell-qc && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "singlecell-qc" agent skill from https://github.com/xuzhougeng/wisp-science/tree/main/skills/singlecell-qc into .agents/skills/singlecell-qc/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "singlecell-qc", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add xuzhougeng/wisp-science --skill singlecell-qc -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install xuzhougeng/wisp-science singlecell-qc --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/xuzhougeng/wisp-science.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/singlecell-qc .cursor/skills/singlecell-qc && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "singlecell-qc" agent skill from https://github.com/xuzhougeng/wisp-science/tree/main/skills/singlecell-qc into .cursor/skills/singlecell-qc/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "singlecell-qc", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/xuzhougeng/wisp-science.git --path skills/singlecell-qc--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add xuzhougeng/wisp-science --skill singlecell-qc -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install xuzhougeng/wisp-science singlecell-qc --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/xuzhougeng/wisp-science.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/singlecell-qc .gemini/skills/singlecell-qc && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "singlecell-qc" agent skill from https://github.com/xuzhougeng/wisp-science/tree/main/skills/singlecell-qc into .gemini/skills/singlecell-qc/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "singlecell-qc", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install xuzhougeng/wisp-science singlecell-qcInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add xuzhougeng/wisp-science --skill singlecell-qc -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/xuzhougeng/wisp-science.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/singlecell-qc .github/skills/singlecell-qc && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "singlecell-qc" agent skill from https://github.com/xuzhougeng/wisp-science/tree/main/skills/singlecell-qc into .github/skills/singlecell-qc/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "singlecell-qc", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add xuzhougeng/wisp-science --skill singlecell-qc -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install xuzhougeng/wisp-science singlecell-qc --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/xuzhougeng/wisp-science.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/singlecell-qc .opencode/skills/singlecell-qc && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "singlecell-qc" agent skill from https://github.com/xuzhougeng/wisp-science/tree/main/skills/singlecell-qc into .opencode/skills/singlecell-qc/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "singlecell-qc", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
singlecell-qcA 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. 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.
8 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit 2ba143b. It shows what the files ask for, not the result of running them.
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.
Ships 3 files in scripts/ (Python and R), which the agent can run.
Shell commands in SKILL.md call:
rgpythonFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
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.
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.
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.
The full file from xuzhougeng/wisp-science at commit 2ba143b, republished under its AGPL-3.0 licence (© xuzhougeng). 535 words, ~1,579 tokens.
.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.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.
inspect data → compute metrics → human reviews → confirm thresholds → small action → re-inspectNot 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
Do not use for integration/Harmony, annotation, or spatial QC unless only expression-matrix QC is needed.
01-calculate_metrics; stop and report.Full gate definitions: references/human-in-the-loop.md
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 -30Report to the user:
Then ask which samples to pilot and which metrics matter for this tissue.
Each stage ends with human confirmation.
| Stage | Script / action | Agent stops until user confirms |
|---|---|---|
| A | Input inspection | sample list, matrix, species |
| B | 01-calculate_metrics (pilot) | metric scope (core / hbb / doublet / …) |
| C | 02-qc_diagnosis figures | figures match expectations |
| D | Threshold proposal (table + loss estimate) | per-sample cutoffs |
| E | 03-filter_cells | filter summary acceptable |
| F | optional doublet / ambient | method and aggressiveness |
| G | 04-merge_qc_passed | all samples signed off |
Stages D–G are skipped until the user says proceed.
| Tier | Metrics | Ask when |
|---|---|---|
| Core | n_genes, n_UMIs, mito_frac, pct_counts_rb | always unless h5ad already has them |
| Recommended | hbb_score, doublet_score, cell cycle | tissue-dependent |
| Extended | chrY_frac, ambient_frac, nuclear_frac | metadata / STARsolo available |
Details: references/metrics-catalog.md
Optional scaffold — create only stages the user needs:
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
| Tool | Role |
|---|---|
scripts/calculate_metrics.py | core metrics → metadata.tsv |
scripts/calculate_metrics.R | same, R/Matrix |
scripts/inspect_qc_metadata.py | read-only cohort summary |
assets/gene_sets/* | hbb / chrY gene lists |
assets/qc_thresholds.example.yaml | template for user-edited thresholds |
--run-scrublet on Python script: ask before using.
# Typical pilot — metrics only
python .../calculate_metrics.py \
--matrix-dir <dir> --sample-id PILOT --species human \
--output-dir result/01-qc/01-calculate_metrics/PILOTUse the template in references/human-in-the-loop.md:
| Context | Reference |
|---|---|
| scanpy / h5ad | references/python-scanpy.md |
| Seurat | references/r-seurat.md |
| threshold methods | references/filtering-strategies.md |
Pick one canonical metadata schema across languages (n_genes, n_UMIs, mito_frac, …).
Only produce what the current confirmed stage needs:
| After stage | Deliverable |
|---|---|
| B | metadata.tsv, metrics_summary.json |
| C | diagnosis PDFs/PNGs |
| D | threshold proposal table (no filter yet) |
| E | filtered checkpoint + filter_summary |
| Sign-off | QC_summary.tsv + documented per-sample decisions |
<project-root>/scripts/calculate_metrics_extended.Rspatial_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
SKILL.md and 13 other files (scripts, references, assets) in skills/singlecell-qc of xuzhougeng/wisp-science.
Open the folder on GitHubat commit 2ba143b
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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Singlecell Qc this skillxuzhougeng/wisp-science | 1k | — | ~1.6k | Automated safety check: Pass | AGPL-3.0 | |
| Spatial TranscriptomicsQING1105/ezST | 101 | — | ~1.4k | Automated safety check: Pass | MIT | |
| Alphagenome Single Variant Analysisgoogle-deepmind/science-skills | 3.2k | 2 repos | ~3k | Automated safety check: Notes | Apache-2.0 | |
| 13C Metabolic Flux AnalysisK-Dense-AI/scientific-agent-skills | 48k | 1 repos | ~3.2k | Automated safety check: Pass | MIT | |
| Trackplotygidtu/trackplot | 109 | — | ~1.9k | Automated safety check: Pass | BSD-3-Clause | |
| UniProt Database Accessdavila7/claude-code-templates | 33k | 14 repos | ~1.7k | Automated safety check: Pass | MIT |
QING1105/ezST
End-to-end 10x Visium spatial transcriptomics analysis workflow with staged execution and human review gates.
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.
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.
ygidtu/trackplot
Generate sashimi-style genome visualization plots (coverage, line, heatmap, IGV read-by-read, HiC, circRNA, motif) from BAM/bigWig/depth/HiC inputs.
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.
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.
xuzhougeng/wisp-science
学术审查 / research-integrity screening of a manuscript's figures and reported numbers.
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…
xuzhougeng/wisp-science
Create, update, validate, and evaluate Wisp skills. An agent skill from xuzhougeng/wisp-science.
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.
xuzhougeng/wisp-science
Set up and validate a reproducible Python or R environment on a Wisp execution context.
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.
Works with
Categories
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.