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scarf-single-cell
82 / 100 · Low risk
Fine to try.
By sickn33/agentic-awesome-skills (GitHub) · coding · 49,524 stars · scanned at commit 00f9c40
Analyze single-cell RNA-seq at million-cell scale with Scarf: out-of-core Zarr stores on disk or object storage, provenance-tracked artifacts, audited QC, clustering, markers and donor comparisons.
Works with any agent that supports SKILL.md skills.
What this skill can do
Downloads and installs other software
The author does not mention this.
Found in 2 places.
Connects to the internet
This is disclosed by the author in the skill's SKILL.md or README.
Found in 15 places.
Show 12 more
- skills/scarf-single-cell/references/clustering-and-embedding.md, line 201
`run.cells.columns` lists the frozen fields. `fetch(...)` returns run cells only. `fetch_all(...)` - skills/scarf-single-cell/references/data-access.md, line 21
- `I` is the live Boolean cell key (feature tables have one too). `fetch(col)` returns rows where - skills/scarf-single-cell/references/data-access.md, line 69
active_ids = ds.cells.fetch("ids") # rows where I is True - skills/scarf-single-cell/references/integration-and-comparisons.md, line 118
print(pd.crosstab(r.cells.fetch("clusters"), r.cells.fetch("condition"))) - skills/scarf-single-cell/references/markers-and-annotation.md, line 125
cluster_values = run.cells.fetch("clusters").astype(str) - skills/scarf-single-cell/references/markers-and-annotation.md, line 160
print(scores.groupby(run.cells.fetch("clusters")).mean().round(2)) - skills/scarf-single-cell/references/performance-and-export.md, line 262
- After an export: reload, compare shape to `run.cells.fetch("ids").shape` and feature count, - skills/scarf-single-cell/references/pipeline-runs-and-artifacts.md, line 87
clusters = run.cells.fetch("clusters") # int labels starting at 1 - skills/scarf-single-cell/references/pipeline-runs-and-artifacts.md, line 261
- `run.cells.fetch("clusters")` is integer, while `get_markers(...)["group_id"]` is a string. - skills/scarf-single-cell/references/quality-control.md, line 127
clusters = np.asarray(scaffold.cells.fetch("clusters")) - skills/scarf-single-cell/references/quality-control.md, line 144
counts = counts.reindex(scaffold.cells.fetch("ids")) - skills/scarf-single-cell/references/quality-control.md, line 234
scores = pd.Series(np.asarray(scaffold.cells.fetch("doublet_score")))
Other copies in this repository
sickn33/agentic-awesome-skills has 2 more folders with a skill named scarf-single-cell. This page shows the lowest-scoring copy.
Install
Paste this into your terminal. It downloads exactly the version we scanned, not whatever is newest.
if [ -e ~/.agents/skills/'scarf-single-cell' ] || [ -L ~/.agents/skills/'scarf-single-cell' ]; then echo 'A skill folder named scarf-single-cell already exists — not replacing it' >&2; false; else d=$(mktemp -d) && git clone --quiet --filter=blob:none --no-checkout 'https://github.com/sickn33/agentic-awesome-skills' "$d" && git -C "$d" checkout --quiet '00f9c4077878be6ffcc093c099f05a6cccaf1b95' -- 'skills/scarf-single-cell' && mkdir -p ~/.agents/skills && cp -R "$d"/'skills/scarf-single-cell' ~/.agents/skills/'scarf-single-cell' && rm -rf "$d"; fi
Codex reads ~/.agents/skills directly. For Claude Code, Cursor, Copilot, Gemini CLI, Antigravity or OpenCode, also link it. For Claude Code: ln -s ~/.agents/skills/'scarf-single-cell' ~/.claude/skills/'scarf-single-cell' (other agents: ~/.cursor/skills, ~/.copilot/skills, ~/.gemini/skills, ~/.gemini/config/skills, ~/.config/opencode/skills). Or install through the finder skill, which does both.
Badge for your README
The badge for sickn33/agentic-awesome-skills shows the score of its lowest-scoring skill.
[](https://zalavijaysinh.github.io/skilltrust/repos/sickn33/agentic-awesome-skills/)
Score history
| Date | Version | Score |
|---|
| 2026-11-11 | 00f9c40 | 82 |
| 2026-11-08 | 828257c | 82 |
| 2026-11-07 | 828257c | 82 |