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Agent skill

Sc Consensus Integration

by TianGzlab in TianGzlab/OmicsClaw

Load when you want a multi-sample single-cell (scRNA) clustering robust to the choice of integration method — fanning out Harmony/Scanorama/scVI + an unintegrated baseline, scoring each by a…

MITAuto-check passedResearch & Science

Install Sc Consensus Integration

skills CLI
$ npx skills add TianGzlab/OmicsClaw --skill sc-consensus-integration -a claude-code

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

GitHub CLI
$ gh skill install TianGzlab/OmicsClaw sc-consensus-integration --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/TianGzlab/OmicsClaw.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/singlecell/scrna/sc-consensus-integration .claude/skills/sc-consensus-integration && 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
sc-consensus-integration
GitHub stars
161
Token cost
~1.5k tokens
SKILL.md length
510 words
Files
6 (incl. references)
Skills in repo
95
Repo updated
First seen
Licence
MIT

At a glance

Load when you want a multi-sample single-cell (scRNA) clustering robust to the choice of integration method — fanning out Harmony/Scanorama/scVI + an unintegrated baseline, scoring each by a…

  • Works in 5 steps: Plan members — the --integration-methods… → Fan out — run sc-integrate-cluster… → Score — the driver computes the… → …
  • Tasks that involve Bioinformatics
  • SKILL.md covers When to use, Inputs & Outputs, Flow and Gotchas, plus 2 more sections
  • Runs Python scripts from its folder; calls python and pip

What it does

Sc Consensus Integration is an agent skill from TianGzlab/OmicsClaw. Load when you want a multi-sample single-cell (scRNA) clustering robust to the choice of integration method — fanning out Harmony/Scanorama/scVI + an unintegrated baseline, scoring each by a batch-mixing intrinsic panel, and voting a consensus. Skip when single-batch (use sc-consensus-clustering); one integration method is fixed.

Its SKILL.md is about 1.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including reference files (for example `references/methodology.md`, `references/output_contract.md` and `references/parameters.md`).

It sits in Research & Science, covering Bioinformatics. The repository describes itself as: Conversational & memory-enabled AI research partner for multi-omics analysis. CLI + Desktop App (installers in Releases). From biological idea to full research paper. The licence is MIT.

When your agent uses it

  • Tasks that involve Bioinformatics

Example prompts

  • “/sc-consensus-integration”

Requirements

  • Python 3

Workflow steps

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

  1. Plan members — the --integration-methods set (none baseline + harmony
  2. Fan out — run sc-integrate-cluster --method per member at the fixed
  3. Score — the driver computes the batch-mixing intrinsic panel (ADR 0029,
  4. Consensus — vote kmode / weighted / lca over the voting members
  5. Report — banner + per-cell support/entropy + a k-divergence section.

What it can do on your machine

Read from SKILL.md and the folder at commit 6fbd79f. 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 script files (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python
    • pip

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use pip, which can reach the network depending on how they are called.

    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

Sc Consensus Integration loads about 1.5k tokens when it runs, and up to ~3k if it reads all its reference files. Until then it costs about 89 tokens; SKILL.md has 510 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~89
When it runs · the whole SKILL.md, loaded when a task matches
~1.5k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~3k

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); files beside SKILL.md are not scanned.

SKILL.md

The full file from TianGzlab/OmicsClaw at commit 6fbd79f, republished under its MIT licence (© TianGzlab). 510 words, ~1,462 tokens.

Download SKILL.mdSave it as .claude/skills/sc-consensus-integration/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.
name
sc-consensus-integration
description
Load when you want a multi-sample single-cell (scRNA) clustering robust to the choice of integration method — fanning out Harmony/Scanorama/scVI + an unintegrated baseline, scoring each by a batch-mixing intrinsic panel, and voting a consensus. Skip when single-batch (use sc-consensus-clustering); one integration method is fixed.
version
0.1.0
author
OmicsClaw
license
MIT
emoji
🧬
tags
singlecell, scrna, consensus, integration, batch-correction
requires
anndata, numpy, pandas, PyYAML, scanpy, scikit-learn, scipy

sc-consensus-integration

When to use

Verified consensus over batch-correction representations. For multi-sample single-cell data the dominant axis of variation is not clustering resolution but how batch effect is removed: clustering uncorrected PCA of multi-sample data clusters batches, not cell types, and different integration methods (Harmony / Scanorama / scVI / …) yield different embeddings and so different clusterings. Use this when you have a preprocessed multi-sample AnnData with a batch key in obs (≥2 batches) and want a clustering that is not an artifact of one integration method, with per-cell confidence and batch-artifact flags.

It mirrors consensus-domains: members fan out sc-integrate-cluster --method <m> — each a self-contained integrate + cluster unit — at a fixed resolution (so member cluster counts stay comparable for the operator), scored by the integration intrinsic panel (ADR 0029) before voting a consensus.

Inputs & Outputs

<!-- AUTO-GENERATED from skill.yaml (interface) — do not edit by hand. Regenerate: python scripts/generate_skill_md.py <skill_dir> -->

Inputs

  • Modalities: scrna
  • File types: .h5ad
  • Requires a preprocessed AnnData (X normalised, PCA/neighbours present)
  • Expects obsm: X_pca

Outputs

  • consensus_labels.tsv
  • member_scores.csv
  • member_intrinsic_panel.csv
  • cross_method_nmi.csv
  • plan.json
  • report.md
  • result.json

Flow

  1. Plan members — the --integration-methods set (none baseline + harmony
    • scanorama by default; scvi via --include-scvi).
  2. Fan out — run sc-integrate-cluster --method <m> per member at the fixed --resolution (member cluster counts stay comparable for the operator).
  3. Score — the driver computes the batch-mixing intrinsic panel (ADR 0029, recalibrated on panc8) on each member's embedding + batch key. The single scored axis is ilisi_norm (iLISI diversity, log(iLISI)/log(n_batches)) — the one metric validated to track ground-truth cell-type recovery. knn_preservation_norm (within-batch X_pca retention), batch_asw_norm and cluster_asw_norm are reported as weight-0 diagnostics (knn_preservation anti-correlated with recovery, so it flags over-integration but does not score).
  4. Consensus — vote kmode / weighted / lca over the voting members (the integration methods; the none baseline is excluded by default, B2).
  5. Report — banner + per-cell support/entropy + a k-divergence section.
Show full SKILL.md (226 more words)Show less

Gotchas

  • none is the unintegrated X_pca baseline, and it does NOT vote by default — it is a reference control that exposes batch-artifact clusters by comparison (it is scored, paneled and reported, with selection_reason = "baseline …"), but voting it as an equal drags the consensus toward un-integrated structure (ADR 0029 B2). Pass --vote-baseline to include it in the vote.
  • scVI is GPU/stochastic and slow — reproducible within tolerance, not bit-identical; add it with --include-scvi (which raises the per-member --timeout to 1800s, since scVI is ~10-15 min on ~15k cells) and serialise GPU members with --max-parallel 1. For very large datasets raise --timeout further. (If scVI is not installed the member fails with an import error, not a timeout — pip install scvi-tools.)
  • The intrinsic panel is unsupervised batch-mixing-vs-structure — it is NOT validated against curated cell types; treat the score as a relative ranking and the panel weights in plan.json as experimental (ADR 0029), not calibrated.
  • Fixed --resolution is intentional — members must produce comparable cluster counts for the operator; do not sweep resolution here (use sc-consensus-clustering for the resolution-robustness question).

Key CLI

bash
# default members: unintegrated (X_pca baseline) + harmony + scanorama
python omicsclaw.py run sc-consensus-integration \
  --input <preprocessed.h5ad> --output <dir> \
  --batch-key batch --resolution 1.0 --operator kmode --seed 0 --non-interactive

# add the GPU/stochastic scVI member (serialise GPU members)
python omicsclaw.py run sc-consensus-integration --input <h5ad> --output <dir> \
  --include-scvi --max-parallel 1 --non-interactive

# explicit method set
python omicsclaw.py run sc-consensus-integration --input <h5ad> --output <dir> \
  --integration-methods harmony,scanorama,scvi --non-interactive

See also

  • references/methodology.md — integration-consensus + intrinsic-panel rationale
  • references/output_contract.md — consensus_labels.tsv / member_scores.csv / plan.json schema
  • references/parameters.md — every CLI flag (generated from skill.yaml)
  • Adjacent skills: sc-preprocessing (upstream), sc-integrate-cluster (the per-member integrate+cluster unit this wraps), sc-consensus-clustering (parallel — resolution-robustness instead of integration-robustness), consensus-domains (parallel — the spatial analogue)
  • ADR 0011/0016/0029 — scoring protocol, workflow runtime, integration intrinsic panel

© TianGzlab, 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 5 other files (references) in skills/singlecell/scrna/sc-consensus-integration of TianGzlab/OmicsClaw.

  • SKILL.md
  • references/methodology.md
  • references/output_contract.md
  • references/parameters.md
  • sc_consensus_integration.py
  • skill.yaml

Open the folder on GitHubat commit 6fbd79f

Compare with similar skills

Sc Consensus Integration 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.

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Clinvar Databasegoogle-deepmind/science-skills3.2k2 repos~3.9kAutomated safety check: NotesApache-2.0
Metabolic Study Planneraiming-lab/AutoResearchClaw15k—~1.9kAutomated safety check: PassMIT
Dbsnp Databasegoogle-deepmind/science-skills3.2k2 repos~3.4kAutomated safety check: NotesApache-2.0

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Categories

Questions about Sc Consensus Integration

What does Sc Consensus Integration do?

Load when you want a multi-sample single-cell (scRNA) clustering robust to the choice of integration method — fanning out Harmony/Scanorama/scVI + an unintegrated baseline, scoring each by a…. Sc Consensus Integration is an agent skill from TianGzlab/OmicsClaw. Load when you want a multi-sample single-cell (scRNA) clustering robust to the choice of integration method — fanning out Harmony/Scanorama/scVI + an unintegrated baseline, scoring each by a batch-mixing intrinsic panel, and voting a consensus.

When should I use Sc Consensus Integration?

Sc Consensus Integration fits situations like: tasks that involve Bioinformatics.

How do I install Sc Consensus Integration in Claude Code?

Run `npx skills add TianGzlab/OmicsClaw --skill sc-consensus-integration -a claude-code`. Or copy the skill folder (skills/singlecell/scrna/sc-consensus-integration in TianGzlab/OmicsClaw) into .claude/skills/sc-consensus-integration in your project. Claude Code loads it when a task matches its description.

How do I install Sc Consensus Integration in Codex?

Run `npx skills add TianGzlab/OmicsClaw --skill sc-consensus-integration -a codex`. Or copy the skill folder (skills/singlecell/scrna/sc-consensus-integration in TianGzlab/OmicsClaw) into .agents/skills/sc-consensus-integration in your project. Codex loads it when a task matches its description.

Can I use Sc Consensus Integration 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 TianGzlab/OmicsClaw --skill sc-consensus-integration -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/sc-consensus-integration, .gemini/skills/sc-consensus-integration, .github/skills/sc-consensus-integration and .opencode/skills/sc-consensus-integration in your project.

What does Sc Consensus Integration need to run?

Going by SKILL.md and its folder, Sc Consensus Integration needs Python for the scripts in its folder and the command-line tools its instructions call (python and pip). Our summary lists: Python 3.

Does Sc Consensus Integration access the network?

SKILL.md contains no URLs. Its commands use pip, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Sc Consensus Integration 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. Review the folder before installing.

What licence does Sc Consensus Integration use?

Sc Consensus Integration 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 Sc Consensus Integration use?

About 1.5k tokens (SKILL.md is roughly 5.8k 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 1.5k tokens, read only when the agent opens those files.

What are the alternatives to Sc Consensus Integration?

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

Who maintains Sc Consensus Integration?

TianGzlab (a GitHub organization) maintains it in TianGzlab/OmicsClaw, which has 161 GitHub stars. The repository holds 95 skills in this directory. The repository was last updated on July 28, 2026.

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