Agent skill

Sc Standardize Input

Load when an external single-cell h5ad/h5/loom/mtx needs to be canonicalised onto the OmicsClaw AnnData contract before downstream scRNA skills run.

Apache-2.0Auto-check passedResearch & Science

Install Sc Standardize Input

skills CLI

$ npx skills add TianGzlab/OmicsClaw --skill sc-standardize-input -a claude-code

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

GitHub CLI

$ gh skill install TianGzlab/OmicsClaw sc-standardize-input --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-standardize-input .claude/skills/sc-standardize-input && 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/

Paste into Claude Code

Install the "sc-standardize-input" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/singlecell/scrna/sc-standardize-input into .claude/skills/sc-standardize-input/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sc-standardize-input", 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

$skill-installer install https://github.com/TianGzlab/OmicsClaw/tree/main/skills/singlecell/scrna/sc-standardize-input

Type 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.

skills CLI

$ npx skills add TianGzlab/OmicsClaw --skill sc-standardize-input -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install TianGzlab/OmicsClaw sc-standardize-input --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/TianGzlab/OmicsClaw.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/singlecell/scrna/sc-standardize-input .agents/skills/sc-standardize-input && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "sc-standardize-input" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/singlecell/scrna/sc-standardize-input into .agents/skills/sc-standardize-input/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sc-standardize-input", 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.

skills CLI

$ npx skills add TianGzlab/OmicsClaw --skill sc-standardize-input -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install TianGzlab/OmicsClaw sc-standardize-input --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/TianGzlab/OmicsClaw.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/singlecell/scrna/sc-standardize-input .cursor/skills/sc-standardize-input && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "sc-standardize-input" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/singlecell/scrna/sc-standardize-input into .cursor/skills/sc-standardize-input/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sc-standardize-input", 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

$ gemini skills install https://github.com/TianGzlab/OmicsClaw.git --path skills/singlecell/scrna/sc-standardize-input

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add TianGzlab/OmicsClaw --skill sc-standardize-input -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install TianGzlab/OmicsClaw sc-standardize-input --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/TianGzlab/OmicsClaw.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/singlecell/scrna/sc-standardize-input .gemini/skills/sc-standardize-input && rm -rf skills-src

Use ~/.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/

Paste into Gemini CLI

Install the "sc-standardize-input" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/singlecell/scrna/sc-standardize-input into .gemini/skills/sc-standardize-input/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sc-standardize-input", 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.

GitHub CLI

$ gh skill install TianGzlab/OmicsClaw sc-standardize-input

Installs 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).

skills CLI

$ npx skills add TianGzlab/OmicsClaw --skill sc-standardize-input -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/TianGzlab/OmicsClaw.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/singlecell/scrna/sc-standardize-input .github/skills/sc-standardize-input && rm -rf skills-src

Use ~/.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/

Paste into GitHub Copilot

Install the "sc-standardize-input" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/singlecell/scrna/sc-standardize-input into .github/skills/sc-standardize-input/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sc-standardize-input", 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.

skills CLI

$ npx skills add TianGzlab/OmicsClaw --skill sc-standardize-input -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install TianGzlab/OmicsClaw sc-standardize-input --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/TianGzlab/OmicsClaw.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/singlecell/scrna/sc-standardize-input .opencode/skills/sc-standardize-input && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "sc-standardize-input" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/singlecell/scrna/sc-standardize-input into .opencode/skills/sc-standardize-input/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sc-standardize-input", 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.

Facts

Skill name: sc-standardize-input
GitHub stars: 161
Token cost: ~1.4k tokens
SKILL.md length: 538 words
Files: 9 (incl. references)
Skills in repo: 88
Repo updated: Oct 7, 2026
First seen: Oct 7, 2026
Licence: Apache-2.0

At a glance

Load when an external single-cell h5ad/h5/loom/mtx needs to be canonicalised onto the OmicsClaw AnnData contract before downstream scRNA skills run.

Works in 6 steps: Load via the shared multi-format… → Pre-flight: validate non-empty input;… → Pick the best count-like matrix among… → …
Tasks that involve Bioinformatics
SKILL.md covers When to use, Use from a step, API and Methods and parameters, plus 6 more sections
Runs Python scripts from its folder; calls python

What it does

Sc Standardize Input is an agent skill from TianGzlab/OmicsClaw. Load when an external single-cell h5ad/h5/loom/mtx needs to be canonicalised onto the OmicsClaw AnnData contract before downstream scRNA skills run. Skip when data already came from sc-count (already canonical); bulk RNA-seq (use bulkrna-qc); spatial (use spatial-preprocess).

Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 11 other files, including reference files (for example `_api.py`, `examples/example_step.py` and `references/methodology.md`).

It sits in Research & Science, covering Bioinformatics. It works with AnnData. 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 Apache-2.0.

When your agent uses it

Tasks that involve Bioinformatics

Example prompts

“/sc-standardize-input”

Requirements

Python 3

Workflow steps

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

Load via the shared multi-format single-cell loader.
Pre-flight: validate non-empty input; auto-detect species from gene name case (UPPER → human, Title → mouse).
Pick the best count-like matrix among layers["counts"], adata.raw, and adata.X (orchestrated by canonicalize_singlecell_adata in…
Harmonise feature names (Ensembl ↔ symbol, deduplicate).
Persist uns["omicsclaw_input_contract"] + uns["omicsclaw_matrix_contract"].
Save processed.h5ad; emit report.md + result.json.

What it can do on your machine

Read from SKILL.md and the folder at commit 90a3bec. 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
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

Sc Standardize Input loads about 1.4k tokens when it runs, and up to ~2.5k if it reads all its reference files. Until then it costs about 74 tokens; SKILL.md has 538 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~74
When it runs · the whole SKILL.md, loaded when a task matches: ~1.4k
With references · SKILL.md plus every file in references/, read only if the agent opens them: ~2.5k

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 90a3bec, republished under its Apache-2.0 licence (© TianGzlab). 538 words, ~1,409 tokens.

Download SKILL.mdSave it as .claude/skills/sc-standardize-input/SKILL.md (or your agent's skills folder). This skill also uses 8 other files; get the full folder from GitHub.

name: sc-standardize-input
description: Load when an external single-cell h5ad/h5/loom/mtx needs to be canonicalised onto the OmicsClaw AnnData contract before downstream scRNA skills run. Skip when data already came from sc-count (already canonical); bulk RNA-seq (use bulkrna-qc); spatial (use spatial-preprocess).
trigger: standardize AnnData, fix scRNA input, canonicalize single-cell input, prepare AnnData, input contract
tags: singlecell, scrna, input, standardization, anndata

sc-standardize-input

When to use

The user has a single-cell expression file from outside OmicsClaw (a public .h5ad, a 10X mtx directory, a .loom, etc.) and needs the canonical AnnData contract every downstream scRNA skill assumes: raw counts in layers["counts"] and adata.raw, harmonised feature names, and a uns["omicsclaw_matrix_contract"] provenance record. Run this once before sc-qc / sc-preprocessing / etc.

Use from a step

python

standardizer = load_skill("sc-standardize-input")
adata = standardizer.standardize(read_input("external.h5ad"), species="human")
write_output(adata, "intermediate/adata_standardized.h5ad")

standardize returns a new AnnData. It does not filter, normalize or cluster. A runnable PBMC example is in examples/example_step.py.

API

<!-- api:begin generated from _api.py; regenerate with run.py api <skill dir> --write -->

`standardize(adata, *, species: str='auto')`

Return a canonical copy with count-like X, layers['counts'] and raw.

Select counts from layers['counts'], aligned raw or X, in that order; harmonize feature names and record the matrix and input contracts.

:param adata: AnnData with at least one count-like expression matrix. :param species: 'auto' uses infer_species; 'human' or 'mouse' overrides it. :returns: A new AnnData. run_info reports the selected matrix and warnings. :raises ValueError: The species is invalid or no count-like matrix is present.

`infer_species(adata) -> str`

Infer human or mouse from feature names, defaulting to human.

:param adata: AnnData whose feature names or gene-symbol metadata are examined. :returns: The species hint used by standardize(species="auto").

`run_info(adata, *, keep: bool=True) -> dict`

Read standardization diagnostics stored as JSON in adata.uns.

:param adata: The AnnData returned by standardize. :param keep: False removes these diagnostics after reading; default True. :returns: A dictionary, or an empty dictionary before standardize runs.

<!-- api:end -->

Methods and parameters

species="auto" uses the existing gene-name heuristic; use human or mouse when the organism is known. infer_species exposes that heuristic. Counts are selected from layers["counts"], aligned raw, then X. run_info reports the chosen source and any warnings. This method has no random state or optional backend.

Inputs & Outputs

Inputs

Input kinds: file, directory
Modalities: scrna
File types: .h5ad, .h5, .loom, .csv, .tsv

Outputs

processed.h5ad
report.md
result.json
Processed AnnData (saves_h5ad) — adds layers: counts

Show full SKILL.md (238 more words)Show less

Flow

Load via the shared multi-format single-cell loader.
Pre-flight: validate non-empty input; auto-detect species from gene name case (UPPER → human, Title → mouse).
Pick the best count-like matrix among layers["counts"], adata.raw, and adata.X (orchestrated by canonicalize_singlecell_adata in skills/singlecell/_lib/adata_utils.py, which calls the matrix_looks_count_like heuristic at _lib/adata_utils.py).
Harmonise feature names (Ensembl ↔ symbol, deduplicate).
Persist uns["omicsclaw_input_contract"] + uns["omicsclaw_matrix_contract"].
Save processed.h5ad; emit report.md + result.json.

Gotchas

--r-enhanced is accepted but produces no R plots. sc_standardize_input.py declares the flag for CLI consistency; this skill is input canonicalisation, not visualisation. Pass it freely, but expect no R Enhanced figures.
Count-source selection uses a count-like heuristic, not file provenance. Check run_info(adata)["expression_source"] and warnings, or the CLI's result.json["summary"], before downstream normalization.
Species auto-detect is gene-case-based. UPPER-case symbols → human, Title-case → mouse. Non-standard gene-name conventions (Ensembl IDs only, lowercase) silently fall through to the auto default. Pass --species human or --species mouse explicitly when working with non-symbol matrices.
No filtering, no normalisation, no clustering. Even if result.json looks complete, the output is still raw counts in canonical form — run sc-qc and sc-preprocessing next.

Key CLI

bash

# Demo (built-in PBMC3K)
python skills/singlecell/scrna/sc-standardize-input/sc_standardize_input.py --demo --output /tmp/sc_std_demo

# Real run with species hint
python skills/singlecell/scrna/sc-standardize-input/sc_standardize_input.py \
  --input external.h5ad --output results/ --species mouse

Dependencies

Python packages this skill's script needs. They are not installed for you — check before a long run.

anndata, matplotlib, numpy, pandas, scanpy, scipy

Files

SKILL.md and 8 other files (references) in skills/singlecell/scrna/sc-standardize-input of TianGzlab/OmicsClaw.

SKILL.md
_api.py
examples/example_step.py
references/methodology.md
references/output_contract.md
references/parameters.md
references/r_visualization.md
sc_standardize_input.py
tests/test_standardize_api.py

Open the folder on GitHubat commit 90a3bec

Compare with similar skills

Sc Standardize Input 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.

Sc Standardize Input compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Sc Standardize Input this skillTianGzlab/OmicsClaw	161	—	~1.4k	Automated safety check: Pass	Apache-2.0	yesterday
Scanpy Single-Cell Analysisdavila7/claude-code-templates	32k	16 repos	~2.8k	Automated safety check: Pass	MIT	today
ScgptJimLiu/science-skills	227	4 repos	~1.3k	Automated safety check: Pass	Apache-2.0	3 mo ago
PyDESeq2 Differential Expressiondavila7/claude-code-templates	32k	12 repos	~4k	Automated safety check: Pass	MIT	today
Anndatadavila7/claude-code-templates	32k	12 repos	~2.5k	Automated safety check: Pass	MIT	today
Single-Cell Initial AnalysisLigphiDonk/Oh-my--paper	738	1 repos	~1.4k	Automated safety check: Pass	MIT	5 mo ago

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Questions about Sc Standardize Input

What does Sc Standardize Input do?

Load when an external single-cell h5ad/h5/loom/mtx needs to be canonicalised onto the OmicsClaw AnnData contract before downstream scRNA skills run. Sc Standardize Input is an agent skill from TianGzlab/OmicsClaw. Load when an external single-cell h5ad/h5/loom/mtx needs to be canonicalised onto the OmicsClaw AnnData contract before downstream scRNA skills run.

When should I use Sc Standardize Input?

Sc Standardize Input fits situations like: tasks that involve Bioinformatics.

How do I install Sc Standardize Input in Claude Code?

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

How do I install Sc Standardize Input in Codex?

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

Can I use Sc Standardize Input 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-standardize-input -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-standardize-input, .gemini/skills/sc-standardize-input, .github/skills/sc-standardize-input and .opencode/skills/sc-standardize-input in your project.

What does Sc Standardize Input need to run?

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

Does Sc Standardize Input 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 Sc Standardize Input 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 Standardize Input use?

Sc Standardize Input is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Sc Standardize Input use?

About 1.4k tokens (SKILL.md is roughly 5.6k 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 1.1k tokens, read only when the agent opens those files.

What are the alternatives to Sc Standardize Input?

Skills that share tags, products or a category with Sc Standardize Input: Scanpy Single-Cell Analysis (davila7/claude-code-templates, 32k stars), Scgpt (JimLiu/science-skills, 227 stars), PyDESeq2 Differential Expression (davila7/claude-code-templates, 32k stars) and Anndata (davila7/claude-code-templates, 32k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Sc Standardize Input?

TianGzlab (a GitHub organization) maintains it in TianGzlab/OmicsClaw, which has 161 GitHub stars. The repository holds 88 skills in this directory. The repository was last updated on October 7, 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.

Install Sc Standardize Input

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

sc-standardize-input

When to use

Use from a step

API

standardize(adata, *, species: str='auto')

infer_species(adata) -> str

run_info(adata, *, keep: bool=True) -> dict

Methods and parameters

Inputs & Outputs

Flow

Gotchas

Key CLI

See also

Dependencies

Files

Compare with similar skills

Similar skills

More from TianGzlab/OmicsClaw

Related

Questions about Sc Standardize Input

`standardize(adata, *, species: str='auto')`

`infer_species(adata) -> str`

`run_info(adata, *, keep: bool=True) -> dict`