Spatial Statistics

What it does

When your agent uses it

• Tasks that involve Statistics

Example prompts

• “/spatial-statistics”

Requirements

• Python 3

Workflow steps

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

1. 1Load AnnData (--input) or chain through spatial-preprocess --demo via subprocess (spatial_statistics.py:1499-1515).
2. 2parser.error validates --analysis-type ∈ VALID_ANALYSIS_TYPES; per-analysis numeric ranges (lines :1558-1601).
3. 3Auto-resolve --cluster-key if unset; auto-leiden if no cluster column exists (with size guard at :1546).
4. 4Dispatch to analysis-type runner; squidpy graph build uses --stats-n-neighs / --stats-n-rings / --stats-n-perms.
5. 5Build standardised result tables; collect per-spot metrics.
6. 6Save tables, figures, processed.h5ad, report.md, result.json.

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 and R), 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

Spatial Statistics loads about 2.1k tokens when it runs, and up to ~7k if it reads all its reference files. Until then it costs about 71 tokens; SKILL.md has 485 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~71

When it runs · the whole SKILL.md, loaded when a task matches

~2.1k

With references · SKILL.md plus every file in references/, read only if the agent opens them

~7k

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

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). 485 words, ~2,054 tokens.

name

spatial-statistics

description

Load when running spatial autocorrelation / hotspot / co-occurrence / neighbourhood-enrichment / Ripley K stats on a clustered spatial AnnData via squidpy. Skip when ranking spatially variable genes (use spatial-genes); tissue domain detection (use spatial-domains).

version

0.5.0

author

OmicsClaw

license

MIT

emoji

📊

tags

spatial, statistics, moran, geary, ripley, co-occurrence, nhood-enrichment, getis-ord, squidpy

requires

anndata, esda, libpysal, matplotlib, networkx, numpy, pandas, scanpy, scipy, seaborn, squidpy

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

# Demo (chained from spatial-preprocess --demo)
python omicsclaw.py run spatial-statistics --demo --analysis-type moran --output /tmp/spatial_stats_demo

# Global Moran's I on a clustered AnnData
python omicsclaw.py run spatial-statistics \
  --input clustered.h5ad --output results/ \
  --analysis-type moran --cluster-key spatial_domain --stats-n-perms 100

# Local Moran with GeoDa quadrants
python omicsclaw.py run spatial-statistics \
  --input clustered.h5ad --output results/ \
  --analysis-type local_moran --local-moran-geoda-quads --n-top-genes 20

# Neighbourhood enrichment between clusters
python omicsclaw.py run spatial-statistics \
  --input clustered.h5ad --output results/ \
  --analysis-type neighborhood_enrichment --cluster-key spatial_domain

# Ripley K on a labelled object
python omicsclaw.py run spatial-statistics \
  --input clustered.h5ad --output results/ \
  --analysis-type ripley --ripley-mode K --ripley-n-simulations 100 --ripley-n-steps 50

# Co-occurrence at increasing distance bins
python omicsclaw.py run spatial-statistics \
  --input clustered.h5ad --output results/ \
  --analysis-type co_occurrence --cluster-key cell_type --coocc-interval 30 --coocc-n-splits 5

# Bivariate Moran between two genes
python omicsclaw.py run spatial-statistics \
  --input clustered.h5ad --output results/ \
  --analysis-type bivariate_moran --genes EGFR,BRCA1

© 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 9 other files (references) in skills/spatial/spatial-statistics of TianGzlab/OmicsClaw.

• SKILL.md
• r_visualization/README.md
• r_visualization/stats_publication_template.R
• references/methodology.md
• references/output_contract.md
• references/parameters.md
• skill.yaml
• spatial_statistics.py
• tests/__init__.py
• tests/test_spatial_statistics.py

Open the folder on GitHubat commit 6fbd79f