Repository
QING1105/ezST agent skills
- skills
- 11
- GitHub stars
- 101
GitHub description: “10x Visium spatial transcriptomics analysis skills for Codex — staged workflow with human review gates and LLM biological interpretation.”
- Stars
- 101 (1 forks)
- Licence
- No licence
- Last push
- Aug 2026
- Created
- Aug 2026
- Source
- github.com/QING1105/ezST
Install all skills
npx skills add QING1105/ezSTAdd --skill <name> for a single skill and -a <agent> to choose the agent (see the agent guides).
Skills in QING1105/ezST, ranked
Ranked by score. Sort bymost stars,trending,newest,recently updated
| # | Skill | Repository | Stars | Used in | Tokens | Auto-check | Licence | Updated |
|---|---|---|---|---|---|---|---|---|
| 1 | End-to-end 10x Visium spatial transcriptomics analysis workflow with staged execution and human review gates. | QING1105/ | 101 | — | ~1.4k | Automated safety check: Pass | MIT | 1 mo ago |
| 2 | Visium HD platform branch of the spatial transcriptomics workflow — reconstruct single cells from 2 μm bins via morphological segmentation and bin-to-cell aggregation. | QING1105/ | 101 | — | ~2.6k | Automated safety check: Pass | MIT | 1 mo ago |
| 3 | Atera platform branch of the spatial transcriptomics workflow — load and validate Atera cell-level output (AnnData + Zarr segmentation) for downstream analysis. | QING1105/ | 101 | — | ~576 | Automated safety check: Pass | MIT | 1 mo ago |
| 4 | Stage 3 of the spatial transcriptomics workflow — identify spatial domains and detect spatially variable genes. | QING1105/ | 101 | — | ~476 | Automated safety check: Pass | MIT | 1 mo ago |
| 5 | Stage 4 of the spatial transcriptomics workflow — deconvolve Visium spots into cell-type proportions. | QING1105/ | 101 | — | ~480 | Automated safety check: Pass | MIT | 1 mo ago |
| 6 | Stage 5 of the spatial transcriptomics workflow — neighborhood enrichment and cell-cell communication analysis. | QING1105/ | 101 | — | ~513 | Automated safety check: Pass | MIT | 1 mo ago |
| 7 | Classic Visium platform branch of the spatial transcriptomics workflow — spot-level 5-stage pipeline (load+QC, normalize+cluster, domains+SVG, deconvolution, downstream prep). | QING1105/ | 101 | — | ~750 | Automated safety check: Pass | MIT | 1 mo ago |
| 8 | Xenium platform branch of the spatial transcriptomics workflow — load and validate the platform's cell-level matrix for downstream analysis. | QING1105/ | 101 | — | ~535 | Automated safety check: Pass | MIT | 1 mo ago |
| 9 | Stage 1 of the spatial transcriptomics workflow — load 10x Visium data and QC-filter low-quality spots. | QING1105/ | 101 | — | ~467 | Automated safety check: Pass | MIT | 1 mo ago |
| 10 | Stage 2 of the spatial transcriptomics workflow — normalize 10x Visium data and cluster spatial spots. | QING1105/ | 101 | — | ~428 | Automated safety check: Pass | MIT | 1 mo ago |
| 11 | Shared downstream analysis for all spatial platforms (Visium, Visium HD, Xenium, Atera) — neighborhood enrichment, CellChat cell-cell communication, and RNA velocity. | QING1105/ | 101 | — | ~737 | Automated safety check: Pass | MIT | 1 mo ago |
Questions, answered from the data.
What is the best skill in QING1105/ezST?
Spatial Transcriptomics from QING1105/ezST ranks first of the 11 skills in QING1105/ezST listed here, with the highest score: its repository has 101 GitHub stars, its SKILL.md loads about 1.4k tokens and it passes the automated safety check with no findings. Next come Spatial Visium Hd and Spatial Atera.
Are the skills in QING1105/ezST official?
None yet. All 11 skills in QING1105/ezST listed here come from community repositories; a skill counts as official when the product's own GitHub organization publishes it.
How do I install all skills from QING1105/ezST?
Run npx skills add QING1105/ezST in your project: the open-source skills CLI installs the repository's skills into your coding agent's skills folder. To install a single skill, open its page here for the exact command.
How are these skills ranked?
By Skill Navigator score, which combines the GitHub stars of the skill's repository (shared across that repo's skills and discounted for large collections), how many other GitHub owners carry a copy of the skill, and automated SKILL.md quality checks, minus penalties for safety-check warnings and for each further skill from the same repository. Skills that fail the safety check are not listed.