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sc-pseudotime

sc-pseudotime is a skill for Claude Code, Codex from TianGzlab/OmicsClaw. It costs 72 tokens per session (2,019 once invoked), scanned A, original, Apache-2.0.

A tool that orders cells along a possible biological trajectory using a value called pseudotime. Pseudotime is an inferred progression, such as cells developing from an early state to a later one, not a clock measurement.

In plain words
What is it for?
Building trajectory orderings, lineage curves, and fate-probability summaries from clustered, normalised single-cell data.
Why use it?
It helps study gradual state changes and branching lineages when cells were measured at one point rather than followed over time.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

not rated 160repo +1 1mo ago A scan SkillSpector: warn 72 tokens original Apache-2.0

Good fit Building trajectory orderings, lineage curves, and fate-probability summaries from clustered, normalised single-cell data.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/tiangzlab/omicsclaw/sc-pseudotime
View source ↗ TianGzlab/OmicsClaw tiangzlab.github.io
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add TianGzlab/OmicsClaw --skill sc-pseudotime
Clone the repo
git clone --depth 1 https://github.com/TianGzlab/OmicsClaw

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for sc-pseudotime

README.md
[![agentmods](https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/sc-pseudotime.svg)](https://agentmods.dev/skills/tiangzlab/omicsclaw/sc-pseudotime)
Your own site
<a href="https://agentmods.dev/skills/tiangzlab/omicsclaw/sc-pseudotime"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/sc-pseudotime.svg" alt="Measured on agentmods" height="20"></a>
Per session 72 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,019 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 1 finding, up to high

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • high Rogue Agent · line 3
    Skill modifies its own code, configuration, or behavior at runtime. Self-modification enables an agent to escalate privileges, disable safety constraints, or install persistent backdoors.
    Fix: Prevent the skill from modifying its own code, SKILL.md, or configuration files. Treat skill files as read-only at runtime.
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00072 $0.02019
Opus 5 $0.00036 $0.01009
Sonnet 5 $0.00014 $0.00404
Haiku 4.5 $0.00007 $0.00202

Measured 4d ago against content hash e2aeb7010fa4, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

Grade A, and why

sc-pseudotime scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.

The scan reads SKILL.md. This mod also ships 2 executable files (sc_pseudotime.py, tests/test_sc_pseudotime_methods.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/singlecell/scrna/sc-pseudotime/SKILL.md · 148 lines

How it starts

The opening of the file, as written. The whole thing — 148 lines — stays where its author put it; the contents beside it link to each section on GitHub.

sc-pseudotime

When to use

The user has a clustered, normalised scRNA AnnData and wants a trajectory / pseudotime ordering across the cells. Six methods:

  • dpt (default) — diffusion pseudotime (Scanpy native).
  • palantir — Palantir waypoint-based pseudotime + fate probabilities.
  • via — VIA, scalable lineage with branching.
  • cellrank — CellRank macrostates + fate probabilities (optionally velocity-coupled with --cellrank-use-velocity).
  • slingshot_r — R-backed Slingshot lineage curves.
  • monocle3_r — R-backed Monocle3 trajectory graph.

Required: a normalised AnnData with a cluster column (leiden by default) and a low-D representation (obsm["X_pca"] / X_harmony / etc.). For per-cluster marker ranking use sc-markers; for velocity vector fields (kinetics, not ordering) use sc-velocity.

Inputs & Outputs

Inputs

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

Outputs

  • tables/cell_metadata.csv
  • tables/fate_probabilities.csv
  • tables/gene_expression.csv
  • tables/monocle3_pseudotime.csv
  • tables/monocle3_trajectory.csv
  • tables/pseudotime_cells.csv
  • tables/pseudotime_points.csv
  • tables/slingshot_branches.csv
  • tables/slingshot_curves.csv
  • tables/slingshot_pseudotime.csv
  • tables/trajectory_genes.csv
  • tables/trajectory_summary.csv
  • figures/monocle3_trajectory_graph.png
  • figures/r_cell_density.png
  • figures/r_embedding_discrete.png
  • figures/r_embedding_feature.png
  • figures/r_pseudotime_dynamic.png
  • figures/r_pseudotime_heatmap.png
  • figures/r_pseudotime_lineage.png
  • analysis_summary.txt
  • input.h5ad
  • processed.h5ad
  • report.md
  • result.json
  • Processed AnnData (saves_h5ad) — adds obs: pseudotime; obsm: trajectory_fate_probabilities

Flow

  1. Load AnnData (--input) or auto-build a demo with the largest cluster as the root.
  2. Validate cluster_key exists; require X = normalized_expression.
  3. Resolve representation (--use-rep) — auto-pick from obsm if unset.
  4. Resolve root cell from --root-cluster or --root-cell (integer index or obs_name).
  5. Dispatch to the method-specific runner; the R-backed methods exec via RScriptRunner against the bundled R scripts.
  6. Build trajectory-gene correlations (--n-genes, --corr-method).
  7. Save processed.h5ad, tables, figures, report.md, result.json (incl. backend, n_clusters, n_trajectory_genes).

Read the full file on GitHub · 148 lines

Files

What ships with it

7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 4d ago First seen · 148 lines · 72 tokens per session scan A e2aeb7010fa4

Subscribe to this mod's changes

sc-pseudotime is a skill published in the GitHub repository TianGzlab/OmicsClaw (160 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 72 tokens to every session and 2,019 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

CellRankDPTPalantirScientificbioinformaticsknowledge-graphmonocle3pseudotimescrnasinglecellslingshottrajectoryvia
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