stream-trajectory-data-formatting

stream-trajectory-data-formatting is a skill for Claude Code, Codex from HolobiomicsLab/asb-skill-collections. It costs 48 tokens per session (1,292 once invoked), scanned A, original, Apache-2.0.

A data-export procedure that converts an ArchR peak-by-cell matrix into a format accepted by STREAM. ArchR and STREAM are tools for analysing single-cell chromatin data, while trajectory analysis estimates possible changes between cell states.

In plain words
What is it for?
Export an annotated, peak-called ArchR dataset so STREAM can reconstruct and visualise cell trajectories, including branching paths.
Why use it?
It removes a format-compatibility step when using STREAM after ArchR processing. The export is focused on the peak matrix and does not preserve all ArchR embeddings or dimensionality-reduction results.

Skill for Claude CodeCodex

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

Good fit Export an annotated, peak-called ArchR dataset so STREAM can reconstruct and visualise cell trajectories, including branching paths.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/holobiomicslab/asb-skill-collections/stream-trajectory-data-formatting
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 HolobiomicsLab/asb-skill-collections --skill stream-trajectory-data-formatting
Clone the repo
git clone --depth 1 https://github.com/HolobiomicsLab/asb-skill-collections

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 stream-trajectory-data-formatting

README.md
[![agentmods](https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/stream-trajectory-data-formatting/github.svg)](https://agentmods.dev/skills/holobiomicslab/asb-skill-collections/stream-trajectory-data-formatting)
Your own site
<a href="https://agentmods.dev/skills/holobiomicslab/asb-skill-collections/stream-trajectory-data-formatting"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/stream-trajectory-data-formatting/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for stream-trajectory-data-formatting

Your own site · 80×15
<a href="https://agentmods.dev/skills/holobiomicslab/asb-skill-collections/stream-trajectory-data-formatting"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/stream-trajectory-data-formatting.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 48 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,292 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 pass 7 Sept 2026
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 $0.00048 $0.01292
Opus 5 $0.00024 $0.00646
Sonnet 5 $0.00010 $0.00258
Haiku 4.5 $0.00005 $0.00129

Measured 6d ago against content hash 739a68fd4d7b, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

stream-trajectory-data-formatting 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 6d ago.

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.

collections/epigenomics/v1/skills/stream-trajectory-data-formatting/SKILL.md · 96 lines

How it starts

The opening of the file, as written. The whole thing — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Reconstruct peak matrix export for STREAM compatibility via exportPeakMatrixForSTREAM

Summary

Export a peak-by-cell matrix from ArchR in a format compatible with STREAM trajectory analysis tool. This skill enables interoperability between ArchR's scATAC-seq processing and STREAM's trajectory inference, allowing users to leverage STREAM's visualization and analysis capabilities on ArchR-processed peak data.

When to use

Use this skill when you have completed peak calling and cell annotation in ArchR and want to perform trajectory inference or visualization in STREAM. Apply it specifically when your analysis goal requires STREAM's specialized trajectory reconstruction methods (e.g., elastic principal graphs, branching structure inference) on single-cell ATAC-seq peak data.

When NOT to use

  • If your downstream trajectory tool is monocle3 or Slingshot — ArchR directly supports these via getMonocleTrajectories and addSlingShotTrajectories, without export overhead.
  • If you need to preserve full dimensionality reduction or embedding information — STREAM export focuses only on the peak matrix; trajectory-specific embeddings from ArchR are not exported.
  • If your peak matrix is already in a STREAM-compatible format from another source — re-exporting introduces redundant processing.

Inputs

  • ArchR project object (processed with peak calls and cell annotations)
  • Peak-by-cell accessibility matrix (internal ArchR representation)

Outputs

  • Peak-by-cell matrix in CSV or TSV format compatible with STREAM
  • Matrix with peaks as rows and cells as columns

How to apply

Load a processed ArchR project object containing peak calls and cell annotations. Call the exportPeakMatrixForSTREAM function on the ArchR project to generate a peak-by-cell matrix formatted for STREAM compatibility. The function produces output in CSV or TSV format that conforms to STREAM's expected matrix structure (peaks as rows, cells as columns, binary or accessibility values as matrix entries). Write the resulting matrix to a file and validate that the output dimensions match your peak and cell counts before importing into STREAM. The export preserves the peak-level and cell-level metadata necessary for downstream trajectory analysis.

Read the full file on GitHub · 96 lines

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. 6d ago First seen · 96 lines · 48 tokens per session scan A 739a68fd4d7b

Subscribe to this mod's changes

stream-trajectory-data-formatting is a skill published in the GitHub repository HolobiomicsLab/asb-skill-collections (15 stars, last pushed 2d ago), licensed Apache-2.0. It adds 48 tokens to every session and 1,292 once invoked, about $0.0002 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 2026-09-06.

Related

Other skills, from other repositories

external-model-validation

Use when validating an existing prognostic risk signature on an external bulk expression cohort with survival outcomes, producing risk scores, Kaplan-Meier curves, risk distribution plots, heatmap, and time-dependent ROC curves. NOT for: model training, feature selection, nomogram construction, calibration analysis…

aipoch/medical-research-skills · 66 tokens

medical-research-literature-reader-pro

A medical-research-native literature reading skill for users with clinical, bioinformatics, translational, and basic experimental backgrounds. Use this skill whenever a user wants to read, analyze, critique, or interpret a medical or scientific paper — whether they provide a PDF, abstract, DOI, PMID, or just a title.…

aipoch/medical-research-skills · 199 tokens

adverse-event-narrative

Generates CIOMS I-compliant ICSR narratives from adverse event case data for FDA and EMA regulatory submission. Includes temporal analysis, MedDRA coding, causality assessment using WHO-UMC or Naranjo criteria, and multi-format output.

aipoch/medical-research-skills · 57 tokens

anatomy-quiz-master

Generate interactive anatomy quizzes for medical education with multiple.

aipoch/medical-research-skills · 17 tokens

decision-curve-analysis

Use when evaluating the clinical utility of a binary prediction model from a single clinical CSV file by fitting a logistic decision-curve model, plotting decision and clinical-impact curves, and exporting summary outputs. NOT for: survival calibration, ROC-only discrimination analysis, nomogram construction, or…

aipoch/medical-research-skills · 64 tokens

elastic-net-feature-selection

Use when selecting predictive genes or other molecular features from bulk expression matrices for binary case-vs-control classification with elastic net logistic regression, including coefficient path and cross-validation plots. Trigger keywords: elastic net, glmnet, feature selection, binary classification…

aipoch/medical-research-skills · 83 tokens