awslabs/hcls-agent-skills is a collection of reusable instructions that help AI agents handle healthcare and life sciences work, including genomics, medical imaging, claims, and drug discovery. It is intended for agents running on Agent Skills-compatible platforms, and the catalogue entries are its individual domain skills.
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.
npx skills add awslabs/hcls-agent-skills --skill trajectory-analysisgit clone --depth 1 https://github.com/awslabs/hcls-agent-skillsWrote 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.
[](https://agentmods.dev/skills/awslabs/hcls-agent-skills/trajectory-analysis)<a href="https://agentmods.dev/skills/awslabs/hcls-agent-skills/trajectory-analysis"><img src="https://agentmods.dev/badge/skills/awslabs/hcls-agent-skills/trajectory-analysis/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.
<a href="https://agentmods.dev/skills/awslabs/hcls-agent-skills/trajectory-analysis"><img src="https://agentmods.dev/badge/skills/awslabs/hcls-agent-skills/trajectory-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00141 | $0.04165 |
| Opus 5 | $0.00071 | $0.02083 |
| Sonnet 5 | $0.00028 | $0.00833 |
| Haiku 4.5 | $0.00014 | $0.00417 |
Grade A, and why
trajectory-analysis 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 10d 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.
The source is not reproduced here
Licensed MIT-0
The repository is licensed MIT-0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.
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.
- 10d ago First seen · 352 lines · 141 tokens per session scan A cb96748f40db
trajectory-analysis is a skill published in the GitHub repository awslabs/hcls-agent-skills (31 stars, last pushed 9d ago), licensed MIT-0. It adds 141 tokens to every session and 4,165 once invoked, about $0.0007 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 2026-08-30.
Other skills, from other repositories
Developmental Gene Panel Design Workflow
Panel design for DEVELOPING / dynamic systems (embryonic organs, differentiation, regeneration). The target experiment is usually a LATE / terminal stage, but the biology is a trajectory: terminal cell types are end-products of earlier lineage programs. A panel built from the target stage alone resolves terminal…
bulkrna-trajblend
Load when placing bulk RNA-seq samples on a single-cell reference's pseudotime axis (NNLS deconvolution + nearest-neighbour mapping). Skip when plain cell-type proportions (use bulkrna-deconvolution); native single-cell trajectory inference (use sc-pseudotime).
sc-consensus-pseudotime
Load when you want a single-cell pseudotime ordering robust to the choice of trajectory method — fanning out DPT/Palantir/VIA from a shared root, rank-aligning them, and voting a consensus pseudotime with per-cell uncertainty. Skip when you have branching multi-lineage trajectories; no defined root.
Single-Cell Analysis Skills Index
Core skills for single-cell RNA-seq analysis: quality control, cell type annotation, and trajectory inference. These are high-priority actionable workflows — load them first for common single-cell tasks.
sc-pseudotime
Load when ordering cells along a developmental trajectory in a normalised scRNA AnnData via DPT, Palantir, VIA, CellRank, Slingshot (R), or Monocle3 (R). Skip when ranking marker genes per cluster (use sc-markers); RNA velocity vector fields (use sc-velocity).
spatial-trajectory
Load when inferring pseudotime / lineage trajectories on a preprocessed spatial AnnData via DPT (default — diffusion pseudotime), CellRank (terminal-state + fate-probability), or Palantir (waypoint branch probabilities). Skip when the data has spliced/unspliced layers and you want velocity-driven dynamics (use…