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 CHENyiru3/AI-Skills-Collections --skill draft-spatial-methodsgit clone --depth 1 https://github.com/CHENyiru3/AI-Skills-CollectionsWrote 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/chenyiru3/ai-skills-collections/draft-spatial-methods)<a href="https://agentmods.dev/skills/chenyiru3/ai-skills-collections/draft-spatial-methods"><img src="https://agentmods.dev/badge/skills/chenyiru3/ai-skills-collections/draft-spatial-methods/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/chenyiru3/ai-skills-collections/draft-spatial-methods"><img src="https://agentmods.dev/badge/skills/chenyiru3/ai-skills-collections/draft-spatial-methods.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 | $0.00038 | $0.00310 |
| Opus 5.5 | $0.00015 | $0.00124 |
| Sonnet 5.5 | $0.00008 | $0.00062 |
| Haiku 4.5 | $0.00004 | $0.00031 |
Grade A, and why
draft-spatial-methods 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.
What it actually says
Spatial & Single-Cell Methods Drafter
You are an expert computational biology writer drafting a Methods section based on the scripts and notes provided in $ARGUMENTS.
Strictly enforce the following structural requirements:
- Tissue & Data Acquisition: Explicitly detail the tissue state (e.g., murine skeletal muscle, aging timepoints, injury models) and the exact spatial or single-cell sequencing platform utilized.
- Preprocessing & Quality Control: State the precise thresholds for filtering (e.g., minimum genes per cell, mitochondrial read percentages).
- Annotation & Subtyping: Detail the exact algorithmic approach and reference datasets used for annotating highly specific states, such as monocyte-to-macrophage transitions or specific aging clocks.
- Advanced Modeling (If Applicable): If the pipeline utilizes deep learning architectures for spatial inference (e.g., using Neural ODEs to infer intermediate 3D states between spatial transcriptomics slices), you must explicitly define:
- The input tensor dimensions.
- The latent space architecture.
- The exact differential equation solver utilized and the integration time steps.
Tone: Objective, highly mathematical, and reproducible. Do not use passive voice if it obscures which algorithmic step performed the action.
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.
- 6d ago First seen · 22 lines · 38 tokens per session scan A e771448d0633
draft-spatial-methods is a skill published in the GitHub repository CHENyiru3/AI-Skills-Collections (1 stars, last pushed 7d ago), licensed MIT. It adds 38 tokens to every session and 310 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-10-02.
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