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 agentmods add skills/agentconfig/agentconfig.org/generate-llmsnpx skills add agentconfig/agentconfig.org --skill generate-llmsgit clone --depth 1 https://github.com/agentconfig/agentconfig.orgWrote 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/agentconfig/agentconfig.org/generate-llms)<a href="https://agentmods.dev/skills/agentconfig/agentconfig.org/generate-llms"><img src="https://agentmods.dev/badge/skills/agentconfig/agentconfig.org/generate-llms.svg" alt="Measured on agentmods" 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 | $0.00051 | $0.01644 |
| Opus 5 | $0.00026 | $0.00822 |
| Sonnet 5 | $0.00010 | $0.00329 |
| Haiku 4.5 | $0.00005 | $0.00164 |
Grade A, and why
generate-llms 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 3d 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.
How it starts
The opening of the file, as written. The whole thing — 222 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Generate LLMs Files
Generate /llms.txt and /llms-full.txt files that help AI agents understand and use the agentconfig.org site content.
Overview
This skill generates machine-readable documentation files following the llmstxt.org standard:
/llms.txt— A curated table of contents with links to detailed pages/llms-full.txt— Comprehensive markdown containing all site content/*.md— Page-specific markdown files (skills.md, agents.md, mcp.md, etc.)
Page Registry
Pages are automatically discovered from the page registry at site/src/data/pages.ts:
export const pages: readonly PageMeta[] = [
{
slug: 'skills',
title: 'Skills Tutorial',
description: 'How to create agent skills...',
mdFile: 'skills.md',
partNumber: 4,
},
// ... more pages
]
When creating a new page, add an entry to the registry to include it in the llms files.
When to Use
Use this skill when:
- You've updated content in the data files (
site/src/data/*.ts) - You've added a new page (remember to add it to the page registry!)
- Before deploying or releasing a new version of the site
- When an agent needs to understand what content should be in llms files
Data Sources
The llms files are generated from these TypeScript data files:
| File | Content |
|---|---|
site/src/data/pages.ts |
Page registry - lists all pages for llms generation |
site/src/data/primitives.ts |
13 AI primitives with descriptions, use cases, and provider implementations |
site/src/data/comparison.ts |
Four-provider comparison matrix derived from primitives.ts |
site/src/data/providerProfiles.ts |
Per-provider profiles derived from primitives.ts |
site/src/data/skillsTutorial.ts |
Skills tutorial sections and concepts |
site/src/data/skillExamples.ts |
5 example skills with full code |
site/src/data/agentsTutorial.ts |
Agent instructions guide with code samples |
site/src/data/hooksTutorial.ts |
Hooks tutorial with provider tabs, lifecycle events, and reusable policy logic |
site/src/data/mcpTutorial.ts |
MCP tutorial sections and code samples |
What ships with it
2 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.
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.
- 3d ago First seen · 222 lines · 51 tokens per session scan A aea34e179478
generate-llms is a skill published in the GitHub repository agentconfig/agentconfig.org (8 stars, last pushed 6d ago), licensed ISC. It adds 51 tokens to every session and 1,644 once invoked, about $0.0003 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 2026-08-31.
Other skills, from other repositories
gget
Fast CLI/Python queries to 20+ bioinformatics databases. Use for quick lookups: gene info, BLAST searches, AlphaFold structures, enrichment analysis. Best for interactive exploration, simple queries. For batch processing or advanced BLAST use biopython; for multi-database Python workflows use bioservices.
deeptools
NGS analysis toolkit. BAM to bigWig conversion, QC (correlation, PCA, fingerprints), heatmaps/profiles (TSS, peaks), for ChIP-seq, RNA-seq, ATAC-seq visualization.
flow-cytometry-analysis
Complete flow cytometry analysis pipeline. FCS file handling, compensation, manual/automated gating, immunophenotyping, CFSE proliferation analysis, cell cycle analysis (Dean-Jett-Fox), and apoptosis assays. Extends flowio with analytical workflows. For raw FCS parsing only use flowio.
flowio
Parse FCS (Flow Cytometry Standard) files v2.0-3.1. Extract events as NumPy arrays, read metadata/channels, convert to CSV/DataFrame, for flow cytometry data preprocessing.
histolab
Lightweight WSI tile extraction and preprocessing. Use for basic slide processing tissue detection, tile extraction, stain normalization for H&E images. Best for simple pipelines, dataset preparation, quick tile-based analysis. For advanced spatial proteomics, multiplexed imaging, or deep learning pipelines use pathml.
microbial-dynamics
Microbial population dynamics modeling and analysis. Bacterial growth curve fitting (logistic, Gompertz, Baranyi), Lotka-Volterra community dynamics, Gillespie stochastic simulation, biofilm quantification, CFU enumeration, and genome annotation. For metabolic modeling use cobrapy; for sequence analysis use biopython.