llms

llms is a command for coding agents from microsoft/skills. It costs 25 tokens per session (2,126 once invoked), scanned A, original, MIT.

A command for generating llms.txt and llms-full.txt files for a project wiki. These are text files that summarize a project and point language models to its documentation; the full version includes the linked content.

In plain words
What is it for?
Use it to create concise and full machine-readable summaries of a wiki, with links based on either a remote repository or local files.
Why use it?
It makes repository documentation easier for language models to discover and read within small or large context limits.

Command

Part of the deep-wiki plugin — 10 commands, 3 agents shipped together

About the project

microsoft/skills is a collection of skills, custom agents, AGENTS.md templates, plugins, hooks, commands, and MCP configurations that give AI coding agents context for Azure SDK and Microsoft AI Foundry development. Developers use it to install selected domain-specific guidance into coding-agent environments. The catalogue entries are the repository’s own agent resources and supporting configurations.

microsoft/skills · 2,989 stars · on GitHub

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.

agentmods
npx agentmods add commands/microsoft/skills/llms
Clone the repo
git clone --depth 1 https://github.com/microsoft/skills

Or install deep-wiki, the plugin that ships this one along with the rest of its 10 commands, 3 agents.

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 llms

README.md
[![agentmods](https://agentmods.dev/badge/commands/microsoft/skills/llms.svg)](https://agentmods.dev/commands/microsoft/skills/llms)
Your own site
<a href="https://agentmods.dev/commands/microsoft/skills/llms"><img src="https://agentmods.dev/badge/commands/microsoft/skills/llms.svg" alt="Measured on agentmods" height="20"></a>
Per session 25 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 2,126 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00025 $0.02126
Opus 5 $0.00013 $0.01063
Sonnet 5 $0.00005 $0.00425
Haiku 4.5 $0.00003 $0.00213

Measured yesterday against content hash 52dbc9ab2789, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

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 yesterday.

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.

.github/plugins/deep-wiki/commands/llms.md · 207 lines

How it starts

The opening of the file, as written. The whole thing — 207 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Deep Wiki: Generate llms.txt

You are generating llms.txt and llms-full.txt files that provide LLM-friendly access to the wiki documentation. These follow the llms.txt specification.

Source Repository Resolution (MUST DO FIRST)

Before generating, resolve the source repository context:

  1. Check for git remote: Run git remote get-url origin
  2. Ask the user: "Is this a local-only repository, or do you have a source repository URL?"
    • Remote URL → store as REPO_URL, use linked references: [Title](REPO_URL/blob/BRANCH/path)
    • Local → use relative paths to wiki files
  3. Determine default branch: Run git rev-parse --abbrev-ref HEAD
  4. Do NOT proceed until resolved

What is llms.txt

llms.txt is a standardized markdown file that helps LLMs quickly understand a project. It provides:

  • A concise project summary
  • Links to key documentation files with brief descriptions
  • Structured sections (Onboarding, Architecture, API, etc.)

Two files are generated:

File Purpose Size
llms.txt Links + brief descriptions — fits in small context windows Small (1-5 KB)
llms-full.txt Full inlined content of all linked pages Large (50-500 KB)

Step 1: Gather Project Context

Scan the repository and existing wiki (if generated) to collect:

  1. Project identity — name, one-sentence description, primary language, key technologies
  2. Wiki pages — scan wiki/ directory for all generated .md files
  3. Onboarding guides — check for onboarding/ folder with audience-tailored guides
  4. README — extract the core project description
  5. Key entry points — main files, API surface, configuration

Step 2: Generate llms.txt

Create wiki/llms.txt following the llms.txt spec format:

# {Project Name}

> {One-paragraph summary: what it does, who it's for, key technologies. Dense and informative.}

{2-3 paragraphs of important context: architectural philosophy, key constraints, what makes this project different. Include things an LLM needs to know to give accurate answers about this project.}

## Onboarding

- [{Contributor Guide}](./onboarding/contributor-guide.md): Step-by-step guide for new contributors — environment setup, first task, testing, and coding conventions
- [{Staff Engineer Guide}](./onboarding/staff-engineer-guide.md): Architectural deep-dive for senior engineers — design decisions, domain model, component types, and failure modes
- [{Executive Guide}](./onboarding/executive-guide.md): Capability overview for engineering leaders — risk assessment, technology investment, and scaling model
- [{Product Manager Guide}](./onboarding/product-manager-guide.md): Feature-focused guide for PMs — user journeys, capabilities, limitations, and data/privacy

## Architecture

- [{Architecture Overview}](./02-architecture/overview.md): System architecture, component boundaries, and deployment topology
- [{Data Model}](./02-architecture/data-model.md): Core entities, relationships, and data invariants
- [{API Reference}](./02-architecture/api-reference.md): Endpoints, authentication, and wire format

## Getting Started

- [{Setup Guide}](./01-getting-started/setup.md): Prerequisites, installation, and first run
- [{Configuration}](./01-getting-started/configuration.md): Environment variables, feature flags, and config files

## Deep Dive

- [{Component Name}](./03-deep-dive/component.md): Description of component purpose and scope
- ...additional pages...

## Optional

- [{Changelog}](./changelog.md): Recent changes and version history
- [{Contributing}](./contributing.md): How to contribute to the project

Read the full file on GitHub · 207 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. yesterday First seen · 207 lines · 25 tokens per session scan A 52dbc9ab2789

Subscribe to this mod's changes

llms is a command published in the GitHub repository microsoft/skills (2,989 stars, last pushed yesterday), licensed MIT. It adds 25 tokens to every session and 2,126 once invoked, about $0.0001 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.