LLM-Friendly Context

LLM-Friendly Context is an AI Engineering skill for Claude Code, published by shinpr in claude-code-workflows.

669 stars102 forkson shinpr/claude-code-workflowsAdded 2026/08/24Repository updated 2026/08/23

agent-skillsagentic-aiagentic-codingai-agentsanthropicclaude-codeclaude-code-pluginclaude-code-skillcode-qualitycode-reviewdeveloper-toolsdevelopment-workflowllm-orchestrationproductivitysubagents

Install in seconds

Install LLM-Friendly Context

Copy LLM-Friendly Context into your Claude Code skills folder. Run the command in your terminal, or review the source on GitHub before installing.

terminal

npx degit https://github.com/shinpr/claude-code-workflows/tree/main/skills/llm-friendly-context ~/.claude/skills/llm-friendly-context

Requires Node.js. Downloads this skill only — not the rest of the repository — into your Claude Code skills folder.

Without Node.js

git clone https://github.com/shinpr/claude-code-workflows.git

Clones the whole repository, then copy the skill’s own directory into your skills folder yourself.

In this catalog

Source file: skills/llm-friendly-context/SKILL.md in shinpr/claude-code-workflows
Installs to: ~/.claude/skills/llm-friendly-context
Collection: One of 25 skills cataloged from this repository
Category: AI Engineering — 2451 skills

What LLM-Friendly Context does

LLM-Friendly Context helps you write prompts, handoffs, and generated instructions with explicit inputs, outputs, success criteria, and unresolved decisions. Use it when revising LLM-facing artifacts so downstream consumers can act without guessing.

LLM-Friendly Context is cataloged under AI Engineering on DirSkills. LLM-Friendly Context comes from a repository tagged agent-skills, agentic-ai, agentic-coding, ai-agents and anthropic.

Documentation

README

Excerpt

LLM-Friendly Context

The goal is stable downstream execution: the next consumer should know what to read, what to do, what counts as success, and which unresolved decisions can change the result.

Core Rules

Use positive, executable instructions
- State what the next consumer should do.
- Convert quality policies into positive criteria.
- Keep a prohibition only when it protects an irreversible boundary or shipped contract. Name the protected condition and the allowed action.
- Example: "Preserve existing public API behavior across the documented compatibility cases."

This is the opening of the README. Read the full README on GitHub.

Frequently asked about LLM-Friendly Context

What else does shinpr publish alongside LLM-Friendly Context?
LLM-Friendly Context is one of 25 skills that DirSkills catalogs from shinpr/claude-code-workflows, the repository it ships in. Its siblings there include AI Developer Guide, Coding Principles and Documentation Criteria. Each one is a separate skill with its own page in this directory, installs the same way LLM-Friendly Context does, and is maintained by shinpr in that same repository. The rest of the collection is listed on the shinpr/claude-code-workflows page.
How does LLM-Friendly Context compare to other AI Engineering skills?
LLM-Friendly Context ranks #1814 by stars among the 2451 AI Engineering skills in this catalog. The most-starred ones next to it are Architecture Decision Records, AI-First Engineering and Agentic OS. DirSkills ranks by the star count of the repository each skill ships in, so that order reflects how popular those repositories are rather than any review of LLM-Friendly Context against them. Open each page to compare what they document and how they install.