LLM-Friendly Context

LLM-Friendly Context is an AI Engineering skill for Claude Code, published by shinpr in ai-coding-project-boilerplate.

227 stars25 forkson shinpr/ai-coding-project-boilerplateAdded 2026/09/03Repository updated 2026/09/01

agent-skillsagentic-aiagentic-workflowai-agentsanthropicboilerplateclaude-codecontext-engineeringdeveloper-toolsllm-orchestrationproductivityquality-gatestypescript

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/ai-coding-project-boilerplate/tree/main/.claude/skills-en/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/ai-coding-project-boilerplate.git

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

In this catalog

Source file: .claude/skills-en/llm-friendly-context/SKILL.md in shinpr/ai-coding-project-boilerplate
Installs to: ~/.claude/skills/llm-friendly-context
Collection: One of 25 skills cataloged from this repository
Category: AI Engineering — 2793 skills

What LLM-Friendly Context does

LLM-Friendly Context helps you write prompts, handoffs, and generated artifacts with explicit inputs, outputs, success criteria, and unresolved conditions. Use it when downstream agents need enough context to execute 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-workflow, ai-agents and anthropic.

Documentation

README

Excerpt

LLM-Friendly Context

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

This skill governs the clarity of LLM-facing output — prompts, handoffs, and generated artifacts. The caller supplies the artifact type and any artifact-specific template or input contract; include only the information its consumer uses to decide, act, or verify. Use a declared contract's field names and value meanings when the consumer branches on them.

Core Rules

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/ai-coding-project-boilerplate, the repository it ships in. Its siblings there include Coding Standards, Coding Standards and Create Skill. 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/ai-coding-project-boilerplate page.
How does LLM-Friendly Context compare to other AI Engineering skills?
LLM-Friendly Context ranks #2511 by stars among the 2793 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.