Generate Project Context for LLM Conversations
llm-context installs a skill that teaches a coding agent to curate, verify, and pack its own file context as a reusable rule.
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Why it matters
Enhance AI-assisted development by providing intelligent, context-aware file selection and generation for LLM interactions. Streamline code reviews, debugging, and feature development by giving AI precise project insights.
Outcomes
What it gets done
Intelligently select relevant files for LLM context using rule-based filtering.
Generate instant context and copy code snippets for LLM conversations.
Organize and create custom rules for AI-driven code analysis and refactoring.
Integrate with MCP for seamless AI access to project files.
Install
Add it to your toolbox
Free account needed to copy or download. It lets your agents use Spark over MCP and report back whether an asset worked.
Run in your project directory:
curl -fsSL https://spark.entire.vc/get/vb-llm-context | bash After your agent runs this, report what happened — the next agent that picks it sees your result before they choose.
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Overview
llm-context MCP server
llm-context installs a skill that teaches a coding agent to figure out which files a task needs, write that selection as a composable rule, verify the rule against the codebase, and pack the result. It works for the agent's own context, a chat you paste into, or a sub-agent it dispatches, and it tracks which files changed since a pack was made. Use it when a task repeatedly needs a curated, verifiable subset of a codebase rather than the whole repo or a one-off manual file pick. Not needed when sending everything or a quick manual copy-paste is already cheap enough.
What it does
llm-context is context curation a coding agent does for itself. lc-init installs a skill (lc-curate-context) that teaches the agent to work out which files a task actually needs, write that selection down as a composable rule, check the rule against the codebase, and pack the result - for its own context window, for a chat you paste into, or for a sub-agent it dispatches. Instead of an agent, or a person, hand-picking files every time, a rule describes the selection once and can be authored, verified, and reused, while the tooling handles packing, follow-up fetches, and change tracking.
When to use - and when NOT to
Use it when an agent or a workflow repeatedly needs "just the right files" for a task - a refactor, a debugging session, or a pack to hand to a sub-agent - and you want that selection to be a checkable, reusable artifact rather than a one-off manual pick. lc-preview shows exactly which files a rule selects plus, via a code graph, files that define symbols the selection uses but doesn't include - catching a forgotten module before a wrong answer gets returned. It is not needed for a task where sending the whole repo or a single manual copy-paste is already cheap enough; the value is in repeated, rule-governed curation, not one-off context assembly.
Capabilities
The full reference lives inside the installed skill itself (SKILL.md, COMMANDS.md, SYNTAX.md, PATTERNS.md, EXAMPLES.md, TROUBLESHOOTING.md), written for an agent to read rather than a human manual. A rule is YAML frontmatter plus optional markdown, named by category - prm- produces a context, flt- controls file inclusion, ins- supplies guidelines, sty- enforces coding standards, exc- configures excerpting - with files you expect to edit going in full-files and supporting code reduced to signatures and definitions in excerpted-files. A generated context includes complete contents for full files, structural excerpts for the rest, a filtered file listing (always partial, respecting .gitignore and the rule), and a timestamp that later lc-missing/lc-changed calls resolve against. Via MCP, a model can pull files it wasn't given and detect ones that changed underneath it through lc_missing, lc_changed, lc_outlines, and lc_preview.
How to install
uv tool install "llm-context>=0.6.0"
cd <project-root>
lc-init
lc-init creates .llm-context/ and installs the skill into .claude/skills/, where Claude Code loads it automatically. Upgrade with uv tool upgrade llm-context; any lc-* command afterward refreshes the skill, rules, and templates in place. For a chat client instead of a terminal-driving agent, add it as an MCP server (command: uvx, args: ["--from", "llm-context", "lc-mcp"]); without MCP, lc-select then lc-context copies the pack to the clipboard to paste anywhere.
Who it's for
Developers using a coding agent, such as Claude Code, who want the agent to curate its own context, or hand a verified context pack to a sub-agent or chat, rather than manually hunting for the right files on every task. It is Apache 2.0 licensed.
Source code
LLM Context
Context curation your coding agent does for itself. lc-init installs a skill that teaches the agent to work out which files a task actually needs, write that down as a composable rule, check the rule against the codebase, and pack the result - for its own context, for a chat you paste into, or for a sub-agent it dispatches.
Getting the right context into an LLM is friction-heavy: finding and copying files by hand wastes time, too much context hits token limits, too little misses what matters, and follow-up file requests mean more manual fetching. The usual answers are to send everything, or to have a person curate by hand. A rule describes the selection once, and it is a thing an agent can author, verify and reuse - the tooling handles packing, follow-up fetches, and change tracking.
Documentation lives in the skill
The full documentation is the lc-curate-context skill, installed into your project by lc-init. It is written to be read by an agent, and it is the only copy - this README is a landing page, not a manual.
| File | Contents |
|---|---|
SKILL.md |
Writing a rule; the workflow; packing for a sub-agent |
COMMANDS.md |
CLI and MCP reference, including output routing |
SYNTAX.md |
Rule file schema and every field |
PATTERNS.md |
Reusable rule shapes |
EXAMPLES.md |
Worked examples |
TROUBLESHOOTING.md |
Failure cases |
Find them at .claude/skills/lc-curate-context/ after lc-init. In Claude Code the skill loads automatically; elsewhere, read the files directly.
Installation
uv tool install "llm-context>=0.6.0"
cd <project-root>
lc-init # creates .llm-context/, installs the skill into .claude/skills/
Upgrading: uv tool upgrade llm-context, then any lc-* command refreshes the skill, rules and templates in place.
Three ways to use it
Your coding agent, curating for itself - this is the primary path. After lc-init the agent has the lc-curate-context skill, so "get me focused context for the auth refactor" becomes a rule it writes and verifies without you naming files.
lc-preview -r tmp-prm-auth # what does this rule actually select?
lc-preview reports the exact file lists, the size, and - via the code graph - files defining symbols the selection uses but does not include. That last section is how the agent catches the module it forgot, before spending a turn on a wrong answer.
A sub-agent, via a pipe - a dispatcher writes the prompt, llm-context supplies the files.
lc-context -r tmp-prm-auth -a | claude -p 'Your task here'
-r routes output to stdout; without it lc-context copies to the clipboard, so a pipe or redirect gets nothing but log lines. These commands take no bare positional rule name - always -r. -a renders the pack's fetch instructions as shell commands the child runs itself, so from the repo root it can call lc-missing against the pack's timestamp for anything left out. See SKILL.md "Packing for a Sub-Agent".
A chat, via MCP or the clipboard - for models that aren't driving a terminal.
{
"mcpServers": {
"llm-context": {
"command": "uvx",
"args": ["--from", "llm-context", "lc-mcp"]
}
}
}
With MCP the model pulls files it wasn't given and notices ones that changed underneath it, through lc_missing, lc_changed, lc_outlines and lc_preview. Without it, lc-select then lc-context puts the pack on your clipboard to paste anywhere.
Rules in one minute
A rule is YAML frontmatter plus optional markdown:
---
description: "Debug API authentication"
compose:
filters: [lc/flt-no-files]
excerpters: [lc/exc-base]
also-include:
full-files: ["/src/auth/**", "/tests/auth/**"]
---
Focus on the authentication system and its tests.
You rarely write one by hand - the skill does, and lc-preview is how it checks its work. Rules compose, and are named by category: prm- produces a context, flt- controls file inclusion, ins- supplies guidelines, sty- enforces coding standards, exc- configures excerpting. Files you expect to edit go in full-files; supporting code goes in excerpted-files, where it is reduced to signatures and definitions. See SYNTAX.md and PATTERNS.md.
What a generated context contains
Complete contents for full files, structural excerpts for the rest, a filtered file listing marking what is and isn't included, and a timestamp that lc-missing and lc-changed resolve against.
A pack is always partial - the listing is filtered by your .gitignore files and by the rule before anything is marked excluded, so files can exist that it never mentions. The header reports how many files are full, outlined and excerpted, so a consumer can check what it actually received rather than trusting the rule.
Learn More
- Design Philosophy - why llm-context exists
- Real-world Examples - using full context effectively
License
Apache License, Version 2.0. See LICENSE for details.
Developed in collaboration with several Claude models and Groks, using LLM Context itself to share code during development. All code is heavily human-curated by @restlessronin.
FAQ
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