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LLM Context.Py
Share code with LLMs via Model Context Protocol or clipboard. Rule-based customization enables easy switching between different tasks (like code review and documentation).…
Install
git clone https://github.com/cyberchitta/llm-context.pyInstall command taken from the project's README or registry entry. Always review code before running it.
README excerpt
Read full README on GitHub →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
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"]
}
}
}
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