Context Distill FAQs

Question 1

How can I integrate Context Distill into my workflow?

Accepted Answer

Context Distill offers a triple interface for maximum flexibility: a zero-config Skill file for AI agents (like Claude Code, Codex, Cursor), a standalone CLI for direct shell use and scripts, and an MCP server for protocol-native clients. The Skill mode is highly recommended for seamless agent adoption.

Question 2

What types of command output can Context Distill optimize?

Accepted Answer

It's ideal for optimizing a wide range of verbose outputs, including CI/build logs, test results (e.g., `go test`), `git diffs`, `docker logs`, file listings (`find`, `ls -lR`), and general status checks. Essentially, any output that's too long to send to an LLM efficiently.

Question 3

What are the core distillation operations it provides?

Accepted Answer

Context Distill features two primary operations: `distill_batch`, which compresses a full block of command output to answer a specific question, and `distill_watch`, which identifies and summarizes relevant deltas between two consecutive snapshots of output, perfect for monitoring changes.

Question 4

Does Context Distill support my LLM provider?

Accepted Answer

Yes, Context Distill is built for flexibility. It supports Ollama and any OpenAI-compatible LLM provider. You can easily configure your preferred provider via YAML files, environment variables, or through its interactive terminal UI (`--config-ui`).

Question 5

What is Context Distill and why do I need it?

Accepted Answer

Context Distill is a Go tool designed to compress verbose command output (like logs, test results, or diffs) into concise, actionable summaries. It's essential for developers using LLMs to reduce token usage, lower API costs, and keep context windows clean for more efficient AI processing.

Context Distill

Context Distill

主な機能

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主な機能

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