| name | llm-tools |
| description | Use when a session is about to bulk-read 3+ files or any single file over ~400 lines, generate predictable boilerplate (tests, fixtures, config scaffolds, doc templates), or compress a long session transcript — and a cheap OpenAI-compatible worker model (Kimi, DeepSeek, OpenRouter, local Ollama) is available via the llm-ask/llm-write/llm-extract CLIs. |
llm-tools — cheap-worker delegation
Overview
Premium-model context is the scarcest resource in a session. These three CLIs route
bulk I/O and predictable generation to a cheap long-context worker; the premium model
consumes the short answer instead of the raw files. Install + provider config:
README.md in this directory (~1-minute setup, any
/v1/chat/completions provider).
Routing rules (the rules save the tokens; the CLIs just execute)
| Situation | Do this |
|---|
| Would read 3+ files, or one file >~400 lines, to answer a question | llm-ask <files...> -q "..." — read the answer, not the files |
| Need tests / fixtures / config scaffold / doc template from a spec | llm-write -r <ref> -s "<spec>" -o <path> — then review and edit; never trust blind |
| Long transcript needs to become doc updates | llm-extract <transcript.jsonl> --sections summary,decisions,todos |
Never delegate
- Architectural or design decisions
- Debugging — cheap models miss subtle bugs
- Anything touching auth, payments, PII, deletion, or production data
- Final commits and PR descriptions
If a delegated answer looks wrong or thin, re-read the sources yourself rather than
trusting the worker. Worker output is untrusted by default: the worker drafts, the
premium model reviews and makes the edits.
Cost reference
Asking a cheap worker to summarize a ~12k-token doc runs ~125× cheaper than the same
read on a frontier model, at quality that's fine for "summarize / find facts" — which
is why the >400-line / 3-file thresholds pay for themselves almost immediately.
Common mistakes
- Delegating a judgment question because it happens to involve big files — split it:
llm-ask extracts the facts, the premium model judges.
- Passing files containing real credentials — redact first; workers don't get secrets.
- Treating
llm-write output as done — it ships boilerplate, you ship correctness.
