How do I install the LLM Tools skill?

Run npx skillmds@latest add iamneilroberts/llm-tools in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is LLM Tools safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with LLM Tools?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is LLM Tools free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published LLM Tools?

iamneilroberts (@iamneilroberts) published this skill. Their other Agent Skills are listed on their SkillMD profile.

LLM Tools by iamneilroberts — Agent Skill

Name: LLM Tools
Author: iamneilroberts

LLM Tools

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.

File contents

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`
A mechanical step repeats ≥3× identically (same transform over many items)	Write a throwaway `scripts/<name>.sh` and run that — automate the repetition in code; don't spend model tokens re-prompting the same operation

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.

---name: llm-toolsdescription: 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## OverviewPremium-model context is the scarcest resource in a session. These three CLIs routebulk I/O and predictable generation to a cheap long-context worker; the premium modelconsumes the short answer instead of the raw files. Install + provider config:[README.md](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` || A mechanical step repeats ≥3× identically (same transform over many items) | Write a throwaway `scripts/<name>.sh` and run *that* — automate the repetition in code; don't spend model tokens re-prompting the same operation |## 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 descriptionsIf a delegated answer looks wrong or thin, re-read the sources yourself rather thantrusting the worker. Worker output is untrusted by default: the worker drafts, thepremium model reviews and makes the edits.## Cost referenceAsking a cheap worker to summarize a ~12k-token doc runs ~125× cheaper than the sameread on a frontier model, at quality that's fine for "summarize / find facts" — whichis 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.

Frequently asked questions