multi-llm

multi-llm is a skill for Claude Code, Codex from beastlabai/multi-llm-plugin. It costs 146 tokens per session (5,181 once invoked), scanned A, original, MIT.

A workflow that asks multiple AI coding tools to review plans, create implementation tasks, write code, and review the results in parallel.

In plain words
What is it for?
Use it to compare plan reviews, split work into tasks, implement those tasks, review code, or ask several AI models the same question.
Why use it?
It gives you several independent opinions and implementations instead of relying on one coding assistant's interpretation.

Skill for Claude CodeCodex

Part of the multi-llm plugin — 1 skill shipped together

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/beastlabai/multi-llm-plugin/multi-llm
Any agent
npx skills add beastlabai/multi-llm-plugin --skill multi-llm
Clone the repo
git clone --depth 1 https://github.com/beastlabai/multi-llm-plugin

Made for: Claude Code, Codex.

Or install multi-llm, the plugin that ships this one along with the rest of its 1 skill.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for multi-llm

README.md
[![agentmods](https://agentmods.dev/badge/skills/beastlabai/multi-llm-plugin/multi-llm.svg)](https://agentmods.dev/skills/beastlabai/multi-llm-plugin/multi-llm)
Your own site
<a href="https://agentmods.dev/skills/beastlabai/multi-llm-plugin/multi-llm"><img src="https://agentmods.dev/badge/skills/beastlabai/multi-llm-plugin/multi-llm.svg" alt="Measured on agentmods" height="20"></a>
Per session 146 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 5,181 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00146 $0.05181
Opus 5 $0.00073 $0.02590
Sonnet 5 $0.00029 $0.01036
Haiku 4.5 $0.00015 $0.00518

Measured 3d ago against content hash fcc75429e892, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

multi-llm scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.

The scan reads SKILL.md. This mod also ships 12 executable files (apply_code_fixes_orchestrator.py, apply_suggestions_orchestrator.py, apply_task_suggestions_orchestrator.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/multi-llm/SKILL.md · 395 lines

How it starts

The opening of the file, as written. The whole thing — 395 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Multi-LLM Skill

Skill directory & path resolution — read this first.

Every orchestrator script, instruction file, prompt, schema, and template this skill uses is bundled inside the skill's own directory, whose absolute path is:

${CLAUDE_SKILL_DIR}

Claude Code expands ${CLAUDE_SKILL_DIR} to that absolute path inside this SKILL.md before you read it, so every command and path shown below is already fully resolved — run them as written.

The mode instruction files you open with the Read tool, and any "run this next" commands the orchestrators print to stdout, are not pre-expanded: they still contain the literal placeholder CLAUDE_SKILL_DIR (written as the shell-style variable ${...}). Whenever you encounter that placeholder in a file you read or in script output, substitute the absolute skill-directory path shown above before running the command or reading the file. The shell does not export this variable, so never run a command that still contains an unexpanded CLAUDE_SKILL_DIR.

Always double-quote the substituted skill path in shell commands — write --project "${CLAUDE_SKILL_DIR}" and "${CLAUDE_SKILL_DIR}/script.py", never the unquoted form. The path may contain spaces (e.g. Windows user profiles like C:\Users\John Smith\...), and an unquoted expansion word-splits and breaks the command.

A unified skill for multi-LLM plan automation. Supports eleven workflow modes plus a status command:

  1. Review Plan (--review-plan): Review an implementation plan with multiple LLMs (default)
  2. Apply Suggestions (--apply-suggestions): Apply validated suggestions from review to the plan
  3. Generate Tasks (--generate-tasks): Generate detailed implementation tasks from a high-level plan
  4. Review Tasks (--review-tasks): Review generated tasks with multiple LLMs
  5. Apply Task Suggestions (--apply-task-suggestions): Apply validated task review suggestions to tasks.md
  6. Implement (--implement): Execute implementation tasks from a plan
  7. Review Code (--review-code): Review code changes against the plan
  8. Apply Code Fixes (--apply-code-fixes): Apply validated fixes from code review
  9. Full Workflow (--full): Run all modes in sequence
  10. Status (--status): Show current workflow state and suggested next action
  11. Ask (--ask): Ask each model a free-text question about a plan; aggregate answers into one markdown file
  12. Init Config (--init): Set up a per-project provider config override at <git-root>/.multi-llm/providers.yaml (no plan path; routed via instructions/init-config.md). Fully automatic and zero-prompt: it auto-detects which provider CLIs are installed on PATH and writes a preconfigured override (uncommenting the detected providers' blocks and default_provider); --template-only skips detection and writes the inert commented stub. Flags: --dir PATH, --force, --gitignore, --template-only.

Read the full file on GitHub · 395 lines

Files

What ships with it

60 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 3d ago First seen · 395 lines · 0 tokens per session scan A fcc75429e892

Subscribe to this mod's changes

multi-llm is a skill published in the GitHub repository beastlabai/multi-llm-plugin (8 stars, last pushed 28d ago), licensed MIT. It adds 146 tokens to every session and 5,181 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Related

Other skills, from other repositories

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

brainstorming

You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.

obra/superpowers · 37 tokens

auto-perf-optimize

Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.

microsoft/vscode · 62 tokens

chat-perf

Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.

microsoft/vscode · 51 tokens

chat-pet-sprite-creation

Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.

microsoft/vscode · 53 tokens

cpu-profile-analysis

Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…

microsoft/vscode · 71 tokens