Now liveThe Skillselion MCP - thousands of ranked skills, loaded into your agent mid-task. No install.Get it →
nickcrew avatar

Multi Llm Consult

  • 52 installs
  • 28 repo stars
  • Updated June 29, 2026
  • nickcrew/claude-ctx-plugin

Helps with ai & agent building tasks.

About

multi-llm-consult is a Claude Code skill for ai & agent building. It helps solo builders move faster with AI-assisted development.

  • multi-llm-consult
  • AI & Agent Building
  • AI-coding skill

Multi Llm Consult by the numbers

  • 52 all-time installs (skills.sh)
  • Ranked #7,034 of 16,556 AI & Agent Building skills by installs in the Skillselion catalog
  • Data as of Aug 4, 2026 (Skillselion catalog sync)
npx skills add https://github.com/nickcrew/claude-ctx-plugin --skill multi-llm-consult

Add your badge

Show developers this skill is listed on Skillselion. Paste this into your README.

Listed on Skillselion
Installs52
repo stars28
Last updatedJune 29, 2026
Repositorynickcrew/claude-ctx-plugin

What it does

Helps with ai & agent building tasks.

Files

SKILL.mdMarkdownGitHub ↗

Multi-LLM Consult

Overview

Use a bundled script to query external LLM providers with a sanitized prompt and return a concise comparison.

Setup

  • Configure API keys in the TUI: open the Command Palette (Ctrl+P) and run Configure LLM Providers.
  • Keys are stored in settings.json under llm_providers.

Workflow

1. Identify the purpose (second-opinion, plan, review, delegate). 2. Summarize the task and sanitize sensitive data before sending it out. 3. Run the consult script with the chosen provider. 4. Compare responses and reconcile with your own plan before acting.

Consult Script

Always run --help first:

python skills/multi-llm-consult/scripts/consult_llm.py --help

Example: second opinion

python skills/multi-llm-consult/scripts/consult_llm.py \
  --provider gemini \
  --purpose second-opinion \
  --prompt "We plan to refactor module X. What risks or gaps do you see?"

Example: delegate a review

python skills/multi-llm-consult/scripts/consult_llm.py \
  --provider qwen \
  --purpose review \
  --prompt-file /tmp/review_request.md \
  --context-file /tmp/patch.diff

Example: plan check with Codex (OpenAI)

python skills/multi-llm-consult/scripts/consult_llm.py \
  --provider codex \
  --purpose plan \
  --prompt "Draft a 5-step plan for implementing feature Y."

Output Handling

  • Treat responses as advisory; verify against repo constraints and current state.
  • Summarize the external response in 3-6 bullets before acting.
  • If responses conflict, call out the differences explicitly and choose a path.

References

  • Provider defaults and configuration: references/providers.md

Related skills

This week in AI coding

Five minutes, every Monday - the tools, releases and tactics for developers.

unsubscribe anytime.