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megaplan-bakeoff

Coordinate multi-profile LLM bake-offs in megaplan with blind rubric scoring.

97|8|Updated Mar 20, 2026
Search Tags:#benchmarking#cost optimization#quality scoring#prompt hygiene#llm bake-off#blind evaluation#pipeline robustness
One-click install
npx skills add https://github.com/peteromallet/arnold --skill megaplan-bakeoff
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: megaplan-bakeoff
Source: https://github.com/peteromallet/arnold/tree/main/megaplan/data/_codex_skills/megaplan-bakeoff
Command: npx skills add https://github.com/peteromallet/arnold --skill megaplan-bakeoff

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It helps you compare different megaplan profile mixes on the same task without wasting money or trusting misleading outputs, producing a fair “winner” based on blind, rubric-driven assessment.

Core Features & Use Cases

  • Multi-profile concurrent bake-offs: Run the same idea across N profiles to test which mix delivers better quality per cost.
  • Smoke testing and launch hygiene: Validate routing/model behavior in doc-mode first to catch failures cheaply before code-mode runs.
  • Blind assessment workflow: Enforce sub-agent blinding, rubric scoring, and style quotes so evaluation is consistent and not profile-aware.
  • Pre-merge validation gate: Detect empty diffs and other misdirections before selecting or merging results into main.
  • Reporting patterns for decision-making: Produce comparison tables and cost-adjusted conclusions that summarize trade-offs and production readiness.

Quick Start

Tell the megaplan bakeoff runner to execute a light-robustness, blind-scored bake-off for your task idea across your chosen profiles, then pick and merge the winner.

Frequently Asked Questions about megaplan-bakeoff

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run a fair LLM bake-off to compare cost and quality across different profiles?▼

Run a fair LLM bake-off by executing identical tasks concurrently across multiple profiles, using blind rubric-based sub-agent scoring and strict prompt hygiene to generate structured cost-quality comparison tables for selecting the optimal mix.

What is blind rubric-based evaluation for benchmarking LLM pipelines?▼

Blind rubric-based evaluation is an assessment mechanism where sub-agents score LLM outputs without profile awareness, ensuring unbiased, consistent quality measurement during pipeline benchmarking and head-to-head comparisons.

How do I prevent wasted spend when benchmarking multiple LLM profiles on code generation?▼

Prevent wasted spend during LLM benchmarking by running a lightweight doc-mode smoke test to validate routing and catch failures cheaply before executing expensive code-mode runs across multiple profiles.

How do I verify LLM code generation outputs before merging the winning profile?▼

Verify LLM code generation outputs before merging by applying a pre-merge validation gate that detects empty diffs and other misdirections, ensuring only valid, production-ready results are selected into main.

Does prompt hygiene affect pipeline robustness when comparing multiple LLM profiles?▼

Prompt hygiene directly affects pipeline robustness during multi-profile comparisons, ensuring consistent task execution and preventing routing or behavioral anomalies from skewing cost-quality trade-offs.

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