What is the V_meta metric in this skill?

V_meta is a meta-quality score calculated by averaging four dimensions: Completeness (documentation), Effectiveness (quantification), Reusability (patterns), and Validation (evidence).

Does this skill work for greenfield projects?

It is less effective for purely exploratory greenfield projects without prior art, as the methodology relies on referencing established practices and historical data.

How does it help Claude converge faster?

By establishing a high-quality baseline early, it provides Claude with clearer objectives and better context, reducing the need for trial-and-error in later iterations.

When is the best time to apply Baseline Quality Assessment?

It is most effective when planning the start of a project (Iteration 0), especially when domain best practices exist or there is historical data to quantify.

How much time can I save by using this skill?

By investing an extra 3-4 hours in Iteration 0 to reach a V_meta ≥0.40, experiments show a 40-50% reduction in total iterations, typically saving 3-6 hours overall.

Baseline Quality Assessment

Name: Baseline Quality Assessment
Author: yaleh

byyaleh

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Productivity & Workflow

Optimizes development workflows by establishing high-quality project baselines to accelerate convergence and reduce total development time by up to 50%.

About

Baseline Quality Assessment is a meta-cognition utility for Claude Code designed to front-load project effort during the initial phase to ensure rapid convergence. By quantifying the project baseline across four dimensions—completeness, effectiveness, reusability, and validation—the skill helps developers achieve a high Meta-Value (V_meta ≥ 0.40). This structured approach allows teams to identify gaps early, leverage prior art, and utilize historical data to reduce the total number of iterations required to reach a stable, production-ready state, ultimately saving hours of development time.

Key Features

15 GitHub stars
Quantified V_meta calculation based on documentation, metrics, patterns, and evidence
Standardized quality levels from Minimal to Exceptional for benchmark tracking
Predictive ROI modeling for time investment vs. total project duration
Three-pillar strategy for leveraging prior art, historical data, and domain universality
Iteration 0 checklist to ensure foundational quality before scaling

Use Cases

Quantifying baseline performance using historical session data and error logs
Targeting rapid 3-4 iteration convergence in domains with established best practices
Planning iteration 0 time allocation for complex or time-constrained software projects

About

Key Features

15 GitHub stars
Quantified V_meta calculation based on documentation, metrics, patterns, and evidence
Standardized quality levels from Minimal to Exceptional for benchmark tracking
Predictive ROI modeling for time investment vs. total project duration
Three-pillar strategy for leveraging prior art, historical data, and domain universality
Iteration 0 checklist to ensure foundational quality before scaling

Use Cases

Quantifying baseline performance using historical session data and error logs
Targeting rapid 3-4 iteration convergence in domains with established best practices
Planning iteration 0 time allocation for complex or time-constrained software projects