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

•

Productivity & Workflow

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

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

0115 GitHub stars

02Quantified V_meta calculation based on documentation, metrics, patterns, and evidence

03Standardized quality levels from Minimal to Exceptional for benchmark tracking

04Predictive ROI modeling for time investment vs. total project duration

05Three-pillar strategy for leveraging prior art, historical data, and domain universality

06Iteration 0 checklist to ensure foundational quality before scaling

Use Cases

01Quantifying baseline performance using historical session data and error logs

02Targeting rapid 3-4 iteration convergence in domains with established best practices

03Planning iteration 0 time allocation for complex or time-constrained software projects

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add yaleh/meta-cc baseline-quality-assessment

For use in Claude.ai and ChatGPT

Download Skill