Project Completeness Scorer

Evaluate project deliverables (code, docs, config) and calculate a 0-100 completeness score with weighted criteria and prioritized action items. Use when assessing project readiness, reviewing milestones, or identifying gaps before release.

No API Required

Download Skill Package (.skill) View Source on GitHub

Table of Contents

1. Overview

This skill systematically evaluates project deliverables across multiple dimensions (functional requirements, quality standards, test coverage, documentation, deployment readiness) and produces a weighted 0-100 completeness score. It identifies gaps, ranks missing items by priority, and provides actionable next steps to reach completion.

2. Prerequisites

  • Python 3.9+
  • No API keys required
  • Standard library only (json, pathlib, argparse)

3. Quick Start 

# List available templates
python3 scripts/score_project.py --list-templates

# Use a specific template
python3 scripts/score_project.py --template skill --project-path ./skills/my-skill

4. How It Works

Step 1: Select Project Template

Determine the project type and load the appropriate evaluation template.

Supported project types:

  • skill – Claude skill development (SKILL.md, scripts, tests, references)
  • webapp – Web application (frontend, backend, API, tests, docs)
  • library – Reusable library/package (code, tests, docs, packaging)
  • document – Documentation-only project (structure, completeness, quality)
  • custom – User-defined criteria from JSON file
# List available templates
python3 scripts/score_project.py --list-templates

# Use a specific template
python3 scripts/score_project.py --template skill --project-path ./skills/my-skill

Step 2: Gather Project Artifacts

Scan the project directory to inventory existing files and categorize them by evaluation dimension:

  1. Functional Requirements – Core deliverables and features

See the skill’s SKILL.md for the full end-to-end workflow.

5. Usage Examples

  • Assessing a project’s readiness for release or handoff
  • Reviewing milestone deliverables against acceptance criteria
  • Identifying gaps in code, documentation, or configuration
  • Comparing actual progress against a project checklist
  • Preparing for stakeholder reviews or gate approvals
  • Scoring skill development projects within this repository

6. Understanding the Output

JSON Report

```json { “schema_version”: “1.0”, “project_path”: “./skills/my-skill”, “project_type”: “skill”, “timestamp”: “2026-03-16T08:00:00Z”, “overall_score”: 85, “dimensions”: [ { “name”: “Functional Requirements”, “weight”: 0.30, “raw_score”: 90, “weighted_score”: 27.0, “criteria”: [ {“name”: “SKILL.md exists”, “met”: true, “severity”: “critical”}, {“name”: “Scripts directory exists”, “met”: true, “severity”: “major”}

The full output details are documented in SKILL.md.

7. Tips & Best Practices

  • Begin with the smallest realistic sample input so you can validate the workflow before scaling up.
  • Keep skills/project-completeness-scorer/SKILL.md open while working; it remains the authoritative source for the full procedure.
  • Review the most relevant reference files first instead of scanning every guide: scoring-methodology.md, project-templates.md.
  • Run helper scripts on test data before using them on final assets or production-bound inputs: score_project.py.
  • Preserve intermediate outputs so you can explain assumptions, diffs, and follow-up actions clearly.

8. Combining with Other Skills

  • Combine this skill with adjacent skills in the same category when the work spans planning, implementation, and review.
  • Browse the broader category for neighboring workflows: category index.
  • Use the English skill catalog when you need to chain this workflow into a larger end-to-end process.

9. Troubleshooting

  • Re-check prerequisites first: missing runtime dependencies and unsupported file formats are the most common failures.
  • If a helper script is involved, run it with a minimal sample input before applying it to a full dataset or repository.
  • Compare your input shape against the reference files to confirm expected fields, sections, or metadata are present.
  • Confirm the expected Python version and required packages are installed in the active environment.
  • When output looks incomplete, inspect the script arguments and rerun with explicit input/output paths.

10. Reference

References:

  • skills/project-completeness-scorer/references/project-templates.md
  • skills/project-completeness-scorer/references/scoring-methodology.md

Scripts:

  • skills/project-completeness-scorer/scripts/score_project.py