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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.

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See how to install

Files included (6)

  • SKILL.md5.4 KB
  • references/project-templates.md10.2 KB
  • references/scoring-methodology.md5.7 KB
  • scripts/score_project.py41.9 KB
  • scripts/tests/conftest.py3.4 KB
  • scripts/tests/test_score_project.py12.7 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

Project Completeness Scorer

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.

When to Use

  • 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

Prerequisites

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

Workflow

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
  2. Quality Standards -- Code quality, linting, formatting
  3. Test Coverage -- Unit tests, integration tests, test results
  4. Documentation -- README, API docs, user guides
  5. Deployment Readiness -- Config files, CI/CD, environment setup
python3 scripts/score_project.py \
  --template skill \
  --project-path ./skills/my-skill \
  --verbose

Step 3: Evaluate Each Dimension

For each evaluation dimension, check criteria against the project inventory:

  1. Read references/scoring-methodology.md for detailed scoring rules
  2. Apply dimension weights from the template
  3. Calculate raw scores (0-100) per dimension
  4. Compute weighted total score

Step 4: Identify Gaps and Prioritize Actions

For each unmet criterion:

  1. Classify severity (Critical / Major / Minor)
  2. Estimate effort (Low / Medium / High)
  3. Prioritize by impact-to-effort ratio
  4. Generate actionable next steps

Step 5: Generate Report

Produce a comprehensive report in both JSON and Markdown formats.

python3 scripts/score_project.py \
  --template skill \
  --project-path ./skills/my-skill \
  --output-dir ./reports \
  --format both

Output Format

JSON Report

{
  "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"}
      ]
    }
  ],
  "gaps": [
    {
      "criterion": "At least 3 tests",
      "dimension": "Test Coverage",
      "severity": "major",
      "effort": "medium",
      "priority": 1,
      "action": "Add unit tests covering core scoring logic"
    }
  ],
  "summary": {
    "critical_gaps": 0,
    "major_gaps": 1,
    "minor_gaps": 2,
    "ready_for_release": false
  }
}

Markdown Report

# Project Completeness Report

**Project**: ./skills/my-skill
**Type**: skill
**Date**: 2026-03-16
**Overall Score**: 85/100

## Score Breakdown

| Dimension | Weight | Score | Weighted |
|-----------|--------|-------|----------|
| Functional Requirements | 30% | 90 | 27.0 |
| Quality Standards | 20% | 80 | 16.0 |
| Test Coverage | 25% | 70 | 17.5 |
| Documentation | 15% | 95 | 14.3 |
| Deployment Readiness | 10% | 100 | 10.0 |

## Priority Actions

1. **[Major]** Add unit tests covering core scoring logic
2. **[Minor]** Add inline code comments
3. **[Minor]** Create CHANGELOG.md

## Readiness Assessment

- Critical Gaps: 0
- Major Gaps: 1
- Minor Gaps: 2
- **Ready for Release**: No (resolve major gaps first)

Resources

  • scripts/score_project.py -- Main scoring script with CLI interface
  • references/scoring-methodology.md -- Detailed scoring rules and dimension definitions
  • references/project-templates.md -- Evaluation templates for different project types

Key Principles

  1. Weighted Scoring -- Different dimensions have different importance based on project type
  2. Actionable Gaps -- Every gap includes a specific action to resolve it
  3. Priority Ranking -- Gaps are ranked by impact-to-effort ratio
  4. Template-Driven -- Flexible templates for different project types
  5. Reproducible -- Same inputs always produce same scores

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