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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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설치 방법 보기

포함된 파일(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(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

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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