Pop Assessment Anthropic

Pop Assessment: Anthropic Engineer

Validates PopKit compliance with official Claude Code patterns, hook protocols, and Anthropic engineering best practices using concrete standards and automated validation.

How This Skill Works

Unlike prose-based assessments, this skill uses:

1. Machine-Readable Standards - JSON schemas defining exact requirements
2. Automated Validation Scripts - Python scripts that programmatically check compliance
3. Consistent Scoring - Same input = same output, every time

Invocation

When invoked, follow this process:

Step 1: Run Automated Checks

Execute the validation scripts in order:

# From packages/plugin directory
python skills/pop-assessment-anthropic/scripts/validate_plugin_structure.py
python skills/pop-assessment-anthropic/scripts/validate_hooks.py
python skills/pop-assessment-anthropic/scripts/validate_routing.py
python skills/pop-assessment-anthropic/scripts/calculate_score.py

Step 2: Read Standards (if manual review needed)

If automated checks identify issues requiring context:

Read: skills/pop-assessment-anthropic/standards/hook-protocol.md
Read: skills/pop-assessment-anthropic/standards/plugin-schema.md
Read: skills/pop-assessment-anthropic/standards/agent-routing.md
Read: skills/pop-assessment-anthropic/standards/progressive-disclosure.md

Step 3: Load Checklists

For comprehensive review:

Read: skills/pop-assessment-anthropic/checklists/claude-code-compliance.json
Read: skills/pop-assessment-anthropic/checklists/hook-patterns.json
Read: skills/pop-assessment-anthropic/checklists/blog-practices.json

Step 4: Generate Report

Use the output style assessment-report and include:

1. Automated Results - From script execution
2. Manual Findings - From checklist review
3. Score Calculation - Using calculate_score.py output
4. Recommendations - Prioritized by severity

Standards Directory

Checklists Directory

Scripts Directory

Scoring System

Each check has a severity and point deduction:

Starting Score: 100 Minimum Score: 0

Pass/Fail Criteria

Example Output

# Anthropic Engineer Assessment Report

**Assessed:** PopKit Plugin v0.2.0
**Date:** 2025-12-12
**Score:** 87/100 (GOOD)

## Automated Check Results

### Plugin Structure (validate_plugin_structure.py)
| Check | Status | Details |
|-------|--------|---------|
| plugin.json schema | PASS | All required fields present |
| hooks.json schema | PASS | Valid event types |
| .mcp.json valid | PASS | Schema reference included |

### Hook Protocol (validate_hooks.py)
| Hook | stdin | stdout | error_handling | Status |
|------|-------|--------|----------------|--------|
| pre-tool-use.py | PASS | PASS | PASS | PASS |
| post-tool-use.py | PASS | PASS | PASS | PASS |
| session-start.py | PASS | PASS | WARN | WARN |

### Routing Coverage (validate_routing.py)
| Category | Coverage | Missing |
|----------|----------|---------|
| Keywords | 95% | 2 agents |
| File Patterns | 90% | yaml, yml |
| Error Patterns | 85% | ImportError |

## Score Breakdown

| Category | Max | Earned | Deductions |
|----------|-----|--------|------------|
| Plugin Structure | 25 | 25 | 0 |
| Hook Protocol | 30 | 28 | -2 (1 warning) |
| Agent Routing | 25 | 21 | -4 (medium issues) |
| Progressive Disclosure | 20 | 13 | -7 (2 medium) |
| **Total** | **100** | **87** | **-13** |

## Recommendations

1. **HIGH**: Add error patterns for ImportError, ConnectionError
2. **MEDIUM**: Add file patterns for *.yaml, *.yml
3. **LOW**: session-start.py error handling could be improved

Why This Approach

1. Reproducible - Scripts produce same results every time
2. Objective - JSON checklists remove interpretation
3. Fast - Automated checks run in seconds
4. Actionable - Specific file:line references
5. Versionable - Standards tracked in git

Related

• Agent: agents/assessors/anthropic-engineer/AGENT.md
• Output Style: output-styles/assessment-report.md
• Other Assessments: security, performance, ux, architect, docs