Agent Skill Grading Report: mastering-postgresql - Score 100/100 (A)

# 🏆 Agent Skill Grading Report

**Score:** 100/100 | **Grade:** A

## Quick Summary of Agent Skill Grades

### Pillar Scores for Agent Skill

| Pillar | Score | Max |
|--------|-------|-----|
| Spec Compliance | 14 | 15 |
| Progressive Disclosure | 28 | 30 |
| Ease of Use | 24 | 25 |
| Writing Style | 9 | 10 |
| Utility | 18 | 20 |
| Modifiers | +8 | ±15 |

### Issues Found: 2
- 🔴 High: 0
- 🟡 Medium: 1
- 🟢 Low: 1

## 📊 Full Grading Report for Agent Skill

# Skill Evaluation Report: mastering-postgresql

**Links:**
- 📁 **GitHub:** [SpillwaveSolutions/mastering-postgresql-agent-skill](https://github.com/SpillwaveSolutions/mastering-postgresql-agent-skill/blob/main/mastering-postgresql/SKILL.md)
- 🛒 **Marketplace:** [View on SkillzWave](https://skillzwave.ai/skill/spillwavesolutions__mastering-postgresql-agent-skill__mastering-postgresql__SKILL/)
- 📤 **Submit Skill:** [Submit to SkillzWave](https://skillzwave.ai/submit/) - Skills are ranked, graded, and scanned for security
- 📊 **Tracking:** [All Graded Skills](https://github.com/SpillwaveSolutions/skill_improver/issues)

**Evaluated:** 2026-01-12
**Files Reviewed:** mastering-postgresql/SKILL.md, references/cloud-serverless.md, references/setup-and-docker.md, references/cloud-gcp.md, references/cloud-common.md, references/python-drivers.md, references/search-fulltext.md, references/cloud-azure.md, references/cloud-aws.md, references/search-vectors-json.md, references/python-queries.md
**Grading Model:** Claude (default) (via claude)

---

## Overall Score: 100/100

| Pillar | Score | Max |
|--------|-------|-----|
| Progressive Disclosure Architecture | 28 | 30 |
| Ease of Use | 24 | 25 |
| Spec Compliance | 14 | 15 |
| Writing Style | 9 | 10 |
| Utility | 18 | 20 |
| Modifiers | +8 | ±15 |

**Grade: A**

---

## Executive Summary

This skill demonstrates excellent quality with a score of 100/100. Strongest area: **Ease of Use** (24/25).

---

## Detailed Scores

### Progressive Disclosure Architecture (28/30)

| Criterion | Score | Max | Assessment |
|-----------|-------|-----|------------|
| Token Economy | 9 | 10 | Concise SKILL.md with essential quick-start; detailed content properly delegated to references; decision trees replace verbose explanations. |
| Layered Structure | 9 | 10 | Excellent hierarchy: 320-line SKILL.md overview → 10 focused reference files (300-550 lines each) covering setup, search, vectors, Python, and cloud. |
| Reference Depth | 5 | 5 | All references exactly one level deep; inter-reference links (Related References sections) exist but don't create nested dependencies. |
| Navigation Signals | 5 | 5 | Every reference file has Contents TOC; SKILL.md has Quick Reference table mapping tasks to files with anchor links. |

### Ease of Use (24/25)

| Criterion | Score | Max | Assessment |
|-----------|-------|-----|------------|
| Metadata Quality | 10 | 10 | Name follows conventions; description includes specific triggers (pgvector, asyncpg, BM25) and explicit exclusions (DBA, stored procedures). |
| Discoverability | 6 | 6 | Excellent trigger list in description; clear 'When NOT to Use' section; decision trees guide feature selection. |
| Terminology Consistency | 4 | 4 | Consistent terms throughout: 'search_vector' for tsvector, 'embedding' for vectors; Python library names used consistently. |
| Workflow Clarity | 4 | 5 | Quick Start Checklist provided; numbered steps with verification commands; decision trees for approach selection; minor: some checklists could be more explicit. |

### Spec Compliance (14/15)

| Criterion | Score | Max | Assessment |
|-----------|-------|-----|------------|
| Frontmatter Validity | 5 | 5 | Valid YAML with required fields |
| Name Conventions | 4 | 4 | Correct hyphen-case format |
| Description Quality | 4 | 4 | Third-person with good trigger coverage |
| Optional Fields | 1 | 2 | Uses allowed-tools |

### Writing Style (9/10)

| Criterion | Score | Max | Assessment |
|-----------|-------|-----|------------|
| Voice And Tense | 4 | 4 | Imperative form used throughout ('Create', 'Enable', 'Use'); no second-person pronouns; consistent technical voice. |
| Objectivity | 3 | 3 | Pure technical instruction; no marketing language; comparative tables present facts without bias. |
| Conciseness | 2 | 3 | Generally dense and efficient; some verification comments slightly verbose; occasional explanatory text could be trimmed. |

### Utility (18/20)

| Criterion | Score | Max | Assessment |
|-----------|-------|-----|------------|
| Problem Solving Power | 7 | 8 | Addresses real gaps: pgvector setup, hybrid search patterns, cloud deployment; covers practical edge cases like filtered vector queries. |
| Degrees Of Freedom | 5 | 5 | Appropriate constraints via decision trees; provides options (HNSW vs IVFFlat, psycopg vs asyncpg) with clear guidance. |
| Feedback Loops | 4 | 4 | Verification queries after each SQL step; EXPLAIN patterns for debugging; troubleshooting tables with symptom→cause→fix. |
| Examples And Templates | 2 | 3 | Good code examples with input/output patterns; docker-compose templates provided; could benefit from more complete example apps. |

### Modifiers Applied (+8)

**Penalties:** deeply_nested_references (-2)
**Bonuses:** self_documenting_scripts (+2), copy_paste_checklists (+2), grep_friendly_structure (+1), exemplary_examples (+2), explicit_scope_boundaries (+1), trigger_phrases_4plus (+1), gerund_style_name (+1)

---

## Critical Issues (Top 2)

### Issue 1: Missing script files

**Severity:** Medium
**Location:** SKILL.md:Script Usage
**Pillar Affected:** Utility

**Problem:** SKILL.md references scripts/ directory with 7 utility scripts (setup_extensions.py, health_check.py, etc.) but no scripts/ directory exists in the skill package.

**Current:**
```
pip install -r scripts/requirements.txt
```

**Suggested Rewrite:**
```
Either add the referenced scripts/ directory with working utilities, or remove the Script Usage section to avoid confusion.
```

**Impact:** +1-2 points Utility

---

### Issue 2: Verification sections slightly verbose

**Severity:** Low
**Location:** references/*:verification comments
**Pillar Affected:** Writing Style

**Problem:** Some verification comments use full sentences where terse output expectations would suffice.

**Current:**
```
-- Expected: 2 rows with version numbers
```

**Suggested Rewrite:**
```
-- Returns: 2 rows (version numbers)
```

**Impact:** +0.5 points Conciseness

---

## General Recommendations

1. Add trigger phrases to description for discoverability
2. Add table of contents for files over 100 lines

---

## Grade Scale

| Grade | Score | Description |
|-------|-------|-------------|
| A | 90-100 | Production-ready |
| B | 80-89 | Good, minor work |
| C | 70-79 | Adequate, gaps |
| D | 60-69 | Needs work |
| F | <60 | Major revision |

---

---

## About This Report

This evaluation uses the [Claude Skills Best Practices](https://platform.claude.com/docs/en/agents-and-tools/agent-skills/best-practices).

**Powered by:**
- [SkillzWave](https://skillzwave.ai) - Claude Skills Marketplace
- [SpillWave](https://spillwave.com) - AI Solutions

*Report generated for [SpillwaveSolutions/mastering-postgresql-agent-skill](https://github.com/SpillwaveSolutions/mastering-postgresql-agent-skill/blob/main/mastering-postgresql/SKILL.md)*

## JSON Output

```json
{
  "skill_name": "mastering-postgresql",
  "evaluated_at": "2026-01-12T20:36:54.645111",
  "files_reviewed": [
    "mastering-postgresql/SKILL.md",
    "references/cloud-serverless.md",
    "references/setup-and-docker.md",
    "references/cloud-gcp.md",
    "references/cloud-common.md",
    "references/python-drivers.md",
    "references/search-fulltext.md",
    "references/cloud-azure.md",
    "references/cloud-aws.md",
    "references/search-vectors-json.md",
    "references/python-queries.md"
  ],
  "scores": {
    "spec_compliance": {
      "total": 14,
      "max": 15,
      "breakdown": {
        "frontmatter_validity": {
          "score": 5,
          "max": 5,
          "assessment": "Valid YAML with required fields"
        },
        "name_conventions": {
          "score": 4,
          "max": 4,
          "assessment": "Correct hyphen-case format"
        },
        "description_quality": {
          "score": 4,
          "max": 4,
          "assessment": "Third-person with good trigger coverage"
        },
        "optional_fields": {
          "score": 1,
          "max": 2,
          "assessment": "Uses allowed-tools"
        }
      }
    },
    "pda": {
      "total": 28,
      "max": 30,
      "breakdown": {
        "token_economy": {
          "score": 9,
          "max": 10,
          "assessment": "Concise SKILL.md with essential quick-start; detailed content properly delegated to references; decision trees replace verbose explanations."
        },
        "layered_structure": {
          "score": 9,
          "max": 10,
          "assessment": "Excellent hierarchy: 320-line SKILL.md overview \u2192 10 focused reference files (300-550 lines each) covering setup, search, vectors, Python, and cloud."
        },
        "reference_depth": {
          "score": 5,
          "max": 5,
          "assessment": "All references exactly one level deep; inter-reference links (Related References sections) exist but don't create nested dependencies."
        },
        "navigation_signals": {
          "score": 5,
          "max": 5,
          "assessment": "Every reference file has Contents TOC; SKILL.md has Quick Reference table mapping tasks to files with anchor links."
        }
      }
    },
    "ease_of_use": {
      "total": 24,
      "max": 25,
      "breakdown": {
        "metadata_quality": {
          "score": 10,
          "max": 10,
          "assessment": "Name follows conventions; description includes specific triggers (pgvector, asyncpg, BM25) and explicit exclusions (DBA, stored procedures)."
        },
        "discoverability": {
          "score": 6,
          "max": 6,
          "assessment": "Excellent trigger list in description; clear 'When NOT to Use' section; decision trees guide feature selection."
        },
        "terminology_consistency": {
          "score": 4,
          "max": 4,
          "assessment": "Consistent terms throughout: 'search_vector' for tsvector, 'embedding' for vectors; Python library names used consistently."
        },
        "workflow_clarity": {
          "score": 4,
          "max": 5,
          "assessment": "Quick Start Checklist provided; numbered steps with verification commands; decision trees for approach selection; minor: some checklists could be more explicit."
        }
      }
    },
    "writing_style": {
      "total": 9,
      "max": 10,
      "breakdown": {
        "voice_and_tense": {
          "score": 4,
          "max": 4,
          "assessment": "Imperative form used throughout ('Create', 'Enable', 'Use'); no second-person pronouns; consistent technical voice."
        },
        "objectivity": {
          "score": 3,
          "max": 3,
          "assessment": "Pure technical instruction; no marketing language; comparative tables present facts without bias."
        },
        "conciseness": {
          "score": 2,
          "max": 3,
          "assessment": "Generally dense and efficient; some verification comments slightly verbose; occasional explanatory text could be trimmed."
        }
      }
    },
    "utility": {
      "total": 18,
      "max": 20,
      "breakdown": {
        "problem_solving_power": {
          "score": 7,
          "max": 8,
          "assessment": "Addresses real gaps: pgvector setup, hybrid search patterns, cloud deployment; covers practical edge cases like filtered vector queries."
        },
        "degrees_of_freedom": {
          "score": 5,
          "max": 5,
          "assessment": "Appropriate constraints via decision trees; provides options (HNSW vs IVFFlat, psycopg vs asyncpg) with clear guidance."
        },
        "feedback_loops": {
          "score": 4,
          "max": 4,
          "assessment": "Verification queries after each SQL step; EXPLAIN patterns for debugging; troubleshooting tables with symptom\u2192cause\u2192fix."
        },
        "examples_and_templates": {
          "score": 2,
          "max": 3,
          "assessment": "Good code examples with input/output patterns; docker-compose templates provided; could benefit from more complete example apps."
        }
      }
    }
  },
  "modifiers": {
    "penalties": [
      {
        "name": "deeply_nested_references",
        "points": -2
      }
    ],
    "bonuses": [
      {
        "name": "self_documenting_scripts",
        "points": 2
      },
      {
        "name": "copy_paste_checklists",
        "points": 2
      },
      {
        "name": "grep_friendly_structure",
        "points": 1
      },
      {
        "name": "exemplary_examples",
        "points": 2
      },
      {
        "name": "explicit_scope_boundaries",
        "points": 1
      },
      {
        "name": "trigger_phrases_4plus",
        "points": 1
      },
      {
        "name": "gerund_style_name",
        "points": 1
      }
    ],
    "net": 8
  },
  "final_score": 100,
  "grade": "A",
  "critical_issues": [
    {
      "rank": 1,
      "title": "Missing script files",
      "severity": "Medium",
      "location": "SKILL.md:Script Usage",
      "pillar": "Utility",
      "problem": "SKILL.md references scripts/ directory with 7 utility scripts (setup_extensions.py, health_check.py, etc.) but no scripts/ directory exists in the skill package.",
      "current": "pip install -r scripts/requirements.txt",
      "suggested": "Either add the referenced scripts/ directory with working utilities, or remove the Script Usage section to avoid confusion.",
      "impact": "+1-2 points Utility"
    },
    {
      "rank": 2,
      "title": "Verification sections slightly verbose",
      "severity": "Low",
      "location": "references/*:verification comments",
      "pillar": "Writing Style",
      "problem": "Some verification comments use full sentences where terse output expectations would suffice.",
      "current": "-- Expected: 2 rows with version numbers",
      "suggested": "-- Returns: 2 rows (version numbers)",
      "impact": "+0.5 points Conciseness"
    }
  ],
  "recommendations": [
    "Add trigger phrases to description for discoverability",
    "Add table of contents for files over 100 lines"
  ],
  "code_quality": null,
  "grading_model": "Claude (default)",
  "grading_provider": "claude"
}
```

**Links:**
- 📁 **GitHub:** [spillwavesolutions/mastering-postgresql-agent-skill](https://github.com/spillwavesolutions/mastering-postgresql-agent-skill/blob/main/SKILL.md)
- 🛒 **Marketplace:** [View on SkillzWave](https://skillzwave.ai/skill/spillwavesolutions__mastering-postgresql-agent-skill__mastering-postgresql__SKILL/)
- 📤 **Submit Skill:** [Submit to SkillzWave](https://skillzwave.ai/submit/) - Skills are ranked, graded, and scanned for security
- 📊 **Tracking:** [All Graded Skills](https://github.com/SpillwaveSolutions/skill_improver/issues)

---

## 📦 Recommended: Add Universal Installer Instructions

Consider adding these installation instructions to your README.md to help users install this skill across 14+ AI coding agents:

```markdown
## Installing with Skilz (Universal Installer)

The recommended way to install this skill across different AI coding agents is using the **skilz** universal installer.

### Install Skilz

```bash
pip install skilz
```

This skill supports [Agent Skill Standard](https://agentskills.io/) which means it supports 14 plus coding agents including Claude Code, OpenAI Codex, Cursor and Gemini.


### Git URL Options

```

# Install for Claude Code (your home directory)
skilz install -g https://github.com/spillwavesolutions/mastering-postgresql-agent-skill

# Or from the SkillzWave marketplace
skilz install spillwavesolutions__mastering-postgresql-agent-skill__mastering-postgresql
```

### Claude Code

Install to user home (available in all projects):
```bash
skilz install -g https://github.com/spillwavesolutions/mastering-postgresql-agent-skill
```

Install to current project only:
```bash
skilz install -g https://github.com/spillwavesolutions/mastering-postgresql-agent-skill --project
```

### OpenCode

Install for [OpenCode](https://opencode.ai):

```bash

# OpenCode
skilz install https://github.com/spillwavesolutions/mastering-postgresql-agent-skill --agent opencode

```

Install for Codex and Gemini too
```
# Gemini CLI
skilz install https://github.com/spillwavesolutions/mastering-postgresql-agent-skill --agent gemini


# OpenAI Codex
skilz install https://github.com/spillwavesolutions/mastering-postgresql-agent-skill --agent codex

```

Project-level install:
```bash
skilz install https://github.com/spillwavesolutions/mastering-postgresql-agent-skill --project --agent codex
```


### Install from Skillzwave Marketplace
```bash
skilz install spillwavesolutions__mastering-postgresql-agent-skill__mastering-postgresql --project
```

See this site [skill Listing](https://skillzwave.ai/skill/spillwavesolutions__mastering-postgresql-agent-skill__mastering-postgresql__SKILL/) to see how to install this exact skill to 14+ different coding agents.


### Other Supported Agents

Skilz supports 20+ coding agents including Claude Code, OpenAI Codex, OpenCode, Cursor, Gemini CLI, GitHub Copilot CLI, Windsurf, Qwen Code, Aidr, and more.

See the [skill on SkillzWave](https://skillzwave.ai/skill/spillwavesolutions__mastering-postgresql-agent-skill__mastering-postgresql__SKILL/) for agent-specific install commands, or check the [skilz-cli docs](https://github.com/SpillwaveSolutions/skilz-cli).


[SkillzWave](https://skillzwave.ai) is a skill marketplace for AI agents. [SpillWave](https://spillwave.com) (where I work) builds AI agent tools.
```

---

## About This Report

This evaluation uses the [Claude Skills Best Practices](https://platform.claude.com/docs/en/agents-and-tools/agent-skills/best-practices).

**Powered by:**
- [SkillzWave](https://skillzwave.ai) - Claude Skills Marketplace
- [SpillWave](https://spillwave.com) - AI Solutions

*Report generated for [spillwavesolutions/mastering-postgresql-agent-skill](https://github.com/spillwavesolutions/mastering-postgresql-agent-skill/blob/main/SKILL.md)*

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Agent Skill Grading Report: mastering-postgresql - Score 100/100 (A) #6

🏆 Agent Skill Grading Report

Quick Summary of Agent Skill Grades

Pillar Scores for Agent Skill

Issues Found: 2

📊 Full Grading Report for Agent Skill

Skill Evaluation Report: mastering-postgresql

Overall Score: 100/100

Executive Summary

Detailed Scores

Progressive Disclosure Architecture (28/30)

Ease of Use (24/25)

Spec Compliance (14/15)

Writing Style (9/10)

Utility (18/20)

Modifiers Applied (+8)

Critical Issues (Top 2)

Issue 1: Missing script files

Issue 2: Verification sections slightly verbose

General Recommendations

Grade Scale

About This Report

JSON Output

📦 Recommended: Add Universal Installer Instructions

Git URL Options

Claude Code

OpenCode

Install from Skillzwave Marketplace

Other Supported Agents

Metadata

Assignees

Labels

Type

Projects

Milestone

Relationships

Development

Pillar	Score	Max
Spec Compliance	14	15
Progressive Disclosure	28	30
Ease of Use	24	25
Writing Style	9	10
Utility	18	20
Modifiers	+8	±15

Pillar	Score	Max
Progressive Disclosure Architecture	28	30
Ease of Use	24	25
Spec Compliance	14	15
Writing Style	9	10
Utility	18	20
Modifiers	+8	±15

Criterion	Score	Max	Assessment
Token Economy	9	10	Concise SKILL.md with essential quick-start; detailed content properly delegated to references; decision trees replace verbose explanations.
Layered Structure	9	10	Excellent hierarchy: 320-line SKILL.md overview → 10 focused reference files (300-550 lines each) covering setup, search, vectors, Python, and cloud.
Reference Depth	5	5	All references exactly one level deep; inter-reference links (Related References sections) exist but don't create nested dependencies.
Navigation Signals	5	5	Every reference file has Contents TOC; SKILL.md has Quick Reference table mapping tasks to files with anchor links.

Criterion	Score	Max	Assessment
Metadata Quality	10	10	Name follows conventions; description includes specific triggers (pgvector, asyncpg, BM25) and explicit exclusions (DBA, stored procedures).
Discoverability	6	6	Excellent trigger list in description; clear 'When NOT to Use' section; decision trees guide feature selection.
Terminology Consistency	4	4	Consistent terms throughout: 'search_vector' for tsvector, 'embedding' for vectors; Python library names used consistently.
Workflow Clarity	4	5	Quick Start Checklist provided; numbered steps with verification commands; decision trees for approach selection; minor: some checklists could be more explicit.

Criterion	Score	Max	Assessment
Frontmatter Validity	5	5	Valid YAML with required fields
Name Conventions	4	4	Correct hyphen-case format
Description Quality	4	4	Third-person with good trigger coverage
Optional Fields	1	2	Uses allowed-tools

Criterion	Score	Max	Assessment
Voice And Tense	4	4	Imperative form used throughout ('Create', 'Enable', 'Use'); no second-person pronouns; consistent technical voice.
Objectivity	3	3	Pure technical instruction; no marketing language; comparative tables present facts without bias.
Conciseness	2	3	Generally dense and efficient; some verification comments slightly verbose; occasional explanatory text could be trimmed.

Criterion	Score	Max	Assessment
Problem Solving Power	7	8	Addresses real gaps: pgvector setup, hybrid search patterns, cloud deployment; covers practical edge cases like filtered vector queries.
Degrees Of Freedom	5	5	Appropriate constraints via decision trees; provides options (HNSW vs IVFFlat, psycopg vs asyncpg) with clear guidance.
Feedback Loops	4	4	Verification queries after each SQL step; EXPLAIN patterns for debugging; troubleshooting tables with symptom→cause→fix.
Examples And Templates	2	3	Good code examples with input/output patterns; docker-compose templates provided; could benefit from more complete example apps.

Grade	Score	Description
A	90-100	Production-ready
B	80-89	Good, minor work
C	70-79	Adequate, gaps
D	60-69	Needs work
F	<60	Major revision

Agent Skill Grading Report: mastering-postgresql - Score 100/100 (A) #6

Description

🏆 Agent Skill Grading Report

Quick Summary of Agent Skill Grades

Pillar Scores for Agent Skill

Issues Found: 2

📊 Full Grading Report for Agent Skill

Skill Evaluation Report: mastering-postgresql

Overall Score: 100/100

Executive Summary

Detailed Scores

Progressive Disclosure Architecture (28/30)

Ease of Use (24/25)

Spec Compliance (14/15)

Writing Style (9/10)

Utility (18/20)

Modifiers Applied (+8)

Critical Issues (Top 2)

Issue 1: Missing script files

Issue 2: Verification sections slightly verbose

General Recommendations

Grade Scale

About This Report

JSON Output

📦 Recommended: Add Universal Installer Instructions

Git URL Options

Claude Code

OpenCode

Install from Skillzwave Marketplace

Other Supported Agents

Metadata

Metadata

Assignees

Labels

Type

Projects

Milestone

Relationships

Development

Issue actions