content-audit

Donchitos/Claude-Code-Game-Studios · updated Apr 16, 2026

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$npx skills add https://github.com/Donchitos/Claude-Code-Game-Studios --skill content-audit
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summary

### Content Audit

  • description: "Audit GDD-specified content counts against implemented content. Identifies what's planned vs built."
  • argument-hint: "[system-name | --summary | (no arg = full audit)]"
  • allowed-tools: Read, Glob, Grep, Write
skill.md
name
content-audit
description
"Audit GDD-specified content counts against implemented content. Identifies what's planned vs built."
argument-hint
"[system-name | --summary | (no arg = full audit)]"
user-invocable
true
allowed-tools
Read, Glob, Grep, Write
agent
producer

When this skill is invoked:

Parse the argument:

  • No argument → full audit across all systems
  • [system-name] → audit that single system only
  • --summary → summary table only, no file write

Phase 1 — Context Gathering

  1. Read design/gdd/systems-index.md for the full list of systems, their categories, and MVP/priority tier.

  2. L0 pre-scan: Before full-reading any GDDs, Grep all GDD files for ## Summary sections plus common content-count keywords:

    Grep pattern="(## Summary|N enemies|N levels|N items|N abilities|enemy types|item types)" glob="design/gdd/*.md" output_mode="files_with_matches"
    

    For a single-system audit: skip this step and go straight to full-read. For a full audit: full-read only the GDDs that matched content-count keywords. GDDs with no content-count language (pure mechanics GDDs) are noted as "No auditable content counts" without a full read.

  3. Full-read in-scope GDD files (or the single system GDD if a system name was given).

  4. For each GDD, extract explicit content counts or lists. Look for patterns like:

    • "N enemies" / "enemy types:" / list of named enemies
    • "N levels" / "N areas" / "N maps" / "N stages"
    • "N items" / "N weapons" / "N equipment pieces"
    • "N abilities" / "N skills" / "N spells"
    • "N dialogue scenes" / "N conversations" / "N cutscenes"
    • "N quests" / "N missions" / "N objectives"
    • Any explicit enumerated list (bullet list of named content pieces)
  5. Build a content inventory table from the extracted data:

    SystemContent TypeSpecified Count/ListSource GDD

    Note: If a GDD describes content qualitatively but gives no count, record "Unspecified" and flag it — unspecified counts are a design gap worth noting.


Phase 2 — Implementation Scan

For each content type found in Phase 1, scan the relevant directories to count what has been implemented. Use Glob and Grep to locate files.

Levels / Areas / Maps:

  • Glob assets/**/*.tscn, assets/**/*.unity, assets/**/*.umap
  • Glob src/**/*.tscn, src/**/*.unity
  • Look for scene files in subdirectories named levels/, areas/, maps/, worlds/, stages/
  • Count unique files that appear to be level/scene definitions (not UI scenes)

Enemies / Characters / NPCs:

  • Glob assets/data/**/enemies/**, assets/data/**/characters/**
  • Glob src/**/enemies/**, src/**/characters/**
  • Look for .json, .tres, .asset, .yaml data files defining entity stats
  • Look for scene/prefab files in character subdirectories

Items / Equipment / Loot:

  • Glob assets/data/**/items/**, assets/data/**/equipment/**, assets/data/**/loot/**
  • Look for .json, .tres, .asset data files

Abilities / Skills / Spells:

  • Glob assets/data/**/abilities/**, assets/data/**/skills/**, assets/data/**/spells/**
  • Look for .json, .tres, .asset data files

Dialogue / Conversations / Cutscenes:

  • Glob assets/**/*.dialogue, assets/**/*.csv, assets/**/*.ink
  • Grep for dialogue data files in assets/data/

Quests / Missions:

  • Glob assets/data/**/quests/**, assets/data/**/missions/**
  • Look for .json, .yaml definition files

Engine-specific notes (acknowledge in the report):

  • Counts are approximations — the skill cannot perfectly parse every engine format or distinguish editor-only files from shipped content
  • Scene files may include both gameplay content and system/UI scenes; the scan counts all matches and notes this caveat

Phase 3 — Gap Report

Produce the gap table:

| System | Content Type | Specified | Found | Gap | Status |
|--------|-------------|-----------|-------|-----|--------|

Status categories:

  • COMPLETE — Found ≥ Specified (100%+)
  • IN PROGRESS — Found is 50–99% of Specified
  • EARLY — Found is 1–49% of Specified
  • NOT STARTED — Found is 0

Priority flags: Flag a system as HIGH PRIORITY in the report if:

  • Status is NOT STARTED or EARLY, AND
  • The system is tagged MVP or Vertical Slice in the systems index, OR
  • The systems index shows the system is blocking downstream systems

Summary line:

  • Total content items specified (sum of all Specified column values)
  • Total content items found (sum of all Found column values)
  • Overall gap percentage: (Specified - Found) / Specified * 100

Phase 4 — Output

Full audit and single-system modes

Present the gap table and summary to the user. Ask: "May I write the full report to docs/content-audit-[YYYY-MM-DD].md?"

If yes, write the file:

# Content Audit — [Date]

## Summary
- **Total specified**: [N] content items across [M] systems
- **Total found**: [N]
- **Gap**: [N] items ([X%] unimplemented)
- **Scope**: [Full audit | System: name]

> Note: Counts are approximations based on file scanning.
> The audit cannot distinguish shipped content from editor/test assets.
> Manual verification is recommended for any HIGH PRIORITY gaps.

## Gap Table

| System | Content Type | Specified | Found | Gap | Status |
|--------|-------------|-----------|-------|-----|--------|

## HIGH PRIORITY Gaps

[List systems flagged HIGH PRIORITY with rationale]

## Per-System Breakdown

### [System Name]
- **GDD**: `design/gdd/[file].md`
- **Content types audited**: [list]
- **Notes**: [any caveats about scan accuracy for this system]

## Recommendation

Focus implementation effort on:
1. [Highest-gap HIGH PRIORITY system]
2. [Second system]
3. [Third system]

## Unspecified Content Counts

The following GDDs describe content without giving explicit counts.
Consider adding counts to improve auditability:
[List of GDDs and content types with "Unspecified"]

After writing the report, ask:

"Would you like to create backlog stories for any of the content gaps?"

If yes: for each system the user selects, suggest a story title and point them to /create-stories [epic-slug] or /quick-design depending on the size of the gap.

--summary mode

Print the Gap Table and Summary directly to conversation. Do not write a file. End with: "Run /content-audit without --summary to write the full report."


Phase 5 — Next Steps

After the audit, recommend the highest-value follow-up actions:

  • If any system is NOT STARTED and MVP-tagged → "Run /design-system [name] to add missing content counts to the GDD before implementation begins."
  • If total gap is >50% → "Run /sprint-plan to allocate content work across upcoming sprints."
  • If backlog stories are needed → "Run /create-stories [epic-slug] for each HIGH PRIORITY gap."
  • If --summary was used → "Run /content-audit (no flag) to write the full report to docs/."

Verdict: COMPLETE — content audit finished.

how to use content-audit

How to use content-audit on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add content-audit
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/Donchitos/Claude-Code-Game-Studios --skill content-audit

The skills CLI fetches content-audit from GitHub repository Donchitos/Claude-Code-Game-Studios and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/content-audit

Reload or restart Cursor to activate content-audit. Access the skill through slash commands (e.g., /content-audit) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases

Task Automation & Efficiency

Automate repetitive workflows and reduce manual effort

Example

Generate reports, summarize documents, draft communications

Save 3-5 hours per week on routine tasks

Knowledge Enhancement

Learn new skills, understand complex topics, get expert guidance

Example

Explain concepts, provide examples, suggest learning resources

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

Improve work quality by 30-40% with less effort

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client with skill support
  • Clear understanding of task or problem to solve
  • Willingness to iterate and refine outputs

Time Estimate

15-45 minutes depending on use case complexity

Installation Steps

  1. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 5.Integrate into regular workflow if valuable

Common Pitfalls

  • Expecting perfect results without iteration
  • Not providing enough context in prompts
  • Using skill for tasks outside its intended scope
  • Accepting outputs without review and validation

Best Practices

✓ Do

  • +Start with clear, specific prompts
  • +Provide relevant context and constraints
  • +Review and refine all outputs before using
  • +Iterate to improve output quality
  • +Document successful prompt patterns

✗ Don't

  • Don't use without understanding skill limitations
  • Don't skip validation of outputs
  • Don't share sensitive information in prompts
  • Don't expect skill to replace human judgment

💡 Pro Tips

  • Be specific about desired format and style
  • Ask for multiple options to choose from
  • Request explanations to understand reasoning
  • Combine AI efficiency with human expertise

When to Use This

✓ Use When

Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.

✗ Avoid When

Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.

Learning Path

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.668 reviews
  • Ava Sharma· Dec 28, 2024

    content-audit has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Mia Li· Dec 28, 2024

    Useful defaults in content-audit — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Soo Diallo· Dec 20, 2024

    content-audit reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Evelyn Yang· Dec 8, 2024

    We added content-audit from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Nikhil Bhatia· Dec 4, 2024

    Solid pick for teams standardizing on skills: content-audit is focused, and the summary matches what you get after install.

  • Nikhil Chawla· Nov 27, 2024

    Registry listing for content-audit matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Evelyn Martin· Nov 27, 2024

    Solid pick for teams standardizing on skills: content-audit is focused, and the summary matches what you get after install.

  • Kofi Martin· Nov 23, 2024

    We added content-audit from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Ava Haddad· Nov 19, 2024

    content-audit fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Nia Bhatia· Nov 19, 2024

    content-audit is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

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