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Content Audit

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Donchitos
content-audit

Audit GDD content counts against what's implemented — planned vs built.

Overview

PublisherDonchitos
RepositoryClaude-Code-Game-Studios
Skill namecontent-audit
Stars
25.8K
Forks
3.7K
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by Donchitos on GitHub. Read the source before you install it.

Installation

Install the Content Audit AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Donchitos/Claude-Code-Game-Studios/tree/main/.claude/skills/content-audit
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Donchitos/Claude-Code-Game-Studios.git /tmp/Claude-Code-Game-Studios
mkdir -p .claude/skills
cp -r /tmp/Claude-Code-Game-Studios/.claude/skills/content-audit .claude/skills/content-audit
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Content Audit in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Content Audit on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Content Audit is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

!bash "${CLAUDE_SKILL_DIR}/../../hooks/yaml-helper.sh" resolve_config --keys workflow,automation

Automation mode: Resolve modes.automation (project.local.yaml → project.yaml → default collaborative). Every AskUserQuestion call and every file write follows .claude/docs/automation-modes.md (collaborative asks always · guided major-only · autonomous logs and proceeds; automation_always_ask categories always prompt).

Resolved above — use as-is. No block → defaults in .claude/docs/config-resolution.md.

This skill declares tier-dependent behaviour ("audit only specs in the required sections"), which is unreachable unless the tier is actually resolved — without it the skill applies whatever tier it assumed. Resolve modes.workflow before auditing, and when a required input such as design/gdd/systems-index.md is absent, report NOT ASSESSED — NO DATA rather than computing a gap percentage (which divides by zero when nothing is specified).

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

workflow (see .claude/docs/workflow-modes.md):

  • full — full content-count audit against all GDD specs.
  • standard — audit only specs in the required sections; list any count found outside them as not audited at this tier.
  • minimal — cannot run (no systems index exists).

Insufficient input — check this before producing any report

If the inputs this skill needs do not exist, the answer is "could not run" — not a filled-in report. Check first, and stop if the check fails.

  1. List the inputs this skill reads (data files, prior reports, profiler output, test results, registries, source code).
  2. For each, record FOUND or ABSENT — not "assumed present".
  3. If any input required for a section is ABSENT, that section is NOT ASSESSED — NO DATA. Do not estimate it, do not infer it from an adjacent artifact, and do not leave a mandated cell to be filled by whoever reads the template next.
  4. If every required input is ABSENT, stop and report NOT ASSESSED — NO DATA as the whole verdict, naming what was missing and which skill produces it (/map-systems for design/gdd/systems-index.md, /design-system for the GDDs).

A verdict of NOT ASSESSED is a success. It is the correct, useful answer to "what does the data say?" when there is no data. The failure mode this prevents is specific and has been observed in practice: report templates whose verdict enum had no "could not run" state produced false clean passes — an asset audit returning COMPLIANT on a project with no assets and no standards, and a performance profile reporting ">99% headroom against a 16.67ms budget" with zero profiler data and no budget ever set.

Absence of evidence is never evidence of absence. A scan that finds no matches because there are no files to scan has not verified anything. Say which of the two happened — a reader cannot tell from a green result.


Phase 1 — Context Gathering

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

  2. Registry-first count: If design/registry/entities.yaml exists, read it first. Its entities and items sections are the cross-GDD named content the audit is counting, already distilled with a source: GDD per entry:

    Grep pattern="^  - name:" path="design/registry/entities.yaml" output_mode="content" -A 2

    - name: appears in all four registry sections; keep only the names whose -A 2 context shows they sit under entities: or items: (the named content this audit counts — not formulas:/constants:). Use those as the planned named-content set without full-reading the GDDs that own them. If design/registry/entities.yaml does not exist or has no entries (it ships as an empty stub until /design-system populates it), skip this step and rely on the GDD scan below — behaviour is unchanged, only more expensive.

  3. L0 pre-scan: Before full-reading any GDDs, run two greps — they answer different questions and must not share a pattern:

    a. Which GDDs carry a Summary (the denominator / fail-open check):

    Grep pattern="^## Summary" glob="design/gdd/*.md" output_mode="files_with_matches"

    b. Which GDDs actually declare content counts (the narrowing step):

    Grep pattern="[0-9]+[[:space:]]+(enemies|enemy types|levels|areas|maps|stages|items|weapons|equipment|abilities|skills|spells|cutscenes|conversations|dialogue scenes|bosses|quests)|(enemy|item|weapon|ability|level) types:" glob="design/gdd/*.md" output_mode="files_with_matches"

    Keep the two scans separate. ## Summary matches every compliant GDD, so a union with it narrows nothing, and a literal placeholder such as N enemies matches no real GDD. Pattern (b) matches digits followed by the content nouns step 5 actually extracts.

    Fail open on a missing Summary. Establish the denominator: glob design/gdd/*.md and count N. Scan (a) matching fewer than N means those GDDs predate ## Summary — never treat an absent Summary as a system out of scope; a zero-match (a) means "no GDD carries a Summary yet", not "no auditable content". Full-read the unmatched in-scope GDDs.

    For a single-system audit: skip this step and go straight to full-read. For a full audit: full-read the GDDs that scan (b) matched and are not already fully covered by the registry entries from step 2, plus every GDD scan (a) missed — the fail-open rule wins over this narrowing: a GDD with no ## Summary is full-read even when scan (b) did not match it. Only a GDD that has a Summary and no content-count language (a pure mechanics GDD) is noted as "No auditable content counts" without a full read.

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

  5. For each GDD, extract explicit content counts or lists, noting the ## section each sits under — at standard, a count outside the required sections (.claude/docs/workflow-modes.md) goes on the "Not audited at this tier" list, never into the inventory. 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)
  6. 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.

On Unreal, content lives in Content/ — maps and data assets both — and the code root (Source/<Module>/) holds only C++. So on Unreal, every assets/… glob below also runs under Content/ (*.uasset in place of the data extensions), and levels are Content/**/*.umap. Skipping this finds zero levels on every Unreal project and reports them NOT STARTED.

On Unity, content lives under Assets/ (compare the case exactly — .claude/docs/code-root-resolution.md), and rarely in a data/ folder. So on Unity every assets/… glob below also runs under Assets/, without requiring a data/ segment — for items, Assets/**/items/**, Assets/**/Items/** and Assets/**/*Item*.asset — and data files include .asset (ScriptableObjects) and .prefab. Skipping this finds zero items, abilities, quests and dialogue on a Unity project and reports them NOT STARTED.

Levels / Areas / Maps:

  • Glob assets/**/*.tscn, assets/**/*.unity, assets/**/*.umap
  • Glob the code root for scene files: *.tscn (Godot), *.unity (Unity); on Unreal, Content/**/*.umap instead (above). Resolve the root per .claude/docs/code-root-resolution.md. If the code root is unresolved, report NOT ASSESSED — code root unresolved rather than zero hits.
  • 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 <code root>/**/enemies/**, <code root>/**/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

A row whose Found is NOT ASSESSED (code root unresolved) has no Gap or Status, is never NOT STARTED, and stays out of both totals; the summary names it.


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:

markdown
# 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] — [all GDD sections | required sections only (workflow: standard)]
- **Not audited at this tier**: [counts found only outside the required sections — or "none"; omit at `full`]

> 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 any system is flagged HIGH PRIORITY → "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/."

Close with the verdict that matches the run:

  • Verdict: COMPLETE — content audit finished.
  • Verdict: NOT ASSESSED — NO DATA — a required input was absent (see the note at the top), nothing in scope gives a count (no registry entry, and every GDD is "Unspecified" or "No auditable content counts"), or a Found count could not be taken (NOT ASSESSED — code root unresolved); name which, and for missing counts name /design-system. Never print COMPLETE for a run that could not compute a gap.

Frequently asked questions

What does the Content Audit AI skill do?

Audit GDD content counts against what's implemented — planned vs built.

Why use Content Audit on TypingMind?

Because you install it once and use it with any model. Content Audit is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Content Audit in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Donchitos/Claude-Code-Game-Studios/tree/main/.claude/skills/content-audit. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Content Audit?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Content Audit?

As many as you like. As long as a model supports skills, you can use Content Audit with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Content Audit AI skill free?

Yes. It is published on GitHub by Donchitos under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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