Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/frabcd/codex-ai-game-studio/reverse-documentnpx skills add frabcd/codex-ai-game-studio --skill reverse-documentgit clone --depth 1 https://github.com/frabcd/codex-ai-game-studioWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/frabcd/codex-ai-game-studio/reverse-document)<a href="https://agentmods.dev/skills/frabcd/codex-ai-game-studio/reverse-document"><img src="https://agentmods.dev/badge/skills/frabcd/codex-ai-game-studio/reverse-document.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00024 | $0.02191 |
| Opus 5 | $0.00012 | $0.01095 |
| Sonnet 5 | $0.00005 | $0.00438 |
| Haiku 4.5 | $0.00002 | $0.00219 |
Grade A, and why
reverse-document scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
83% identical to reverse-document — 25 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 265 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Port provenance: adapted from the pinned upstream source at
984023ddac0d5e27624f2baacde6105e45de375funder MIT; see the repository parity ledger for the exact path and blob.
Reverse Documentation
This skill analyzes existing implementation (code, prototypes, systems) and generates appropriate design or architecture documentation. Use this when:
- You built a feature without writing a design doc first
- You inherited a codebase without documentation
- You prototyped a mechanic and need to formalize it
- You need to document "why" behind existing code
Workflow
Phase 1: Parse Arguments
Format: $ai-game-studio:reverse-document <type> <path>
Type options:
design→ Generate a game design document (GDD section)architecture→ Generate an Architecture Decision Record (ADR)concept→ Generate a concept document from prototype
Path: Directory or file to analyze
src/gameplay/combat/→ All combat-related codesrc/core/event-system.cpp→ Specific fileprototypes/stealth-mech/→ Prototype directory
Examples:
$ai-game-studio:reverse-document design src/gameplay/magic-system
$ai-game-studio:reverse-document architecture src/core/entity-component
$ai-game-studio:reverse-document concept prototypes/vehicle-combat
Phase 2: Analyze Implementation
Read and understand the code/prototype:
For design docs (GDD):
- Identify mechanics, rules, formulas
- Extract gameplay values (damage, cooldowns, ranges)
- Find state machines, ability systems, progression
- Detect edge cases handled in code
- Map dependencies (what systems interact?)
For architecture docs (ADR):
- Identify patterns (ECS, singleton, observer, etc.)
- Understand technical decisions (threading, serialization, etc.)
- Map dependencies and coupling
- Assess performance characteristics
- Find constraints and trade-offs
For concept docs (prototype analysis):
- Identify core mechanic
- Extract emergent gameplay patterns
- Note what worked vs what didn't
- Find technical feasibility insights
- Document player fantasy / feel
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 265 lines · 24 tokens per session scan A 4cf418afe6e0
reverse-document is a skill published in the GitHub repository frabcd/codex-ai-game-studio (9 stars, last pushed 5d ago), licensed MIT. It adds 24 tokens to every session and 2,191 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 83% identical to reverse-document, differing in 25 lines, and is treated as a copy.
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