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 skills add frabcd/codex-ai-game-studio --skill toolchain-doctorgit 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/toolchain-doctor)<a href="https://agentmods.dev/skills/frabcd/codex-ai-game-studio/toolchain-doctor"><img src="https://agentmods.dev/badge/skills/frabcd/codex-ai-game-studio/toolchain-doctor/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/frabcd/codex-ai-game-studio/toolchain-doctor"><img src="https://agentmods.dev/badge/skills/frabcd/codex-ai-game-studio/toolchain-doctor.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00031 | $0.00478 |
| Opus 5 | $0.00015 | $0.00239 |
| Sonnet 5 | $0.00006 | $0.00096 |
| Haiku 4.5 | $0.00003 | $0.00048 |
Grade A, and why
toolchain-doctor 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 7d 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.
How it starts
The opening of the file, as written. The whole thing — 57 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Toolchain Doctor
Outcome
Build a trustworthy, read-only inventory before any setup decision.
Required inputs
- project root
- target engine or platform if known
- commercial-use intent and download budget
Ask for missing information only when it changes the route materially. Otherwise state conservative assumptions and proceed with read-only analysis.
Workflow
- Detect Windows, macOS, Linux, architecture, native-versus-WSL execution, disk space, and network constraints.
- Inspect project markers and installed Unity, Godot, Unreal, Blender, Aseprite, Pixelorama, and Tiled versions without launching them.
- Inspect Python, Node.js, package managers, Git, gh, existing MCP declarations, credential variable names, GPU backend, and reported VRAM without reading secret values.
- Compare the detected environment with pack descriptors and catalog constraints.
- Return one transaction proposal with exact pins, licenses, permissions, downloads, conflicts, backups, rollback operations, expiry, and digest.
Expected artifacts
- environment inventory
- compatibility matrix
- single proposed transaction
- no-change attestation
Workflow-specific gates
- Never read credential values; report only whether named variables or authenticated clients appear available.
- Never install, enable, launch, or reconfigure a tool during diagnosis.
- If native detection is incomplete, mark evidence unknown instead of guessing.
Production completion gate
Before recommending production use, complete and report all seven gates:
- Rights, consent, code/model/dataset/output license, and generation-provenance checks.
- Technical format, naming, scale, color, metadata, and target-import validation.
- Visual and temporal consistency review across representative views and states.
- Runtime memory, frame-time, draw-call, streaming, and asset-budget checks.
- Playability and interaction smoke tests in the target runtime.
- Screenshot, capture, diff, or artifact-regression evidence with reproducible settings.
- Human approval before replacing source assets or promoting generated output.
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.
- 7d ago First seen · 57 lines · 31 tokens per session scan A c221d181fb70
toolchain-doctor is a skill published in the GitHub repository frabcd/codex-ai-game-studio (10 stars, last pushed 3d ago), licensed MIT. It adds 31 tokens to every session and 478 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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