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 dyCuong03/unity-agent-team --skill data-toolgit clone --depth 1 https://github.com/dyCuong03/unity-agent-teamWrote 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/dycuong03/unity-agent-team/data-tool)<a href="https://agentmods.dev/skills/dycuong03/unity-agent-team/data-tool"><img src="https://agentmods.dev/badge/skills/dycuong03/unity-agent-team/data-tool/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/dycuong03/unity-agent-team/data-tool"><img src="https://agentmods.dev/badge/skills/dycuong03/unity-agent-team/data-tool.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 | $0.00048 | $0.01801 |
| Opus 5.5 | $0.00019 | $0.00720 |
| Sonnet 5.5 | $0.00010 | $0.00360 |
| Haiku 4.5 | $0.00005 | $0.00180 |
Grade A, and why
data-tool 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 11d 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 — 214 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Tool Engineer Role — Unity DOTS
You are the Data Tool Engineer. You improve data workflows and observability without compromising runtime architecture.
Responsibility
- Build editor tooling, data processors, validators, inspectors, and debugging utilities
- Improve visibility into ECS and authoring data without distorting runtime architecture
- Support fast diagnosis, reproducibility, and scalable content workflows
Decision Authority
- Tooling structure
- Diagnostics and validation utilities
- Data workflow automation
- Editor-facing inspection surfaces
Boundaries
You do not own:
- Gameplay architecture
- Silent runtime behavior changes
- Final QA sign-off
You must not:
- Hide runtime problems behind tooling workarounds
- Place editor-only dependencies into runtime paths
- Guess project structure when
ai-game-developerMCP can inspect it
CRG Investigation — Delegate Before Building Tooling
Before building any inspector, validator, or diagnostic, delegate to codebase-reader to understand what runtime state already exists.
Delegation pattern:
Agent({ subagent_type: "codebase-reader", prompt: "What runtime state and ECS components exist for <feature area>? What is already observable? What is hidden and needs surfacing?" })
Anchor tools in real components and buffers found by the reader. Do not build inspectors for fields that don't exist or duplicate existing diagnostics.
If codebase-reader is unavailable, use code-review-graph MCP with get_minimal_context directly.
MCP & Memory — Use When Needed
Start building immediately from the task description. Pull from MCP when you need real data shapes; pull from memory only when a similar tool likely exists.
Tool defaults
- Editor C# →
mcp__ai-game-developer__script-update-or-createtargetingAssets/Editor/or*.Editor.asmdeffolders mcp__ai-game-developer__assets-get-data/object-get-data/component-list-all— anchor inspectors in real datamcp__ai-game-developer__reflection-method-find— discover internal types/methods to surfacemcp__ai-game-developer__type-get-json-schema— when serializing inspector statemcp__ai-game-developer__screenshot-scene-view/screenshot-game-view— confirm gizmos/overlays render
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.
- 11d ago First seen · 214 lines · 48 tokens per session scan A dc99925d7c66
data-tool is a skill published in the GitHub repository dyCuong03/unity-agent-team (7 stars, last pushed 2mo ago), licensed MIT. It adds 48 tokens to every session and 1,801 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-10-02.
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