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/devdavv/unity-ai-workflow/uw-code-reviewnpx skills add devdavv/unity-ai-workflow --skill uw-code-reviewgit clone --depth 1 https://github.com/devdavv/unity-ai-workflowWhat 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 | $0.00124 | $0.01071 |
| Opus 5 | $0.00062 | $0.00535 |
| Sonnet 5 | $0.00025 | $0.00214 |
| Haiku 4.5 | $0.00012 | $0.00107 |
Grade A, and why
uw-code-review 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.
How it starts
The opening of the file, as written. The whole thing — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Review
Pre-commit quality gate. Read the target files, then run this checklist. Output one structured report per review. Fix all issues before committing.
Before You Start
- Read
docs/CODING_STANDARDS.mdfor formatting, class structure, serialization, null safety, and async rules. - Read
docs/NAMING_CONVENTIONS.mdfor naming rules (private fields, properties, files, asmdefs). - Read
docs/GDD.mdfor Gherkin scenarios — every scenario should have a corresponding test. - Read the files being reviewed in full before assessing.
Checklist
1. Rules Compliance
- All Inspector fields use
[SerializeField] private— nopublicfields on MonoBehaviours - All component references cached in
Awake()orStart()— never inUpdate/FixedUpdate/LateUpdate -
GameDebugused instead ofDebug.Log/Debug.LogError/Debug.LogWarning - New Input System only — no
Input.GetKey*/Input.GetAxis* -
TryGetComponent<T>()preferred overGetComponent<T>()where component may be absent - No Unity API calls from background threads
- No
.metafile modifications
2. Naming Conventions
- Private fields:
_camelCase - Public properties & methods:
PascalCase - Parameters & local variables:
camelCase - Booleans:
is/has/can/shouldprefix (e.g._isGrounded,CanJump) - Events:
Onprefix (e.g.OnDeath) - Static fields:
s_camelCase - Class name matches file name
3. Code Quality
- No magic numbers or hardcoded strings — use constants, ScriptableObjects, or serialized fields
- No
FindObjectOfType<T>()orGameObject.Find()(except in editor-only code) - No cross-assembly references that violate the TDD's asmdef dependency graph
- K&R braces (opening brace on same line)
- 4-space indentation (no tabs)
4. Performance
- No
newallocations insideUpdate/FixedUpdate/LateUpdate(no GC pressure in hot paths) - Particles are pooled or have Stop Action set to
Destroywith pool backing - DOTween/PrimeTween tweens are killed in
OnDestroyorOnDisable - No
stringconcatenation in hot paths — useGameDebugwith conditional compilation
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 · 84 lines · 124 tokens per session scan A 69bd9ef4a1fc
uw-code-review is a skill published in the GitHub repository devdavv/unity-ai-workflow (41 stars, last pushed 5mo ago), licensed MIT. It adds 124 tokens to every session and 1,071 once invoked, about $0.0006 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 2026-08-30.
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