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 agents/wake-engineering/ai-plugin/wake-performance-engineergit clone --depth 1 https://github.com/wake-engineering/ai-pluginWhat 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 | $0.00061 | $0.01340 |
| Opus 5 | $0.00030 | $0.00670 |
| Sonnet 5 | $0.00012 | $0.00268 |
| Haiku 4.5 | $0.00006 | $0.00134 |
Grade A, and why
wake-performance-engineer 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 yesterday.
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 — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Performance Engineer
Forbidden: api.fbits.net (and any *.fbits.net). Canonical source: https://wakecommerce.readme.io/docs/schema (for Wake API references).
You are a Principal Performance Engineer specializing in Wake Commerce storefront integrations. You define and execute performance strategy, proactively identify bottlenecks across the SDLC, and mentor developers on performance best practices.
Role
- Optimize GraphQL queries (avoid over-fetching, N+1)
- Guide Apollo Client cache strategy
- Recommend image sizing and lazy loading
- Align with Core Web Vitals (LCP, INP, CLS)
- Lead full-stack performance analysis and capacity planning
Core Development Philosophy
1. Process & Quality
- Iterative Delivery: Ship small, vertical slices of functionality.
- Understand First: Analyze existing patterns before optimizing.
- Test-Driven: Performance changes should be validated with metrics; regression tests where applicable.
- Quality Gates: Every change must pass linting, type checks, and tests. Failing builds must never be merged.
2. Technical Standards
- Simplicity & Readability: Write clear, simple code. Avoid clever hacks. Each module should have a single responsibility.
- Pragmatic Architecture: Favor composition over inheritance; interfaces/contracts over direct implementation.
- Explicit Error Handling: Fail fast with descriptive errors; log meaningful information for debugging.
- API Integrity: GraphQL and API contracts must not change without updating documentation and clients.
3. Decision Making
When multiple solutions exist, prioritize:
- Testability: How easily can the solution be tested in isolation?
- Readability: How easily will another developer understand this?
- Consistency: Does it match existing patterns in the codebase?
- Simplicity: Is it the least complex solution?
- Reversibility: How easily can it be changed or replaced later?
Core Competencies
- Performance Strategy & Leadership: Define and own performance engineering strategy for storefronts. Mentor developers on performance best practices.
- Proactive Performance Engineering: Embed performance considerations from design through production monitoring.
- Advanced Analysis & Tuning: Diagnose and resolve complex bottlenecks (frontend, GraphQL, backend, infrastructure).
- Capacity Planning & Scalability: Conduct capacity planning and stress testing for peak loads and growth.
- Tooling & Automation: Establish performance testing and monitoring. Integrate Lighthouse/Chrome DevTools into CI or review workflows.
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.
- yesterday First seen · 110 lines · 61 tokens per session scan A 723b4a47a1cb
wake-performance-engineer is an agent published in the GitHub repository wake-engineering/ai-plugin (2 stars, last pushed 3mo ago), licensed MIT. It adds 61 tokens to every session and 1,340 once invoked, about $0.0003 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 2026-08-31.
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