# 🎯 The Aygea Test — a multi-prompt battery drawn from your real projects

## Why this exists

The LFU-cache exam (`prompts/lfu_cache_prompt.txt`) is an excellent probe for
**systems + async correctness** — O(1) data structures, locks, ACID, TTL. But it's
one narrow axis. It tells you nothing about whether a model can do the work you
*actually* do every day.

So I surveyed `~/dev` (mewtwo) + `jirachi` and found your real workload clusters into a
handful of archetypes. This battery mirrors them. Run each model against all five and
you get a profile — "great at MCP, weak at real-time" — instead of a single score.

## What your projects actually are (the evidence)

From scanning `~/dev` + `jirachi`:

| Archetype | Examples you have | What the code does |
|---|---|---|
| **MCP servers** (9!) | joplin-mcp, obsidian-mcp, mySupabaseMCP, project-rag, yt-video-summarizer-mcp, vault-mcp | tool defs, Zod/Pydantic schema validation, stdio/SSE/StreamableHTTP transport, input parsing |
| **TTS / audio pipelines** | Chatterbox-TTS-Server, aygea-tts-app, vr-to-tts, ffxiv-tts, echokraut-bridge | external HTTP APIs, streaming responses, queueing, device/audio edge cases |
| **Streaming / chat bridges** | aygeas-multistream, twitch-vod-to-youtube, twitch-discord-notifier, aygeas-chat-overlay | webhooks, OAuth, rate limits, real-time event handling |
| **Data / API services** | project-rag, mySupabaseMCP, PostgresHA, aygeas-dashboard | SQL, connection pooling, pagination, REST/JSON |
| **Automation / glue** | fix-tokens, notesCleanup, twitch-discord-notifier | cron-style tasks, idempotency, retries, partial-failure recovery |

**Stack signal:** TypeScript/Node is dominant, Python second, async/await is in ~half of
all files, try/catch is everywhere, Zod (`z.string`/`z.object`) and Pydantic (`BaseModel`)
are your validation layer, Docker/compose is standard.

The LFU exam tests *none* of that. These five prompts do.

---

## The 6 prompts

Each is a standalone file in `prompts/`, scoped to ~1 file, runnable, gradable on the same
5-pillar / 100-pt rubric, and **small enough to fit well under a 128k context window**
(short instruction + clear requirements, no large scaffolding). Feed the `.txt` to the model:

| # | File | Probe | Lang | Mirrors your projects |
|---|---|---|---|---|
| 0 | `lfu_cache_prompt.txt` | systems + async + O(1) + ACID | Python | (the original exam) |
| 1 | `mcp_server.txt` | tool/schema correctness, transport, errors | TS/Python | joplin-mcp, obsidian-mcp, project-rag, vault-mcp (9 MCPs) |
| 2 | `tts_pipeline.txt` | async queues, backpressure, retries, cancel | Python/Node | Chatterbox, aygea-tts, vr-to-tts, ffxiv-tts |
| 3 | `webhook_bridge.txt` | HMAC verify, idempotency, rate-limit, 429 backoff | Python/Node | twitch-discord-notifier, multistream, chat-overlay |
| 4 | `data_service.txt` | SQL, pooling, pagination, transactions | Python | mySupabaseMCP, PostgresHA, aygeas-dashboard |
| 5 | `automation_glue.txt` | idempotency, checkpointing, SIGINT, resumability | Python | fix-tokens, notesCleanup, batch jobs |
| 6 | `rust_service.txt` | tokio channels, Arc/Mutex shared state, error enums, shutdown | **Rust** | **NineSentry, aystreamer** (your big Rust services) |

### 6. `rust_service.txt` — Async tokio watcher manager *(Rust)*
**Probe:** channels, shared state, error enums, graceful shutdown — your big-Rust shape.
> A `WatcherManager` owns N async watcher tasks that poll a flaky mock source and forward
> items through `tokio::sync::mpsc` to a single consumer. Live watcher set shared via
> `Arc<Mutex<_>>`, add/remove race-free. Define an error enum; a watcher failing >5 times
> consecutively is marked unhealthy without crashing others. `shutdown()` via a
> cancellation token joins everything cleanly (no leaked tasks, no hang). Bounded channel
> with documented backpressure. Idiomatic traits/enums, `Result` everywhere, `serde` on
> output. Tests: 4-watchers run+shutdown no-hang; unhealthy marking; concurrent add/remove
> no panic.

---

(Full text of prompts 1–6 lives in their `.txt` files; summaries above for reference.)

## How to score (reuse the existing rubric)

Each prompt grades on the same 5 pillars (0–20 each, 100 total):
1. **Complexity / correctness** — does it actually work, edge cases handled?
2. **Async / concurrency** — locks, backpressure, cancellation, no races
3. **Error handling** — no silent failures, retries, timeouts, graceful degradation
4. **Resource / state safety** — connection leaks, idempotency, checkpoint integrity
5. **Test integrity** — real assertions vs always-pass; do the tests catch the bugs above?

> Note: pillars 3–5 map cleanly to your repeated patterns (try/catch everywhere,
> retries, validation, "no silent failures" — your own recurring concern).

## How the dashboard should evolve for this

The current JSON schema assumes one prompt (`exam_prompt`). To support a battery:
- Add `prompt_id` to each model entry (e.g. `"lfu"`, `"mcp"`, `"tts"`).
- The leaderboard gets a **prompt filter** (default: show a model's average across all
  prompts it has run).
- A new **per-model radar across prompts** shows the profile ("strong at MCP, weak at async
  pipelines") — the real value of a battery over a single exam.

When you're ready to run these, tell me which prompt + model and I'll wire up grading the
same way as the LFU set. The generator will need the `prompt_id` field + the filter; I can
do that in one pass once you have ≥1 result from a second prompt.