chore: add flash agent skill co-located with source code

flash/SKILL.md

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+---
+name: flash
+description: runpod-flash SDK and CLI for deploying AI workloads on Runpod serverless GPUs/CPUs.
+user-invocable: true
+---
+
+# Runpod Flash
+
+Write code locally, test with `flash run` (dev server at localhost:8888), and flash automatically provisions and deploys to remote GPUs/CPUs in the cloud. `Endpoint` handles everything.
+
+## Setup
+
+```bash
+pip install runpod-flash                 # requires Python >=3.10
+
+# auth option 1: browser-based login (saves token locally)
+flash login
+
+# auth option 2: API key via environment variable
+export RUNPOD_API_KEY=your_key
+
+flash init my-project                    # scaffold a new project in ./my-project
+```
+
+## CLI
+
+```bash
+flash run                                # start local dev server at localhost:8888
+flash run --auto-provision               # same, but pre-provision endpoints (no cold start)
+flash build                              # package artifact for deployment (500MB limit)
+flash build --exclude pkg1,pkg2          # exclude packages from build
+flash deploy                             # build + deploy (auto-selects env if only one)
+flash deploy --env staging               # build + deploy to "staging" environment
+flash deploy --preview                   # build + launch local preview in Docker
+flash env list                           # list deployment environments
+flash env create staging                 # create "staging" environment
+flash env get staging                    # show environment details + resources
+flash env delete staging                 # delete environment + tear down resources
+flash undeploy list                      # list all active endpoints
+flash undeploy my-endpoint               # remove a specific endpoint
+```
+
+## Endpoint: Three Modes
+
+### Mode 1: Your Code (Queue-Based Decorator)
+
+One function = one endpoint with its own workers.
+
+```python
+from runpod_flash import Endpoint, GpuGroup
+
+@Endpoint(name="my-worker", gpu=GpuGroup.AMPERE_80, workers=5, dependencies=["torch"])
+async def compute(data):
+    import torch  # MUST import inside function (cloudpickle)
+    return {"sum": torch.tensor(data, device="cuda").sum().item()}
+
+result = await compute([1, 2, 3])
+```
+
+### Mode 2: Your Code (Load-Balanced Routes)
+
+Multiple HTTP routes share one pool of workers.
+
+```python
+from runpod_flash import Endpoint, GpuGroup
+
+api = Endpoint(name="my-api", gpu=GpuGroup.ADA_24, workers=(1, 5), dependencies=["torch"])
+
+@api.post("/predict")
+async def predict(data: list[float]):
+    import torch
+    return {"result": torch.tensor(data, device="cuda").sum().item()}
+
+@api.get("/health")
+async def health():
+    return {"status": "ok"}
+```
+
+### Mode 3: External Image (Client)
+
+Deploy a pre-built Docker image and call it via HTTP.
+
+```python
+from runpod_flash import Endpoint, GpuGroup, PodTemplate
+
+server = Endpoint(
+    name="my-server",
+    image="my-org/my-image:latest",
+    gpu=GpuGroup.AMPERE_80,
+    workers=1,
+    env={"HF_TOKEN": "xxx"},
+    template=PodTemplate(containerDiskInGb=100),
+)
+
+# LB-style
+result = await server.post("/v1/completions", {"prompt": "hello"})
+models = await server.get("/v1/models")
+
+# QB-style
+job = await server.run({"prompt": "hello"})
+await job.wait()
+print(job.output)
+```
+
+Connect to an existing endpoint by ID (no provisioning):
+
+```python
+ep = Endpoint(id="abc123")
+job = await ep.runsync({"input": "hello"})
+print(job.output)
+```
+
+## How Mode Is Determined
+
+| Parameters | Mode |
+|-----------|------|
+| `name=` only | Decorator (your code) |
+| `image=` set | Client (deploys image, then HTTP calls) |
+| `id=` set | Client (connects to existing, no provisioning) |
+
+## Endpoint Constructor
+
+```python
+Endpoint(
+    name="endpoint-name",                  # required (unless id= set)
+    id=None,                               # connect to existing endpoint
+    gpu=GpuGroup.AMPERE_80,               # single GPU type (default: ANY)
+    gpu=[GpuGroup.ADA_24, GpuGroup.AMPERE_80],  # or list for auto-select by supply
+    cpu=CpuInstanceType.CPU5C_4_8,        # CPU type (mutually exclusive with gpu)
+    workers=5,                             # shorthand for (0, 5)
+    workers=(1, 5),                        # explicit (min, max)
+    idle_timeout=60,                       # seconds before scale-down (default: 60)
+    dependencies=["torch"],                # pip packages for remote exec
+    system_dependencies=["ffmpeg"],        # apt-get packages
+    image="org/image:tag",                 # pre-built Docker image (client mode)
+    env={"KEY": "val"},                    # environment variables
+    volume=NetworkVolume(...),             # persistent storage
+    gpu_count=1,                           # GPUs per worker
+    template=PodTemplate(containerDiskInGb=100),
+    flashboot=True,                        # fast cold starts
+    execution_timeout_ms=0,                # max execution time (0 = unlimited)
+)
+```
+
+- `gpu=` and `cpu=` are mutually exclusive
+- `workers=5` means `(0, 5)`. Default is `(0, 1)`
+- `idle_timeout` default is **60 seconds**
+- `flashboot=True` (default) -- enables fast cold starts via snapshot restore
+- `gpu_count` -- GPUs per worker (default 1), use >1 for multi-GPU models
+
+### NetworkVolume
+
+```python
+NetworkVolume(name="my-vol", size=100)  # size in GB, default 100
+```
+
+### PodTemplate
+
+```python
+PodTemplate(
+    containerDiskInGb=64,    # container disk size (default 64)
+    dockerArgs="",           # extra docker arguments
+    ports="",                # exposed ports
+    startScript="",          # script to run on start
+)
+```
+
+## EndpointJob
+
+Returned by `ep.run()` and `ep.runsync()` in client mode.
+
+```python
+job = await ep.run({"data": [1, 2, 3]})
+await job.wait(timeout=120)        # poll until done
+print(job.id, job.output, job.error, job.done)
+await job.cancel()
+```
+
+## GPU Types (GpuGroup)
+
+| Enum | GPU | VRAM |
+|------|-----|------|
+| `ANY` | any | varies |
+| `AMPERE_16` | RTX A4000 | 16GB |
+| `AMPERE_24` | RTX A5000/L4 | 24GB |
+| `AMPERE_48` | A40/A6000 | 48GB |
+| `AMPERE_80` | A100 | 80GB |
+| `ADA_24` | RTX 4090 | 24GB |
+| `ADA_32_PRO` | RTX 5090 | 32GB |
+| `ADA_48_PRO` | RTX 6000 Ada | 48GB |
+| `ADA_80_PRO` | H100 | 80GB |
+| `HOPPER_141` | H200 | 141GB |
+
+## CPU Types (CpuInstanceType)
+
+| Enum | vCPU | RAM | Max Disk | Type |
+|------|------|-----|----------|------|
+| `CPU3G_1_4` | 1 | 4GB | 10GB | General |
+| `CPU3G_2_8` | 2 | 8GB | 20GB | General |
+| `CPU3G_4_16` | 4 | 16GB | 40GB | General |
+| `CPU3G_8_32` | 8 | 32GB | 80GB | General |
+| `CPU3C_1_2` | 1 | 2GB | 10GB | Compute |
+| `CPU3C_2_4` | 2 | 4GB | 20GB | Compute |
+| `CPU3C_4_8` | 4 | 8GB | 40GB | Compute |
+| `CPU3C_8_16` | 8 | 16GB | 80GB | Compute |
+| `CPU5C_1_2` | 1 | 2GB | 15GB | Compute (5th gen) |
+| `CPU5C_2_4` | 2 | 4GB | 30GB | Compute (5th gen) |
+| `CPU5C_4_8` | 4 | 8GB | 60GB | Compute (5th gen) |
+| `CPU5C_8_16` | 8 | 16GB | 120GB | Compute (5th gen) |
+
+```python
+from runpod_flash import Endpoint, CpuInstanceType
+
+@Endpoint(name="cpu-work", cpu=CpuInstanceType.CPU5C_4_8, workers=5, dependencies=["pandas"])
+async def process(data):
+    import pandas as pd
+    return pd.DataFrame(data).describe().to_dict()
+```
+
+## Common Patterns
+
+### CPU + GPU Pipeline
+
+```python
+from runpod_flash import Endpoint, GpuGroup, CpuInstanceType
+
+@Endpoint(name="preprocess", cpu=CpuInstanceType.CPU5C_4_8, workers=5, dependencies=["pandas"])
+async def preprocess(raw):
+    import pandas as pd
+    return pd.DataFrame(raw).to_dict("records")
+
+@Endpoint(name="infer", gpu=GpuGroup.AMPERE_80, workers=5, dependencies=["torch"])
+async def infer(clean):
+    import torch
+    t = torch.tensor([[v for v in r.values()] for r in clean], device="cuda")
+    return {"predictions": t.mean(dim=1).tolist()}
+
+async def pipeline(data):
+    return await infer(await preprocess(data))
+```
+
+### Parallel Execution
+
+```python
+import asyncio
+results = await asyncio.gather(compute(a), compute(b), compute(c))
+```
+
+## Gotchas
+
+1. **Imports outside function** -- most common error. Everything inside the decorated function.
+2. **Forgetting await** -- all decorated functions and client methods need `await`.
+3. **Missing dependencies** -- must list in `dependencies=[]`.
+4. **gpu/cpu are exclusive** -- pick one per Endpoint.
+5. **idle_timeout is seconds** -- default 60s, not minutes.
+6. **10MB payload limit** -- pass URLs, not large objects.
+7. **Client vs decorator** -- `image=`/`id=` = client. Otherwise = decorator.
+8. **Auto GPU switching requires workers >= 5** -- pass a list of GPU types (e.g. `gpu=[GpuGroup.ADA_24, GpuGroup.AMPERE_80]`) and set `workers=5` or higher. The platform only auto-switches GPU types based on supply when max workers is at least 5.