Replace Pi tool registrations with skills and CLI integration

- Remove all manually registered Pi tools (alpha_search, alpha_get_paper,
  alpha_ask_paper, alpha_annotate_paper, alpha_list_annotations,
  alpha_read_code, session_search, preview_file) and their wrappers
  (alpha.ts, preview.ts, session-search.ts, alpha-tools.test.ts)
- Add Pi skill files for alpha-research, session-search, preview,
  modal-compute, and runpod-compute in skills/
- Sync skills to ~/.feynman/agent/skills/ on startup via syncBundledAssets
- Add node_modules/.bin to Pi subprocess PATH so alpha CLI is accessible
- Add /outputs extension command to browse research artifacts via dialog
- Add Modal and RunPod as execution environments in /replicate and
  /autoresearch prompts
- Remove redundant /alpha-login /alpha-logout /alpha-status REPL commands
  (feynman alpha CLI still works)
- Update README, researcher agent, metadata, and website docs

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

skills/modal-compute/SKILL.md

@@ -0,0 +1,56 @@
+---
+name: modal-compute
+description: Run GPU workloads on Modal's serverless infrastructure. Use when the user needs remote GPU compute for training, inference, benchmarks, or batch processing and Modal CLI is available.
+---
+
+# Modal Compute
+
+Use the `modal` CLI for serverless GPU workloads. No pod lifecycle to manage — write a decorated Python script and run it.
+
+## Setup
+
+```bash
+pip install modal
+modal setup
+```
+
+## Commands
+
+| Command | Description |
+|---------|-------------|
+| `modal run script.py` | Run a script on Modal (ephemeral) |
+| `modal run --detach script.py` | Run detached (background) |
+| `modal deploy script.py` | Deploy persistently |
+| `modal serve script.py` | Serve with hot-reload (dev) |
+| `modal shell --gpu a100` | Interactive shell with GPU |
+| `modal app list` | List deployed apps |
+
+## GPU types
+
+`T4`, `L4`, `A10G`, `L40S`, `A100`, `A100-80GB`, `H100`, `H200`, `B200`
+
+Multi-GPU: `"H100:4"` for 4x H100s.
+
+## Script pattern
+
+```python
+import modal
+
+app = modal.App("experiment")
+image = modal.Image.debian_slim(python_version="3.11").pip_install("torch==2.8.0")
+
+@app.function(gpu="A100", image=image, timeout=600)
+def train():
+    import torch
+    # training code here
+
+@app.local_entrypoint()
+def main():
+    train.remote()
+```
+
+## When to use
+
+- Stateless burst GPU jobs (training, inference, benchmarks)
+- No persistent state needed between runs
+- Check availability: `command -v modal`