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>
This commit is contained in:
Advait Paliwal
42
skills/alpha-research/SKILL.md
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skills/alpha-research/SKILL.md
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---
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name: alpha-research
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description: Search, read, and query research papers via the `alpha` CLI (alphaXiv-backed). Use when the user asks about academic papers, wants to find research on a topic, needs to read a specific paper, ask questions about a paper, inspect a paper's code repository, or manage paper annotations.
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---
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# Alpha Research CLI
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Use the `alpha` CLI via bash for all paper research operations.
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## Commands
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| Command | Description |
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|---------|-------------|
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| `alpha search "<query>"` | Search papers. Modes: `--mode semantic`, `--mode keyword`, `--mode agentic` |
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| `alpha get <arxiv-id-or-url>` | Fetch paper content and any local annotation |
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| `alpha get --full-text <arxiv-id>` | Get raw full text instead of AI report |
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| `alpha ask <arxiv-id> "<question>"` | Ask a question about a paper's PDF |
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| `alpha code <github-url> [path]` | Read files from a paper's GitHub repo. Use `/` for overview |
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| `alpha annotate <paper-id> "<note>"` | Save a persistent annotation on a paper |
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| `alpha annotate --clear <paper-id>` | Remove an annotation |
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| `alpha annotate --list` | List all annotations |
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## Auth
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Run `alpha login` to authenticate with alphaXiv. Check status with `alpha status`.
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## Examples
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```bash
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alpha search "transformer scaling laws"
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alpha search --mode agentic "efficient attention mechanisms for long context"
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alpha get 2106.09685
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alpha ask 2106.09685 "What optimizer did they use?"
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alpha code https://github.com/karpathy/nanoGPT src/model.py
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alpha annotate 2106.09685 "Key paper on LoRA - revisit for adapter comparison"
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```
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## When to use
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- Academic paper search, reading, Q&A → `alpha`
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- Current topics (products, releases, docs) → web search tools
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- Mixed topics → combine both
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56
skills/modal-compute/SKILL.md
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skills/modal-compute/SKILL.md
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---
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name: modal-compute
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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.
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---
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# Modal Compute
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Use the `modal` CLI for serverless GPU workloads. No pod lifecycle to manage — write a decorated Python script and run it.
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## Setup
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```bash
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pip install modal
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modal setup
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```
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## Commands
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| Command | Description |
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|---------|-------------|
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| `modal run script.py` | Run a script on Modal (ephemeral) |
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| `modal run --detach script.py` | Run detached (background) |
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| `modal deploy script.py` | Deploy persistently |
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| `modal serve script.py` | Serve with hot-reload (dev) |
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| `modal shell --gpu a100` | Interactive shell with GPU |
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| `modal app list` | List deployed apps |
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## GPU types
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`T4`, `L4`, `A10G`, `L40S`, `A100`, `A100-80GB`, `H100`, `H200`, `B200`
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Multi-GPU: `"H100:4"` for 4x H100s.
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## Script pattern
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```python
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import modal
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app = modal.App("experiment")
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image = modal.Image.debian_slim(python_version="3.11").pip_install("torch==2.8.0")
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@app.function(gpu="A100", image=image, timeout=600)
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def train():
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import torch
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# training code here
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@app.local_entrypoint()
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def main():
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train.remote()
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```
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## When to use
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- Stateless burst GPU jobs (training, inference, benchmarks)
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- No persistent state needed between runs
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- Check availability: `command -v modal`
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27
skills/preview/SKILL.md
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skills/preview/SKILL.md
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---
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name: preview
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description: Preview Markdown, LaTeX, PDF, or code artifacts in the browser or as PDF. Use when the user wants to review a written artifact, export a report, or view a rendered document.
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---
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# Preview
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Use the `/preview` command to render and open artifacts.
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## Commands
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| Command | Description |
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|---------|-------------|
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| `/preview` | Preview the most recent artifact in the browser |
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| `/preview --file <path>` | Preview a specific file |
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| `/preview-browser` | Force browser preview |
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| `/preview-pdf` | Export to PDF via pandoc + LaTeX |
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| `/preview-clear-cache` | Clear rendered preview cache |
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## Fallback
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If the preview commands are not available, use bash:
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```bash
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open <file.md> # macOS — opens in default app
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open <file.pdf> # macOS — opens in Preview
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```
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48
skills/runpod-compute/SKILL.md
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skills/runpod-compute/SKILL.md
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---
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name: runpod-compute
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description: Provision and manage GPU pods on RunPod for long-running experiments. Use when the user needs persistent GPU compute with SSH access, large datasets, or multi-step experiments.
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---
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# RunPod Compute
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Use `runpodctl` CLI for persistent GPU pods with SSH access.
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## Setup
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```bash
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brew install runpod/runpodctl/runpodctl # macOS
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runpodctl config --apiKey=YOUR_KEY
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```
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## Commands
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| Command | Description |
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|---------|-------------|
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| `runpodctl create pod --gpuType "NVIDIA A100 80GB PCIe" --imageName "runpod/pytorch:2.4.0-py3.11-cuda12.4.1-devel-ubuntu22.04" --name experiment` | Create a pod |
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| `runpodctl get pod` | List all pods |
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| `runpodctl stop pod <id>` | Stop (preserves volume) |
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| `runpodctl start pod <id>` | Resume a stopped pod |
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| `runpodctl remove pod <id>` | Terminate and delete |
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| `runpodctl gpu list` | List available GPU types and prices |
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| `runpodctl send <file>` | Transfer files to/from pods |
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| `runpodctl receive <code>` | Receive transferred files |
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## SSH access
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```bash
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ssh root@<IP> -p <PORT> -i ~/.ssh/id_ed25519
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```
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Get connection details from `runpodctl get pod <id>`. Pods must expose port `22/tcp`.
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## GPU types
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`NVIDIA GeForce RTX 4090`, `NVIDIA RTX A6000`, `NVIDIA A40`, `NVIDIA A100 80GB PCIe`, `NVIDIA H100 80GB HBM3`
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## When to use
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- Long-running experiments needing persistent state
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- Large dataset processing
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- Multi-step work with SSH access between iterations
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- Always stop or remove pods after experiments
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- Check availability: `command -v runpodctl`
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26
skills/session-search/SKILL.md
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26
skills/session-search/SKILL.md
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---
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name: session-search
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description: Search past Feynman session transcripts to recover prior work, conversations, and research context. Use when the user references something from a previous session, asks "what did we do before", or when you suspect relevant past context exists.
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---
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# Session Search
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Use the `/search` command to search prior Feynman sessions interactively, or search session JSONL files directly via bash.
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## Interactive search
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```
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/search <query>
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```
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Opens the session search UI. Supports `resume <sessionPath>` to continue a found session.
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## Direct file search
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Session transcripts are stored as JSONL files in `~/.feynman/sessions/`. Each line is a JSON record with `type` (session, message, model_change) and `message.content` fields.
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```bash
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grep -ril "scaling laws" ~/.feynman/sessions/
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```
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For structured search across sessions, use the interactive `/search` command.
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