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Replace Pi tool registrations with skills and CLI integration

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- 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
2026-03-25 00:38:45 -07:00
parent 5fab329ad1
commit 7024a86024
26 changed files with 320 additions and 1009 deletions
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2
prompts/autoresearch.md
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@@ -27,6 +27,8 @@ Ask the user where to run:
- **New git branch** — create a branch so main stays clean
- **Virtual environment** — create an isolated venv/conda env first
- **Docker** — run experiment code inside an isolated Docker container
- **Modal** — run on Modal's serverless GPU infrastructure. Write Modal-decorated scripts and execute with `modal run`. Best for GPU-heavy benchmarks with no persistent state between iterations. Requires `modal` CLI.
- **RunPod** — provision a GPU pod via `runpodctl` and run iterations there over SSH. Best for experiments needing persistent state, large datasets, or SSH access between iterations. Requires `runpodctl` CLI.
Do not proceed without a clear answer.

2
prompts/replicate.md
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@@ -14,6 +14,8 @@ Design a replication plan for: $@
- **Local** — run in the current working directory
- **Virtual environment** — create an isolated venv/conda env first
- **Docker** — run experiment code inside an isolated Docker container
- **Modal** — run on Modal's serverless GPU infrastructure. Write a Modal-decorated Python script and execute with `modal run <script.py>`. Best for burst GPU jobs that don't need persistent state. Requires `modal` CLI (`pip install modal && modal setup`).
- **RunPod** — provision a GPU pod on RunPod and SSH in for execution. Use `runpodctl` to create pods, transfer files, and manage lifecycle. Best for long-running experiments or when you need SSH access and persistent storage. Requires `runpodctl` CLI and `RUNPOD_API_KEY`.
- **Plan only** — produce the replication plan without executing
4. **Execute** — If the user chose an execution environment, implement and run the replication steps there. Save notes, scripts, raw outputs, and results to disk in a reproducible layout. Do not call the outcome replicated unless the planned checks actually passed.
5. **Log** — For multi-step or resumable replication work, append concise entries to `CHANGELOG.md` after meaningful progress, failed attempts, major verification outcomes, and before stopping. Record the active objective, what changed, what was checked, and the next step.