todo-to-node

Productivity

Convert a project TODO item into a ResearchOps tree node via LLM. Use when a user wants to expand a TODO into an executable research plan node with commands, checks, and acceptance criteria.

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Source SKILL.md: https://github.com/CurryTang/Amadeus/blob/HEAD/skills/todo-to-node/SKILL.md

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TODO to Tree Node

Convert a single project TODO (title + hypothesis) into a fully-specified ResearchOps tree node that can be inserted into the project plan.

Workflow

Input: Receive todo object (id, title, hypothesis) and optional parentNodeId.
Clarification Phase (optional, per-run configurable): Call POST /api/researchops/projects/:projectId/tree/nodes/from-todo/clarify with:
- todo: the TODO object
- messages: conversation so far (starts empty)
- The backend returns { done, question, options? }. Ask the question; when done: true, proceed to generate.
- User can Skip Q&A to bypass this phase entirely.
- Prompts are targeted at the TODO kind: papers referenced, KB code files needed, env assumptions, implementation vs experiment differences.
Generate: Call POST /api/researchops/projects/:projectId/tree/nodes/from-todo with:
- todo: the TODO object
- parentNodeId: optional parent node to attach under
- messages: clarification exchange collected in phase 2 (empty if skipped)
Review: Present the generated node fields to the user:
- id, title, kind
- assumption[]: what the node assumes
- target[]: measurable success criteria
- commands[]: concrete runnable commands
- checks[]: verification steps
Refine (optional): If user wants changes, send a follow-up request with:
- Same todo
- messages: conversation history [{role, content}] where assistant messages include the previous node JSON
Insert: Call POST /api/researchops/projects/:projectId/tree/plan/patches with:
```
{ "patches": [{ "op": "add_node", "node": <generated_node> }] }
```

Node Schema

{
  "id": "snake_case_slug",
  "parent": "optional_parent_node_id",
  "title": "Human-readable title",
  "kind": "experiment | analysis | knowledge | setup | milestone | patch | search",
  "assumption": ["key assumption 1"],
  "target": ["measurable criterion 1"],
  "commands": [{"cmd": "bash command", "label": "label"}],
  "checks": [{"condition": "verification", "label": "label"}],
  "tags": ["tag1"]
}

Refinement Examples

"Make commands use python3 instead of python"
"Add a validation step that checks the model accuracy"
"Change kind to analysis"
"Add an assumption that the dataset is already downloaded"

Error Handling

If LLM fails to return valid JSON: show raw text and ask user to retry
If insert fails: show patch error and keep generated node for retry