| name | logic-bridge-protocol |
| description | Validates vague product requirements and user stories against five closure rules (actor, scenario, goal, actionable path). Returns structured follow-up questions when incomplete, or FileEditor-ready JSON tasks when the story is ready. Use when refining PRDs, user stories, feature specs, or before delegating implementation to an agent. |
| version | 1.0.0 |
| metadata | {"openclaw":{"requires":{"anyBins":"[Truncated]"},"emoji":"🧠"}} |
Logic Bridge Protocol
Purpose
Turn fuzzy natural-language requests into reviewable, structured output. The companion script protocol.py performs a lightweight closure check inspired by first-principles and pyramid-style thinking: if the text is too thin, the skill returns specific follow-up questions; if it passes, it returns JSON tasks suitable for a file editor or coding agent.
When to use
- The user pastes a one-line idea, half-baked user story, or “make a button” request.
- You need a gate before writing code or large docs.
- You want a repeatable JSON contract for downstream tools (e.g. FileEditor, task runners).
Dependencies
- Python 3.10+
- Pydantic v2 (
pip install pydantic or uv pip install pydantic)
How to run
From the skill folder:
python3 protocol.py
To call the API in code or from a REPL:
from protocol import logic_bridge_protocol
result = logic_bridge_protocol({
"raw_text": "As a store manager, on the inventory page I need to export CSV when stock is low so I can reorder."
})
print(result)
Input
| Field | Type | Required | Description |
|---|
raw_text | string | yes | Raw requirement or user story text |
Output (JSON)
Failure — status: "error"
message: short summary for the agent.follow_up_questions: list of concrete gaps (actor, scenario, goal, path, or length).
Success — status: "ok"
message: confirmation string.file_editor_tasks: list of tasks with:
intent: write | patch | reviewtarget_path: suggested file path (default brief: docs/logic_bridge_task.md)instructions: what to write in natural language, including a sha256 digest of the normalized input for traceability.
Rules the checker enforces
- Minimum substance — not just a couple of words.
- Actor — who benefits or performs the action (supports EN/ZH cues).
- Scenario — where/when in the product this applies.
- Problem / goal — pain or intended outcome.
- Actionable path — steps or navigation, not only intent.
Limitations
- Heuristic only; it can false-negative on poetic or highly implicit writing.
- Tune regexes in
protocol.py for your domain (e.g. B2B, internal tools).
Examples
Too vague
Input: {"raw_text": "add a feature"}
→ Error with follow-ups asking for actor, scenario, goal, and steps.
Stronger story
Input: {"raw_text": "As a support agent, when I open a ticket I want to paste logs and save them so the engineer sees them. I click Attach, choose file, then Save."}
→ Success with a docs/logic_bridge_task.md write task and sha256 note.
Testing
A self-contained test suite ships with the skill:
python3 test_protocol.py
Coverage: empty input, missing keys, wrong types, vague one-liners, partially-complete stories (EN + ZH), fully-closed stories, hash determinism.
Publishing to ClawHub
Zip the folder that contains SKILL.md, protocol.py, requirements.txt, and test_protocol.py (same directory level), or use the ClawHub CLI per current docs. Ensure only text-based files are included; total bundle must respect registry limits.
