Description: Turn vague product ideas into AI-ready specs. Bilingual requirement closure checker for non-technical builders.把模糊的产品想法变成 AI Coding 能读懂的需求文档。面向没有产品背景的创作者，通过五步追问完成需求闭环。

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.

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:

bash

python3 protocol.py

To call the API in code or from a REPL:

python

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 | review
  • target_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

1. Minimum substance — not just a couple of words.
2. Actor — who benefits or performs the action (supports EN/ZH cues).
3. Scenario — where/when in the product this applies.
4. Problem / goal — pain or intended outcome.
5. 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:

bash

python3 test_protocol.py
# 12/12 tests passed

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.