⏳ This skill is pending AI review.

Scores will appear once the review pipeline completes.

version unknown

trivia-refiner

@yinon-alfred-openclaw⭐ 0 stars

Refine Hebrew or English trivia questions with a config-driven semi-auto workflow, guarded consensus submission, and explicit held-question review.

Choose how to use this skill

You do not need every option. Choose the path your AI client supports. The stable page stays the same; versioned files are immutable.

1. Native installer

This listing has no registered native installer command. Use the complete package or source fallback below, depending on what your client supports.

Do not guess an installer command or replace an existing version without reviewing the diff.

2. Complete package recommended

Download the ZIP when available. It includes SKILL.md plus the references, security notes and version metadata.

No complete ProSkills package is published for this listing yet.

3. Prompt-only

Copy the prompt above when the agent can read the stable page or when you want to adopt the workflow without installing a skill.

Need only the instruction file?

Download SKILL.md only if your client requires a single file. The complete ZIP is safer for a full installation because it preserves the references and release context.

No path installs or executes anything by itself. Your agent still needs access to the project files. Before updating, compare the installed version and review the diff.

—/10

// RATINGS

⭐GitHub Stars
⭐ 0 on GitHubGitHub ↗

New / niche

🟢ProSkills Score
—
📍

Not yet listed on ClawHub or SkillsMP

// README

trivia-refiner

An OpenClaw skill for refining Hebrew trivia questions from a Supabase quiz database, with user approval before any database updates.

What it does now

The current workflow is:

  1. Fetch a batch of questions from raw_questions_he
  2. Fetch available categories from Supabase
  3. Build a detailed prompt with rephrasing, option-review, categorization, and formatting rules
  4. Print that prompt for Alfred to handle in the current session
  5. Wait for user review and approval
  6. Submit approved changes back to the database
  7. Track processed question IDs locally

Important model note

This skill does not currently call a hardcoded model directly from run_batch.py.

Instead, run_batch.py prints the prompt and the actual refinement work is done by Alfred in the current OpenClaw session.

So the effective model is simply:

  • whatever model Alfred is currently running

Main scripts

scripts/run_batch.py

Main entrypoint.

  • No args: fetch next 10 questions from raw_questions_he, starting after the highest ID already present in questions_he
  • With a range like 193-202: fetch that exact range
  • Fetches categories
  • Prints the full prompt for Alfred to process in-session
  • Does not directly update the database

scripts/submit_changes.py

Writes approved changes back to Supabase.

  • Validates required fields
  • Updates raw_questions_he
  • Upserts into questions_he
  • Records success/failure in tracking for audit/history

scripts/tracking.py

Manages local runtime tracking. This is audit/history only; automatic batch selection uses questions_he as the source of truth.

Tracking file:

~/.openclaw/workspace/memory/trivia-refiner-processed.json

Used for:

  • processed IDs
  • refined/failed status
  • highest processed ID for audit/history; not the next-batch source of truth
  • batch count

How to run it

Automatic / next batch

python3 scripts/run_batch.py

Specific range

python3 scripts/run_batch.py 193-202

Submit approved changes

python3 scripts/submit_changes.py changes.json

Dry run submission

python3 scripts/submit_changes.py changes.json --dry-run

What someone should know before using it

  • It is an approval-based workflow — database writes should only happen after explicit approval
  • The refinement logic currently happens in the Alfred session, not inside a hardcoded Gemini/Claude pipeline
  • It depends on Supabase credentials stored at:
~/.openclaw/workspace/memory/supabase-creds.json

Expected format:

{
  "url": "https://xxxx.supabase.co",
  "key": "your-service-role-key"
}
  • It updates two tables when submitting approved changes:
    • raw_questions_he
    • questions_he

In one sentence

This skill is currently a prompt-driven, human-approved trivia refinement workflow that uses Alfred’s current session model for the actual question processing.

License

MIT

// HOW IT'S BUILT

KEY FILES

trivia-refiner/SKILL.mdREADME.md

// REPO STATS

0 stars

// SHARE

Share on XShare on LinkedIn