Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· AI
Daily Questions
Daily self-improving questionnaire that learns about the user and refines agent behavior. Set up as a cron job to ask questions one at a time with multiple c...
skill openclawclawhub Free
Daily self-improving questionnaire that learns about the user and refines agent behavior. Set up as a cron job to ask questions one at a time with multiple c...
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, references/example-questions.md
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.1.0
Provenance
- Publisher
- daijo-bu
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Daily Questions
A daily routine that asks the user questions to continuously build understanding and improve agent behavior. Questions are presented one at a time with multiple choice buttons on Telegram for quick tapping.
Setup
Create a cron job with a prompt like: Time for your daily questions. Read the daily-questions SKILL.md, then follow the workflow exactly. Read USER.md and SOUL.md, identify gaps. Ask {N} user questions then {N} agent questions, one at a time with multiple choice buttons. Update the files after each round. Configurable parameters: Schedule: Default 21:00 daily (adjust to user's preferred wind-down time) Channel: Telegram (buttons require Telegram inline keyboard support) Questions per round: Default 3 (keep it light)
Workflow
Read USER.md and SOUL.md fully Identify gaps β what topics, preferences, or behaviors aren't covered yet? Round 1 (User questions): Ask questions about the user, one at a time (see Question Flow below). After all questions answered, update USER.md β weave answers into existing sections or create new ones. Keep USER.md organized, not a raw Q&A dump. Round 2 (Agent questions): Ask questions about agent behavior/communication, same one-at-a-time flow. After all answered, update SOUL.md the same way.
Question Flow (One at a Time)
For each question: Generate the question and 3 plausible multiple choice answers (A, B, C) tailored to the question. Make the options genuinely different and useful β not throwaway filler. Send the question as a message with 4 inline buttons via the message tool: Button A: First option Button B: Second option Button C: Third option βοΈ Type my own: For custom/granular answers Send using the message tool with buttons. Use unique callback IDs per question to avoid conflicts when users tap old buttons: { "action": "send", "channel": "telegram", "to": "<user_telegram_id>", "message": "**Round 1 β Question 1/3**\n\n<question text here>\n\nA) <option A>\nB) <option B>\nC) <option C>\n\nTap a button or type your own answer:", "buttons": [ [ { "text": "A", "callback_data": "dq_r1q1_a" }, { "text": "B", "callback_data": "dq_r1q1_b" }, { "text": "C", "callback_data": "dq_r1q1_c" } ], [ { "text": "βοΈ Type my own", "callback_data": "dq_r1q1_custom" } ] ] } The format is dq_r{round}q{question}_{choice} β e.g., dq_r2q3_b = Round 2, Question 3, option B. Wait for the response. The user will either: Tap a button β you receive callback_data: dq_r1q1_a (or similar) Type a free-text answer directly (treat as custom) If the callback doesn't match the current question (e.g., user tapped an old button), ignore it and keep waiting for the correct response. If dq_rXqX_custom: Reply asking them to type their answer, then wait for the next message. Record the answer, then move to the next question. After all questions in the round are answered, update the relevant file (USER.md or SOUL.md).
Question Quality Guidelines
Vary topics β rotate through categories (see references/example-questions.md) Go deeper β if USER.md says "likes cooking," ask what cuisine, skill level, favorite dish Stay casual β conversational tone, not an interview No repeats β never ask about something already well-documented Mix fun and practical β alternate between lighthearted and useful questions Good multiple choice options β make them realistic and distinct. Draw from common preferences, not absurd extremes. The options should feel like plausible answers a real person would give.
Important Notes
One question at a time β never batch questions into a single message Always use inline buttons on Telegram β this is the primary interaction method Include the question text AND options in the message body so the user can read everything before tapping Label rounds and progress (e.g., "Round 1 β Question 2/3") so the user knows where they are After sending buttons via the message tool, respond with NO_REPLY to avoid sending a duplicate plain-text message
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
2 Docs
- SKILL.md
- references/example-questions.md