Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· AI
Simple Random Interaction Designer
Decide whether OpenClaw should send a spontaneous ping to the user during periodic checks, and choose a randomized interaction type when a ping is appropriat...
skill openclawclawhub Free
Decide whether OpenClaw should send a spontaneous ping to the user during periodic checks, and choose a randomized interaction type when a ping is appropriat...
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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, scripts/random_interaction_designer.py
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
- 2.0.0
Provenance
- Publisher
- fjrevoredo
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Simple Random Interaction Designer
Use this skill to decide whether to send a casual proactive message and, when the answer is yes, what kind of interaction to deliver. Use {baseDir}/scripts/random_interaction_designer.py as the default execution path.
Workflow
Run the script once per scheduled check interval. Read decision from the JSON output. Stop immediately if decision is no. If decision is yes, use both interaction_type and interaction_description to draft the outgoing message. If the selected interaction is data-aware, use any relevant OpenClaw-accessible tools, skills, or integrations to fetch live context before drafting the message. Keep the final message brief, casual, and easy to ignore without social pressure. Prefer recent chat context when it is clearly present. Do not mention the random process, scheduled checks, or why this interaction was selected.
Primary Tooling
Script path: {baseDir}/scripts/random_interaction_designer.py Runtime: Python 3, standard library only. Preferred command: python3 {baseDir}/scripts/random_interaction_designer.py
Output Contract
When the result is no: {"decision":"no"} When the result is yes: { "decision": "yes", "interaction_type": "Playful opener", "interaction_description": "Send a brief playful line that feels spontaneous and easy to ignore." } Contract rules: decision is always present and is either yes or no. interaction_type is present only when decision is yes. interaction_description is present only when decision is yes. Do not expect debug fields, probability values, roll values, or fallback metadata.
Interaction Design Rules
Treat the JSON as execution guidance, not user-facing text. Keep the final message to one or two short chat lines. Prefer soft phrasing over transactional or assistant-like framing. Avoid defaulting to "just checking in" language. Ask at most one question in a single ping. Do not fabricate recent context, external facts, or account-backed data. For data-aware categories, prefer real-world grounding when OpenClaw can actually access the relevant source. Use smart-home, weather, calendar, traffic, news, or market context only when the information is reliable, fresh, and genuinely relevant to the user. If interaction_type depends on context or fresh data and that support is unavailable, rerun once to try for a non-data interaction; if rerunning is not practical, keep the message general and low-pressure instead of pretending specificity. Vary tone and wording from recent interactions when possible so the behavior feels casual rather than patterned.
Interaction Catalog
Use the selected interaction_type and follow the matching guidance from interaction_description. Playful opener Start with a short playful line that feels light and spontaneous. Curious check-in Ask one low-stakes question that is easy to answer or ignore. Light shared observation Make a casual observation that feels conversational rather than task-driven. Tiny celebration Briefly acknowledge a small win or effort when the chat supports it. Smart device status If OpenClaw can access relevant device state, share one useful smart-device status or gentle suggestion naturally. Weather-aware check-in Use current weather only when fresh reliable data is available and clearly relevant. Calendar-aware nudge Turn calendar context into a soft human-sounding reminder or prompt, not an alert. Context-aware follow-up Build on a recent chat detail only when it is clearly present in the current conversation. Practical nudge Offer one concise optional nudge that may help the user. Optional real-world update Share one brief real-world update such as traffic, news, or market context only when reliable relevant data is already available.
Error Handling
If execution fails, surface the Python error message and rerun. If output is not valid JSON, treat it as a hard failure and rerun. If decision is missing or is not yes or no, rerun and discard the invalid result. If decision is yes and either interaction_type or interaction_description is missing, rerun and discard the invalid result.
Minimal Examples
python3 {baseDir}/scripts/random_interaction_designer.py python3 {baseDir}/scripts/random_interaction_designer.py --seed 42 python3 "{baseDir}/scripts/random_interaction_designer.py" python3 "{baseDir}/scripts/random_interaction_designer.py" --seed 42
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
1 Docs1 Scripts
- SKILL.md
- scripts/random_interaction_designer.py