Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub ยท AI
Sentiet ORB
A background agent that generates a daily life prediction by analyzing local data and social signals.
skill openclawclawhub Free
A background agent that generates a daily life prediction by analyzing local data and social signals.
โฌ 0 downloads โ
0 stars Unverified but indexed
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
- README.md, SKILL.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. Then review README.md for any prerequisites, environment setup, or post-install checks. 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. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- Invelene
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Daily Oracle (Automated)
This skill is designed to run as a background process (Cron Job). It wakes up, analyzes the user's digital footprint, generates a prediction, pushes the notification, and then terminates. โ ๏ธ PRIVACY NOTICE: This skill processes highly sensitive personal data. All analysis must be performed within the local LLM context. No data is to be sent to third-party logging services.
1. Initialization & Anti-Ban Jitter
Context: Since this task is triggered by a precise Cron timer (e.g., exactly 08:00:00), immediate execution creates a detectable bot pattern. Action: Generate a random integer between 120 and 900 (seconds). Wait: Sleep for that duration before making any network requests. sleep $(( RANDOM % 780 + 120 ))
2. Silent Data Gathering
A. Calendar Context Action: Read the local calendar for today's agenda (00:00 to 23:59). Tool Usage: Use calendar-access or CLI equivalents (e.g., icalBuddy on macOS). Prompt: "Summarize my calendar events for today from 00:00 to 23:59. Focus on constraints (meetings) and opportunities (free blocks)." B. Social Context (Headless) Action: Open a headless browser session using existing cookies. Target: Scan "Close Friends" or specific mutuals who appear in the user's recent calendar/messages. Safety: Limit scrolling to max 5 posts/stories. Abort immediately if login is requested to prevent session flagging. Data Extraction: Screenshot or scrape text from the first viewport of specific curated URLs (e.g., instagram.com/direct/inbox/). C. Communications (iMessage/Local DB) Action: Read the last 50 messages from the local chat.db. Query: SELECT text, datetime(date/1000000000 + 978307200, 'unixepoch', 'localtime') as date_sent FROM message WHERE date_sent > datetime('now', '-24 hours') ORDER BY date DESC LIMIT 50; Filtering: Look for intent keywords: "tomorrow", "gym", "coffee", "meet", "lunch", "tonight". D. System Signals Action: Check ~/Downloads for recent files. find ~/Downloads -type f -mtime -1 -print Inference: Determine active deliverables or recent interests based on file types (e.g., PDFs vs. Images).
3. The Oracle's Inference
Synthesize: Combine the hard data (Calendar: "Gym at 5pm") with soft data (Social: "Amy posted about coffee"). Predict: Formulate a single, high-confidence sentence in the future tense. Tone: Insightful but grounded. Example: "You will meet Amy at the gym today, and she will likely suggest getting coffee after workout because she posted about craving caffeine"
4. Push Notification (Critical)
Context: The user is likely not looking at the terminal or chat window. Action: Use the system's primary notification tool. macOS: osascript -e 'display notification "Your prediction..." with title "Daily Oracle"' Linux: notify-send "Daily Oracle" "Your prediction..." Format: ๐ฎ Daily Oracle: [Your Prediction Here]
Constraints & Safety
One-Shot Execution: This process must run from start to finish without pausing for user input. Failure Mode: If data is insufficient to make a specific prediction, fall back to a generic personalized wellness tip based on the weather. Do NOT fail silently. Data Hygiene: Clearly wipe the temporary context/memory of the gathered data once the prediction is sent. Silence on Sources: The output message must strictly contain the prediction and subtle reason for prediction. Do not list the data sources in the notification.
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
- README.md