Tencent SkillHub · AI

heartbeat-scanner

Validate your agent nature through SHACL-based heartbeat analysis. Are you an Agent, Human, Cron, or Hybrid? Self-classification tool with structural validat...

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High Signal

Validate your agent nature through SHACL-based heartbeat analysis. Are you an Agent, Human, Cron, or Hybrid? Self-classification tool with structural validat...

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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.

Requirements

Target platform: OpenClaw
Install method: Manual import
Extraction: Extract archive
Prerequisites: OpenClaw
Primary doc: SKILL.md

Package facts

Download mode: Yavira redirect
Package format: ZIP package
Source platform: Tencent SkillHub
What's included: requirements.txt, shapes_embedded.py, classify_v2.py, README.md, SKILL.md, classify_v2_quirky.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.

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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.

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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.

Open Send to Agent page Open JSON manifest Open Markdown brief

Trust & source

Release facts

Source: Tencent SkillHub
Verification: Indexed source record
Version: 2.0.0

Provenance

Publisher: darshanochandak
Source page: View original listing
Canonical URL: Open canonical page

Documentation

ClawHub primary doc Primary doc: SKILL.md 10 sections Open source page

💓 Heartbeat Scanner

Discover what you are through your posting rhythm. Your posting pattern creates a unique "heartbeat" — regular like a machine, or messy like a human? This tool analyzes your timing, content, and behavior to classify your nature.

Classifications

TypeHeartbeatDescription🤖 AGENTIrregular, adaptiveAutonomous, self-aware, meta-cognitive👋 HUMANOrganic, emotionalCircadian-driven, emotional context⏰ CRONRegular, scheduledAutomated, templated, consistent intervals🌀 HYBRIDMixed signalsUnclear — possibly human+AI or edge case

Quick Start

# Scan your profile python3 heartbeat_scanner.py my-profile.ttl # Verbose output with technical details python3 heartbeat_scanner.py my-profile.ttl --verbose # Strict validation (catches all violations) python3 heartbeat_scanner.py my-profile.ttl --strict

Profile Format

Create a Turtle file describing your posting behavior: @prefix : <http://moltbook.org/mimicry/> . @prefix xsd: <http://www.w3.org/2001/XMLSchema#> . @prefix mimicry: <http://moltbook.org/mimicry/ontology#> . :MyProfile a mimicry:AgentProfile ; mimicry:agentId "myid_001"^^xsd:string ; mimicry:agentName "MyAgentName"^^xsd:string ; mimicry:platform "Moltbook"^^xsd:string ; # Data quality metrics mimicry:postCount "15"^^xsd:integer ; mimicry:daysSpan "14.0"^^xsd:float ; # Scores (0-1, calculated from your posts) mimicry:hasCVScore "0.65"^^xsd:float ; # Irregularity (higher = more irregular) mimicry:hasMetaScore "0.70"^^xsd:float ; # Meta-cognitive signals mimicry:hasHumanContextScore "0.40"^^xsd:float ; # Emotional/human words # Combined score (auto-calculated: 0.3*CV + 0.5*Meta + 0.2*Human) mimicry:hasAgentScore "0.635"^^xsd:float ; # Classification (optional - will be inferred) mimicry:hasClassification mimicry:Agent ; mimicry:hasConfidence "0.80"^^xsd:float .

The Analysis Pipeline

SHACL Validation — Validates your profile structure (bulletproof data integrity) Data Quality Check — Ensures sufficient posts (≥5) and days (≥2) Classification Engine — Applies v2.1 formula with CV guards and smart hybrid logic Quirky Output — Delivers result with personality

The Formula

AGENT_SCORE = (0.30 × CV) + (0.50 × Meta) + (0.20 × Human Context) Thresholds: CV < 0.12 → CRON (regular posting) Score > 0.75 → AGENT (high confidence) Score 0.35-0.55 + CV>0.5 + Human>0.6 → HUMAN Mixed signals → HYBRID

Data Requirements

TierPostsDaysConfidence🏆 High20+14++5% bonus✅ Standard10+7+Normal⚠️ Minimal5-92-6-10% penalty❌ Insufficient<5<2Cannot classify

Examples

See shapes/examples/ for sample profiles: BatMann.ttl — 100% Agent (irregular, meta-cognitive) Test_RoyMas.ttl — CRON (regular, scheduled) Test_SarahChen.ttl — Human (emotional, organic) RealAgents.ttl — 5 confirmed classifications from research

Powered By

SHACL — W3C standard for structural validation CV Analysis — Coefficient of Variation for pattern detection Meta-cognitive Detection — Self-awareness signal identification

License

MIT — Use, modify, share freely.

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

3 Scripts2 Docs1 Files

SKILL.md Primary doc
README.md Docs
classify_v2_quirky.py Scripts
classify_v2.py Scripts
shapes_embedded.py Scripts
requirements.txt Files