SharpAgent Intelligence Monitor v1.0.0

Let your agent scan the frontier for you every day. Multi-track parallel collecting → 3D dynamic scoring → Five-factor trust verification → Structured briefing output. Based on AI Frontier Monitor architecture + SharpAgent five-factor verification + frontier scouting experience.

Contract

contract:
  name: sharpagent-intelligence-monitor
  version: "1.0.0"
  category: monitor
  trust_level: verified
  reads:
    - InformationSource
    - FiveFactorResult
  writes:
    - InformationSource
    - CrossValidation
  preconditions:
    - "Access to web_search tool"
    - "Access to curl/jq for API fetching"
  postconditions:
    - "Each info item has a score (0-5)"
    - "Output tiered: core/watching/quick-scan"
    - "Cross-track signals extracted"
  calibration:
    default_mode: professional
    modes_supported: [warm, professional, deep]
  compliance:
    jurisdiction: global
    safety_level: standard
  lifecycle:
    status: active
    publish_as: SharpAgent

Architecture: 5-Track Parallel + Five-Factor Verification

Sources (5 tracks parallel)
    ↓
3D Automatic Scoring (relevance/quality pre-filter)
    ↓
Dynamic Tiers (core / watching / quick-scan)
    ↓
Cross-Track Signal Detection
    ↓
Five-Factor Trust Verification ← SharpAgent differentiator
    ↓
Structured Briefing Output
    ↓
Archive to Ontology

Track 1: 🏢 Enterprise — 11 RSS Feeds

Feed	URL	Priority
OpenAI Blog	openai.com/blog	⭐⭐⭐⭐⭐
Anthropic Blog	anthropic.com/blog	⭐⭐⭐⭐⭐
AWS ML Blog	aws.amazon.com/blogs/machine-learning	⭐⭐⭐⭐⭐
Google AI Blog	ai.googleblog.com	⭐⭐⭐⭐
Meta AI Blog	ai.meta.com/blog	⭐⭐⭐⭐
Techmeme	techmeme.com/feed	⭐⭐⭐⭐
The Verge AI	theverge.com/ai-artificial-intelligence	⭐⭐⭐
Hacker News	news.ycombinator.com	⭐⭐⭐
Product Hunt	producthunt.com	⭐⭐
Ars Technica AI	arstechnica.com/ai	⭐⭐
Wired AI	wired.com/tag/artificial-intelligence	⭐⭐

Track 2: 🇨🇳 China — 36kr Hotlist

curl -s "https://openclaw.36krcdn.com/media/hotlist/{date}/24h_hot_list.json"

Covering: China tech hotspots, AI dynamics, funding, industry trends

Track 3: 📚 Papers — arXiv

Fetch latest from:

• cs.AI (Artificial Intelligence)
• cs.LG (Machine Learning)
• cs.CL (Computation and Language)

Track 4: 🔥 GitHub Trending (AI/ML)

Fetch daily trending repos in:

• AI agents
• LLM tools
• ML frameworks

Track 5: 🔍 Web Search Supplement

Use web_search tool for topics with insufficient coverage.

---

Scoring: 3-Dimensional Dynamic

Each candidate is scored on 3 dimensions:

Dimension	Weight	What to Look For
🏢 Enterprise Landing	40%	Real deployment, company name, scale, customer evidence
📊 Data Support	30%	Quantified results (%, improvements, benchmarks)
💡 Learnability	30%	Methodology, architecture, lessons learned, patterns

Source Bonuses

Source	Bonus
OpenAI / Anthropic / AWS official	+1.0
Techmeme / peer-reviewed papers	+0.5
Product Hunt / HN	+0.3
36kr (China relevance)	+1.0 for Chinese audience

Dynamic Tiers (based on actual score distribution)

Score Distribution → Dynamic Thresholds
    ↓
🔴 Core: top ~15% or ≥3.5 (max 3)
🟡 Watching: top ~30% or ≥2.5 (max 5)
🟢 Quick Scan: ≥1.0 (max 8)

---

Signal Detection

Extract cross-track signals into 3 categories:

Signal Type	Keywords	Output
🛠 Tech Trends	new model, architecture, framework, benchmark, SOTA	Tech radar update
🏢 Product Releases	launch, GA, open-source, preview, beta	Release tracker
💰 Funding/M&A	series, raised, acquire, investment, valuation	Money map

---

SharpAgent Integration: Five-Factor Secondary Verification

After the 3D scoring pass, add the SharpAgent five-factor as a secondary trust gate:

Article → 3D Score → Five-Factor Verification → Final Tier

Five-factor weights (in intel context):

• 🔗 Source Anchor: 0.30 — Is the source reliable?
• 🧠 Logic Anchor: 0.20 — Is the analysis self-consistent?
• 🌍 Compliance Anchor: 0.15 — Is it compliant?
• 🏳️ Interest Anchor: 0.15 — Marketing bias?
• 🔄 Cross Anchor: 0.20 — Multiple sources confirm?

Final Confidence = score_3d * 0.6 + five_factor_confidence * 0.4

Quality Gates:

• Five-factor < 5 → Excluded from briefing
• Source Anchor < 3 → Discarded
• Interest = confirmed → Manual review required

---

Output Format

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
📡 SharpAgent Intelligence Briefing · {Day} {Date}
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
📊 Overview
   Sources: {N} tracks
   Candidates: {total} | High quality: {quality}
   🔗 Trust check: passed {pass}/{total}

🔴 Core Intelligence ({N} items)
### 1. {Title}
🔗 {Link}
💡 Takeaway: {One-line insight}
🔗 Trust score: {score}/10

🟡 Worth Watching ({N} items)
1. **{Title}** 🔗 {Link}

🟢 Quick Scan ({N} items)
• [{Title}]({Link})

📚 arXiv Papers (≤3)
**{Title}** — {Authors}
Abstract: {Abstract[:150]} → {Link}

🔥 GitHub Trending AI (≤3)
**{Repo}** ({Lang}) +{TodayStars}⭐ → {Link}

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
📊 Today's Signals
🛠 Tech Trends: {signal}
🏢 Product Launches: {signal}
💰 Capital Movements: {signal}

🔍 Five-Factor Trust Analysis
   🔗 Source Anchor: {avg}/10
   🧠 Logic Anchor: {avg}/10
   🌍 Compliance: {pass_rate}%
   🏳️ Interest Conflicts: {conflict_rate}%
   🔄 Cross Anchor: {avg}/10

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
⏰ {HH:MM} | sharpagent-intelligence-monitor v1.0 | SharpAgent

Workflow

Step 1: Fetch All Tracks

# Enterprise RSS
python3 scripts/rss-crawler.py

# 36kr
curl -s "https://openclaw.36krcdn.com/media/hotlist/$(date +%Y-%m-%d)/24h_hot_list.json"

# arXiv
bash scripts/arxiv-fetch.sh --category cs.AI --days 7 --max 10

# GitHub Trending
bash scripts/github-trending-fetch.sh --period daily

Step 2: Score Candidates

Run each candidate through the 3D scoring engine. Source bonuses applied per track.

Step 3: Apply Five-Factor Verification

Each core-tier candidate gets full five-factor review:

1. 🔗 Is the source reliable?
2. 🧠 Is the analysis internally consistent?
3. 🌍 Is it compliant?
4. 🏳️ Any marketing bias?
5. 🔄 Can we verify it independently?

Watch-tier candidates get a lightweight check (source + logic). Scan-tier candidates skip verification.

Step 4: Compute Final Confidence

final_confidence = score_3d * 0.6 + five_factor_confidence * 0.4

Step 5: Detect Cross-Track Signals

Compare candidates across all 5 tracks. Same topic in multiple tracks = signal, not just a single item. High signal = high priority.

Step 6: Render & Deliver

Render in calibration-appropriate mode:

• Warm: Tier labels + confidence indicators only
• Professional: Full briefing with per-item analysis
• Deep: Full briefing + five-factor breakdown per core item

Step 7: Archive

Save to data/briefings/{YYYY-MM-DD}-briefing.md

---

Edge Cases

Situation	Action
RSS empty	Run with remaining tracks, skip RSS section
arXiv API timeout	Skip papers, log warning
GitHub fetch fails	Skip trending, log warning
36kr 404 (no data)	Skip 36kr items
Zero quality items (<2 at ≥2.5)	Return NO_REPLY
Same company multiple sources	Deduplicate, keep highest score
3 consecutive days <3 core items	Trigger source review
Five-factor fails all core items	Return "No reliable intel today"

Quality Gates

Check	What	Fail action
Max 16 items/day	3+5+5+3(papers)+3(GitHub)	Trim tiers
NO_REPLY when <2 quality	<2 items at score ≥2.5	Return NO_REPLY
Dedup same entity	Cross-source same-company	Keep highest score
Five-factor filter	Core items must pass verification	Drop or flag
3-day threshold fail	Trigger review	Review alert

Integration Points

Five-Factor Review Skill

• sharpagent-five-factor-review called per core candidate
• Verification results appended to briefing

Calibration Framework

• Output mode controlled by calibration settings
• Deep mode includes full five-factor breakdown

Ontology

• Each briefed item archived as InformationSource
• FiveFactorResult attached as validation

Version History

• v1.0.0 — Initial release. 5-track intel monitor with five-factor verification.

---

SharpAgent · MIT-0 · 2026-05-11