Skill: yezhaowang888-stack/sharpagent-intelligence-monitor (version 1.0.0)

--- name: sharpagent-intelligence-monitor version: 1.0.0 description: "SharpAgent Intelligence Monitor — Multi-track parallel intelligence aggregation system. Auto-collects from RSS/arXiv/GitHub/36kr, 3D dynamic scoring, five-factor trust verification, structured briefing output. For daily intelligence summaries, tech trend tracking, and competitive monitoring." metadata: openclaw: emoji: "📡" tags: - intelligence - monitor - daily-briefing - frontier - sharpagent - analysis --- # 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 ```yaml 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 ```bash 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 ```bash # 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*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)