| name | intel-center |
| description | Global intelligence hub. Three-sector architecture (Politics/Finance/Tech) with cross-sector causal chain analysis, market anomaly detection, and delta engine. Collects from Google News, Yahoo Finance, RSS, Hacker News, and search engines. |
| enabled | true |
Intel Center
A comprehensive global intelligence collection and analysis skill for 1052 OS.
Three-sector architecture: Politics <-> Finance <-> Tech (interconnected — events in one sector often cascade to others)
What It Does
- Collects news and market data from 6 parallel sources
- Detects market anomalies and scan-to-scan price changes (delta engine)
- You analyze the collected data: filter, score relevance, identify cross-sector causal chains
- You write structured intelligence briefs with transmission chain analysis
Quick Start
In 1052 OS Agent mode, prefer the system tool:
intel_center_collect
The tool runs this Skill's collector from the correct Skill directory and returns the collected JSON for analysis.
Manual fallback:
Run the collection script:
python3 scripts/intel.py 2>&1
The script outputs JSON to stdout with all collected data. Progress goes to stderr.
When running manually, set the current working directory to the directory containing this SKILL.md; scripts/intel.py is relative to that directory.
The collector has a hard runtime budget and per-source stage budgets. In 1052 OS Agent mode the total budget is derived from the tool timeout; manual runs can override it with INTEL_CENTER_TOTAL_BUDGET_SECONDS.
In 1052 OS Agent mode, available sources are controlled by the system Search Sources registry (intel-source:*). Disabled registry sources are skipped by the collector and reported in diagnostics.
Then analyze the JSON output following the workflow below.
Data Sources
| Source | What | Count |
|---|
| Google News RSS | 10 keyword queries across 3 sectors | ~50-80 articles |
| Yahoo Finance | 15 key assets (oil, gold, S&P500, VIX, BTC, etc.) | Price + daily change |
| RSS Feeds | 15 sources (BBC, Reuters, TechCrunch, Wired, etc.) | ~50-75 articles |
| Hacker News | Top stories filtered by score >= 50 | ~20-30 items |
| Search Engines | Bing CN/INT, DuckDuckGo, Sogou WeChat | ~10-30 results |
| A/H Stocks | Northbound flow, sector rotation, limit up/down | Optional (needs akshare) |
| Tencent News | Reserved Chinese news source slot | Registered, adapter pending |
Market Assets Tracked
Politics/Geopolitics
- Gold (>1.5% = safe haven signal)
- Crude Oil (>2% = energy/geopolitical event)
- Hang Seng (>2% = China/HK geopolitical proxy)
Finance
- USD Index, S&P 500, VIX, 10Y Treasury, Bitcoin
- HS Tech ETF, CSI 300, SSE Composite
Tech
- Nasdaq, Philadelphia Semiconductor, TSMC, ARK Innovation ETF
Cross-Sector Transmission Paths
Politics -> Finance: Sanctions -> market shock / War -> inflation
Politics -> Tech: Export bans / Tech decoupling / State-funded R&D
Finance -> Tech: Interest rates -> tech valuations / USD -> chip costs
Tech -> Politics: AI regulation / Chip geopolitics / Cyber warfare
Tech -> Finance: Big tech earnings -> market / AI -> energy demand
Delta Engine
The script maintains a market-snapshot.json file to track scan-to-scan changes.
How it works:
- Each run saves current prices as a snapshot
- Next run compares against the previous snapshot
- Reports significant changes with severity (moderate/high)
- Computes risk direction: risk_on / risk_off / neutral
Thresholds:
- VIX: ±2.0 absolute | S&P 500: ±0.8% | Gold: ±0.8%
- Oil: ±1.2% | Bitcoin: ±2.0% | USD: ±0.4%
- Nasdaq: ±0.8% | Philly Semi: ±1.2%
Analysis Workflow
After running the script, analyze the JSON output:
Step 1: Market Signal Reading
Check market.anomalies for significant moves. Cross-reference with news:
- Oil spike + military news = geopolitical escalation
- VIX spike + no news = positioning ahead of known event
- Multiple assets moving same direction = high-conviction signal
Step 2: LLM Annotation
For each article, annotate:
| Field | Description |
|---|
title_cn | Chinese title (if needed) |
summary | Core content (50 chars) |
sector | politics / finance / tech |
relevance_score | 1-10 (6=notable, 8=important, 10=historic) |
event_type | intent / action / market / accident |
key_actors | Main actors involved |
causal_tags | From the cross-sector tag library |
cross_sector | Cross-sector transmission (if any) |
Filter rules:
- relevance < 5: discard
- Entertainment/sports: discard
- Duplicate events: keep highest-scored only
- Cross-sector events: boost relevance +1
Step 3: Transmission Chain Analysis (Core Value)
This is the key output — not listing events, but connecting causal chains:
Chain N: [Sector A -> Sector B (-> Sector C)]
Origin: Event title (Sector A)
Mechanism: One sentence explaining the transmission
Endpoint: Market signal or event (Sector B/C)
Confidence: High/Medium/Low (basis: N signals cross-confirmed)
Rules:
- Quality over quantity: 1 solid chain > 5 weak ones
- Must have origin event + endpoint (market signal or another event)
- Military events -> check finance (oil, gold, defense stocks)
- Diplomatic events -> check tech (export bans, chips) and finance (FX, tariffs)
Step 4: Write Intelligence Brief
Structure your output as a brief with:
- Sector summaries (top events per sector with relevance scores)
- Market anomalies (if any)
- Transmission chains (the core analytical value)
- Delta alerts (if significant scan-to-scan changes detected)
Optional Dependencies
| Package | Purpose | Install |
|---|
| certifi | HTTPS CA fallback when the local Python CA store is incomplete | pip install certifi |
| akshare | A/H stock data (northbound flow, sectors, limit up/down) | pip install akshare |
| tencent-news-cli | Chinese news (Tencent hot topics, morning brief) | npm package |
The script works without optional packages, but certifi improves reliability on Python installs whose default OpenSSL CA store is incomplete. akshare provides additional Chinese market data. Tencent News is registered in the system source registry, but its collector adapter is intentionally left pending until a stable source contract is available.
Scheduling
For best coverage, run 2-3 times daily:
| Time | Purpose |
|---|
| 09:30 | Morning brief (Asian markets open) |
| 15:00 | Afternoon update (US pre-market) |
| 22:00 | Evening brief (US market hours) |
Use 1052 OS's built-in calendar/scheduler to automate.
Output JSON Structure
{
"date": "2026-04-22",
"version": "1.0.0",
"sectors": ["politics", "finance", "tech"],
"cross_sector_tags": { ... },
"gnews": { "total": 65, "items": [...] },
"market": { "signals": {...}, "anomalies": [...], "by_sector": {...} },
"market_delta": { "since": "...", "deltas": [...], "risk_direction": "...", "risk_score": 0 },
"rss": { "total": 52, "items": [...] },
"hackernews": { "total": 25, "items": [...] },
"search_engines": { "total": 18, "items": [...] },
"china_market": { "northbound": {...}, "sectors_top": [...], ... },
"tencent_news": { "total": 0, "items": [] },
"diagnostics": { "warning_count": 0, "warnings": [], "elapsed_seconds": 0.0, "skipped_source_ids": [] }
}
