# 1052666 1052 Os 1052 Os > --- - Skill: `tomevault-io/1052666-1052-os-1052-os` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/1052666-1052-os-1052-os` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/1052666-1052-os-1052-os/raw - Safety review: pending (external: skill-scanner PASS, skillspector CAUTION) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/1052666-1052-os-1052-os --- --- 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 1. **Collects** news and market data from 6 parallel sources 2. **Detects** market anomalies and scan-to-scan price changes (delta engine) 3. **You analyze** the collected data: filter, score relevance, identify cross-sector causal chains 4. **You write** structured intelligence briefs with transmission chain analysis ## Quick Start In 1052 OS Agent mode, prefer the system tool: ```text 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: ```bash 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: 1. **Sector summaries** (top events per sector with relevance scores) 2. **Market anomalies** (if any) 3. **Transmission chains** (the core analytical value) 4. **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 ```json { "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": [] } } ``` --- > Source: [1052666/1052-OS](https://github.com/1052666/1052-OS) — distributed by [TomeVault](https://tomevault.io).