Tracks US macro signals from Bloomberg, WSJ, and Reuters and maps potential spillover to Vietnam sectors; used when users ask about US macro news and likely...
--- name: us-macro-news-monitor description: Tracks US macro signals from Bloomberg, WSJ, and Reuters and maps potential spillover to Vietnam sectors; used when users ask about US macro news and likely impacts on Vietnamese equities. compatibility: Requires OpenClaw web fetch and Brave API access. --- # US Macro News Monitor (Web Workflow) Use this skill for US macro monitoring that can indirectly impact Vietnamese equities. ## Data sources Primary media sources: - Bloomberg: https://www.bloomberg.com/ - WSJ: https://www.wsj.com/ - Reuters Markets: https://www.reuters.com/markets/ Fallback (open/public macro sources): - Federal Reserve (FOMC/calendar/speeches): https://www.federalreserve.gov/ - BLS releases (CPI, jobs): https://www.bls.gov/ - BEA releases (GDP, PCE): https://www.bea.gov/ - FRED calendars/series metadata: https://fred.stlouisfed.org/ ## Requirements and fallback behavior - Requires web access to collect current macro headlines, release pages, and source URLs. - Prefer primary media sources when accessible; use public official sources when paywalls or blocks prevent full-text access. - If fewer than three distinct sources are accessible, continue with available sources, downgrade confidence, and list blocked or missing sources. - No Python scripts are required for this skill. ## Execution workflow (ordered) 1. Collect fresh headlines from primary sources. 2. Replace blocked/paywalled coverage with fallback public sources. 3. Classify items into macro themes. 4. Tag each item with tone (`risk-on`, `risk-off`, `neutral`). 5. Map transmission channels to Vietnam sectors and representative tickers. 6. Run quality gate and confidence scoring. 7. Produce required output sections with source links. ## Theme taxonomy Classify each item into these macro themes: - inflation - rates/yields - growth/employment - USD/FX - oil/energy - geopolitics/trade Summarization horizon: - 24h changes - 7d narrative drift ## Data quality gate (required) Run this gate before finalizing output: 1. Freshness: include only items with explicit publish/update time; report window in ICT (`UTC+7`). 2. Coverage: target >= 3 distinct sources; if < 3, downgrade confidence. 3. De-duplication: remove near-duplicate headlines by normalized title + URL domain. 4. Evidence tags: mark each claim as `Fact` or `Inference`. 5. Missingness log: list blocked/paywalled sources and what fallback replaced them. ## Shared confidence rubric (required) Apply this common standard: - `High`: >= 3 distinct sources, <= 24h freshness for core items, and no single-source dominance over 60%. - `Medium`: >= 3 distinct sources with partial freshness gaps or moderate concentration issues, but core themes still cross-confirmed. - `Low`: fewer than 3 distinct sources, stale coverage, or major paywall blocks without reliable fallback. Always output confidence with: 1. Source count and source list. 2. Time window used (ICT `UTC+7`). 3. Main uncertainty driver (paywall, missing source, stale timing, or weak cross-confirmation). ## Output format (required) - Section 1: Top headlines with source URLs. - Section 2: Theme counts and tone distribution. - Section 3: Vietnam sector spillover map. - Section 4: Actionable watchlist (what to monitor next). ## Watchlist mode (optional) If the user provides an `ACTIVE_WATCHLIST` (tickers), extend Sections 3–4: - Add a `Watchlist Impact Map` that maps macro themes → transmission channel → each ticker. - Provide **monitoring triggers** (what would confirm/negate the impact) and a confidence tag. - Never output absolute buy/sell instructions; this skill produces signals and watch items. ## Trigger examples - "What US macro news overnight could move VN stocks today?" - "Summarize Fed/CPI signals and their impact on Vietnam sectors." - "Map US rates and USD narrative to Vietnamese equities." ## Quality rules - Clearly mark inferred impacts as inference. - Respect paywalls: use only accessible metadata when full text is unavailable. - Avoid over-claiming causality. - If source coverage is below threshold, explicitly set confidence to `Low`.
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