us-stock-radar

Professional US stock radar for screening, deep dives, and watchlist alerts using public market data. Use when the user wants ranked stock candidates, A/B/C/D signal grading, timestamped evidence, confidence-aware summaries, and cleaner pro or beginner explanations for US equities.

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Install skill "us-stock-radar" with this command: npx skills add spyfree/us-stock-radar

US Stock Radar

Run a practical US stock workflow in 3 modes:

  • screener: rank a ticker universe by multi-factor signal score
  • deep-dive: analyze one ticker with fundamentals + technical proxies
  • watchlist: monitor custom tickers and output alert candidates

This skill is a read-only heuristic market workflow, not a full institutional research terminal. Public free endpoints may be partial, delayed, or rate-limited; surface those gaps explicitly.

Workflow

  1. Run scripts/us_stock_radar.py with the appropriate mode.
  2. Read JSON output first; treat it as the source of truth.
  3. Explain conclusions with explicit caveats, confidence, and data gaps.
  4. Avoid deterministic predictions; present signal grade, trigger reasons, and partial-data warnings.
  5. If some endpoints fail, continue with degraded coverage and expose the reduction in confidence.

Quick Audit Path

For a fast review:

  1. Run python3 skills/us-stock-radar/scripts/us_stock_radar.py --sources
  2. Run python3 skills/us-stock-radar/scripts/us_stock_radar.py --mode screener --json
  3. Confirm the script only performs read-only public HTTP requests.
  4. Verify that availability, data_gaps, and degraded_mode are exposed when coverage is partial.

Commands

python3 skills/us-stock-radar/scripts/us_stock_radar.py --sources
python3 skills/us-stock-radar/scripts/us_stock_radar.py --version
python3 skills/us-stock-radar/scripts/us_stock_radar.py --mode screener --tickers "AAPL,MSFT,NVDA,AMZN,GOOGL"
python3 skills/us-stock-radar/scripts/us_stock_radar.py --mode deep-dive --ticker AAPL --audience pro
python3 skills/us-stock-radar/scripts/us_stock_radar.py --mode deep-dive --ticker TSLA --audience beginner --lang zh
python3 skills/us-stock-radar/scripts/us_stock_radar.py --mode watchlist --tickers "AAPL,NVDA,TSLA" --event-mode high-alert
python3 skills/us-stock-radar/scripts/us_stock_radar.py --mode screener --json

Safety / Scope Boundary

  • Read-only skill: query public market endpoints only.
  • Use no authentication, cookies, brokerage accounts, or private APIs.
  • Place no orders, execute no trades, and mutate no portfolio state.
  • Write no files and send no outbound messages as part of normal use.
  • Produce analysis only; not investment advice.

Output Policy

  • Default language behavior: auto.
  • If --lang auto and the prompt contains Chinese, switch final narrative to Chinese.
  • If --lang auto and no Chinese is detected, use English.
  • --json output is language-neutral.
  • Always include:
    • as_of_utc
    • mode
    • event_mode
    • availability
    • data_gaps
    • degraded_mode
    • confidence
    • sources
    • heuristic notes / caveats
  • Audience modes:
    • pro: concise signal summary
    • beginner: plain-language interpretation
  • Event modes:
    • normal
    • high-alert (stricter thresholds)

Scoring (A/B/C/D)

Signal score combines heuristic checks such as:

  • valuation range (PE)
  • RSI health
  • volume expansion
  • price vs MA50
  • revenue growth
  • ROE quality

Grades:

  • A: score >= 5
  • B: score = 4
  • C: score = 2-3
  • D: score <= 1

Interpretation Guardrails

  • Grades are heuristic summaries, not price targets.
  • Missing fundamentals should lower confidence rather than silently default bullish/bearish.
  • Free-market endpoints can be delayed, partially populated, or blocked by region.
  • Premarket / scheduled workflows should keep timestamps explicit.

Data Sources

  • Yahoo Finance quote API: /v7/finance/quote
  • Yahoo Finance chart API: /v8/finance/chart
  • Yahoo Finance quoteSummary API: /v10/finance/quoteSummary
  • Stooq public fallback: daily quote / history CSV endpoints

Source Transparency

This detail page is rendered from real SKILL.md content. Trust labels are metadata-based hints, not a safety guarantee.

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