earnings-calendar
Get upcoming earnings dates with timing (before/after market) and EPS estimates. Use when user asks about earnings dates, earnings calendar, when a company…
Detect institutional whale activity in options for a given underlying. Use when the user asks about unusual options activity, large block trades, whale trades, or institutional options flow for a specific symbol.
$ npx -y skills add staskh/trading_skills --skill whale-hunting --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/whale-huntingContext preview
The summary Claude sees to decide when to auto-load this skill.
Detect institutional whale activity in options for a given underlying. Use when the user asks about unusual options activity, large block trades, whale trades, or institutional options flow for a specific symbol.
name: whale-hunting description: Detect institutional whale activity in options for a given underlying. Use when the user asks about unusual options activity, large block trades, whale trades, or institutional options flow for a specific symbol. dependencies: ["trading-skills"]
Scans option chains for a given underlying to identify institutional-sized trades using a two-step approach: 1. **Crude scan** (Yahoo Finance) — finds contracts with anomalous daily investment vs the rest of the chain. 2. **Precise drill-down** (Massive API) — fetches per-second bars for each candidate and flags seconds with outlier dollar invested.
> **Note:** If `uv` is not installed or `pyproject.toml` is not found, replace `uv run python` with `python` in all commands below.
uv run python .claude/skills/whale-hunting/scripts/whale_hunting.py SYMBOL [--months N] [--date YYYY-MM-DD] [--sigma F] [--sigma-z F] [--summary]
Returns JSON with:
# Hunt whales for AAPL (latest trading day) uv run python .claude/skills/whale-hunting/scripts/whale_hunting.py AAPL # Hunt whales for NVDA on a specific date uv run python .claude/skills/whale-hunting/scripts/whale_hunting.py NVDA --date 2026-03-13 # With per-ticker summary uv run python .claude/skills/whale-hunting/scripts/whale_hunting.py HOOD --months 3 --summary # Looser detection threshold uv run python .claude/skills/whale-hunting/scripts/whale_hunting.py SPY --sigma 2.0
After running the script, present the results as follows.
**Header line:** > Whale activity for **{underlying}** on {trading_date} — source: {source} > Call flow: ${total_call_invested:,.0f} | Put flow: ${total_put_invested:,.0f} | C/P ratio: {call_put_ratio:.2f}
**When `--summary` was requested**, render the `summary` array as a table:
| Time (ET) | Ticker | Type | Strike | Expiry | # Events | Total Invested | Break Even | |-----------|--------|------|--------|--------|----------|----------------|------------| | {timestamp} | {ticker} | {type} | {strike} | {expiry} | {whale_count} | ${total_invested:,.0f} | {break_even} |
Sort by `total_invested` descending. For multi-event rows use the time range of first–last event (e.g. `11:46–12:33`).
**Interpretation guidance:**
All timestamps and time-based calculations must use the `America/New_York` timezone. All JSON output must include `generated_at` (NY time string) and `data_delay` fields.
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