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catalyst-calendar

The catalyst-calendar skill builds and maintains a forward-looking event tracker for financial analysis, capturing earnings dates, conferences, product launches, regulatory decisions, and macroeconomic events across a defined coverage universe. Use this when you need to prioritize investment attention, anticipate market-moving catalysts, assess positioning ahead of key announcements, or generate weekly event summaries for strategic planning.

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git clone --depth 1 https://github.com/ginlix-ai/LangAlpha /tmp/catalyst-calendar && cp -r /tmp/catalyst-calendar/skills/catalyst-calendar ~/.claude/skills/catalyst-calendar
Después abre una sesión nueva de Claude Code; el skill carga automáticamente.

SKILL.md

# Catalyst Calendar

Build and maintain a calendar of upcoming catalysts across a coverage universe — earnings dates, conferences, product launches, regulatory decisions, and macro events. Helps prioritize attention and position ahead of events. Triggers on "catalyst calendar", "upcoming events", "what's coming up", "earnings calendar", "event calendar", or "catalyst tracker".

## Workflow

### Step 1: Define Coverage Universe

- List of companies to track (tickers or names)
- Sector / industry focus
- Include macro events? (Fed meetings, economic data, regulatory deadlines)
- Time horizon (next 2 weeks, month, quarter)

### Step 2: Gather Catalysts

For each company, identify upcoming events using platform tools:

- Use macro MCP: `get_earnings_calendar(from_date, to_date)` for all companies reporting in date range
- Use macro MCP: `get_economic_calendar(from_date, to_date)` for upcoming macro events
- Use `get_company_overview` tool for company details
- Use `WebSearch` / `WebFetch` for news-driven catalysts

**Earnings & Financial Events**
- Quarterly earnings date and time (pre/post market)
- Annual shareholder meeting
- Investor day / analyst day
- Capital markets day
- Debt maturity / refinancing dates

**Corporate Events**
- Product launches or announcements
- FDA approvals / regulatory decisions
- Contract renewals or expirations
- M&A milestones (close dates, regulatory approvals)
- Management transitions
- Insider trading windows (lockup expirations)

**Industry Events**
- Major conferences (dates, which companies presenting)
- Trade shows and expos
- Regulatory comment periods or rulings
- Industry data releases (monthly sales, traffic, etc.)

**Macro Events**
- Fed meetings (FOMC dates)
- Jobs report, CPI, GDP releases
- Central bank decisions (ECB, BOJ, etc.)
- Geopolitical events with market impact

### Step 3: Calendar View

| Date | Event | Company/Sector | Type | Impact (H/M/L) | Our Positioning | Notes |
|------|-------|---------------|------|-----------------|----------------|-------|
| | | | Earnings/Corp/Industry/Macro | | Long/Short/Neutral | |

### Step 4: Weekly Preview

Each week, generate a forward-looking summary:

**This Week's Key Events:**
1. [Day]: [Company] Q[X] earnings — consensus [$X EPS], our estimate [$X], key focus: [metric]
2. [Day]: [Event] — why it matters for [stocks]
3. [Day]: [Macro release] — expectations and positioning

**Next Week Preview:**
- Early heads-up on important events coming

**Position Implications:**
- Events that could move specific positions
- Any pre-positioning recommended
- Risk management ahead of binary events

### Step 5: Output

Save all outputs to `$WORK_DIR/work/{task}/`.

- Excel workbook with calendar view and sortable columns
- Weekly preview note (markdown)

> For all Excel formatting standards, follow the guidelines in `skills/xlsx/SKILL.md`.
> After generating Excel, run recalculation: `python skills/xlsx/scripts/recalc.py calendar.xlsx 30`

## Important Notes

- Earnings dates shift — verify against company IR pages and `get_earnings_calendar` closer to the date
- Pre-announce risk: track companies with a history of pre-announcing (positive or negative)
- Conference attendance lists are valuable — which companies are presenting and which are conspicuously absent?
- Some catalysts are recurring (monthly industry data) — build a template and auto-populate
- Color-code by impact level: Red = high impact, Yellow = moderate, Green = routine
- Archive past catalysts with the actual outcome — builds pattern recognition over time
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