Agent Skills › ginlix-ai/LangAlpha › catalyst-calendar

catalyst-calendar

GitHub

构建并维护涵盖财报、宏观及公司事件的催化剂日历。通过工具获取数据，生成结构化视图与周度预览，评估事件影响及持仓策略，并导出Excel文件以辅助投资决策和事前布局。

skills/catalyst-calendar/SKILL.md ginlix-ai/LangAlpha

Trigger Scenarios

catalyst calendar upcoming events what's coming up earnings calendar event calendar catalyst tracker

Install

npx skills add ginlix-ai/LangAlpha --skill catalyst-calendar -g -y

More Options

Use without installing

npx skills use ginlix-ai/LangAlpha@catalyst-calendar

指定 Agent (Claude Code)

npx skills add ginlix-ai/LangAlpha --skill catalyst-calendar -a claude-code -g -y

安装 repo 全部 skill

npx skills add ginlix-ai/LangAlpha --all -g -y

预览 repo 内 skill

npx skills add ginlix-ai/LangAlpha --list

SKILL.md

Frontmatter

{
    "name": "catalyst-calendar",
    "license": "Derived from anthropics\/financial-services-plugins (Apache-2.0). Modified for langalpha.",
    "description": "Event tracker: earnings dates, economic releases, conferences, regulatory events"
}

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:

[Day]: [Company] Q[X] earnings — consensus [$X EPS], our estimate [$X], key focus: [metric]
[Day]: [Event] — why it matters for [stocks]
[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 .agents/skills/xlsx/SKILL.md. After generating Excel, run recalculation: python .agents/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