| name | alphameta-earnings |
| description | Post-earnings analysis skill for institutional-grade earnings updates. Covers beat/miss, segment breakdown, margin trends, guidance assessment, estimate revisions, and valuation. Dual-mode: Lite (in-chat summary card, default) + Full (Markdown report, optional DOCX). Supports US markets. Triggers: "earnings update", "quarterly results", "Q1/Q2/Q3/Q4 results", "earnings report", "post-earnings analysis", "beat/miss", "guidance update", "财报分析", "业绩更新", "季度业绩", "季报", "年报", "盈利分析", "财报点评", "财报前瞻", "业绩前瞻", "财报预览". |
AlphaMeta Earnings
Response language: match the user's input language — Simplified Chinese / English.
RULE: Response language priority: English is the default when language is ambiguous. If the user input is only a slash command, command name, ticker / symbol, or contains no natural-language language signal, you MUST respond in English. Do not infer Chinese from trigger keywords, skill metadata, or examples.
Routing: Pre-earnings vs Post-earnings
Determine whether the company has reported the latest quarter:
| Signal | Action |
|---|
| Earnings not yet released (upcoming) | Route to references/pre-earnings.md. Follow the pre-earnings preview workflow. |
| Already reported | Proceed with post-earnings analysis below. Default to Lite Mode. |
To check: use python3 scripts/collect.py <SYMBOL> and inspect the digest for period-end date and report status.
Post-Earnings: Dual-Mode Architecture
Lite Mode (default, ~2-3 min)
The default path. Produces an in-chat summary card with 8 modules.
Step 1 — Collect data
Run the parallel data collector (pure Python stdlib, no pip needed):
python3 scripts/collect.py <SYMBOL>
This fetches 10 data sources concurrently and prints a compact digest (~3-4K tokens). The script:
- Normalizes symbol format automatically
- Exits with an error if the AlphaMeta server is unreachable
- Saves raw JSON responses to a temp directory (printed in digest)
Step 2 — Output summary card directly (no DOCX, no file, no DCF). Card modules — skip any whose data is N/A:
- Header —
[Company] ([Ticker]) — Q[X] [Year] Earnings + consensus rating, avg target, current price, upside
- Core KPI table — 4-5 metrics (Revenue, NI, Gross Margin, EPS): Reported / YoY / vs Estimate (Beat +X% or Miss -X%)
- Revenue by segment — table with Unicode
█ share bars + YoY (from Segment digest)
- Quarterly trend — last 6-8 quarters of revenue + net margin (from Income Statement digest)
- Thesis status — 2-4 bullets, each tagged 🟢 Strengthened / 🟡 Maintained / 🟠 Weakened, grounded in the quarter's numbers
- Street view — rating distribution + target price range (from Consensus digest, do not compute your own)
- Next-quarter consensus — what the Street expects next quarter (from Earnings digest)
- Risks — one line of inline-backtick tags:
`Risk1` · `Risk2` · `Risk3`
Refer to earnings quarterly data for beat/miss flags and consensus analyst_ratings for ratings. Use the user's language for card content; keep numbers/formats consistent with Markdown tables.
Step 3 — Close with upgrade hint
💡 如需完整研报（含 DCF 估值、目标价推导、逐段分析），回复"生成完整报告"。
If the user's language is English, localize the hint accordingly: "Reply 'generate full report' for a complete report with DCF valuation, price target derivation, and segment-level analysis."
Full Report Mode (on explicit request)
Triggered when the user asks for a full report, detailed analysis, or "完整报告".
Step 1 — Collect full data
If a RAW_DIR was printed in the Lite run (same session), reuse it. Otherwise run:
python3 scripts/collect.py <SYMBOL> --full
This adds balance sheet and cash flow statements (12 data sources total).
Step 2 — Fetch earnings call transcript
Run one web search for the earnings call transcript to supplement with management commentary.
Step 3 — Analyze
Read references/full-report.md and follow the analysis framework:
- Beat/miss analysis (revenue, EPS, operating income vs consensus)
- Segment breakdown (revenue and margin by segment)
- Margin analysis (gross, operating, net — YoY and QoQ)
- Guidance assessment (next-quarter and full-year)
- Estimate revision model (forward 1-2 year EPS updates)
Step 4 — Valuation
Read references/valuation-methodologies.md and apply the three-method approach:
- DCF valuation
- Trading comparables
- Precedent transactions
Step 5 — Deliverable
By default, write a Markdown file: [SYMBOL]_Q[N]_[YEAR]_Earnings_Update.md
For institutional use, optionally generate a DOCX report with embedded matplotlib charts (requires python-docx, matplotlib, numpy — the LLM will install on demand).
DOCX workflow: Read data from the RAW_DIR JSON files, write an inline Python script using DocxBuilder and ChartBuilder from scripts/generate_report.py. Pipe JSON data into charts and tables, then call python3 to generate the DOCX.
See references/pre-earnings.md §§Output: DOCX Upgrade Path for the complete code pattern (preview), and references/report-structure.md for the page-by-page DOCX template (full report).
The DOCX path is an upgrade, not the default. Always deliver the Markdown report as the primary full-mode output first.
Data Sources
Primary path: python3 scripts/collect.py <SYMBOL>
- Pure Python stdlib — no pip dependencies
- Fetches all data in one parallel call (10 sources lite, 12 full)
- Prints a structured digest with all key metrics
Fallback (no Python / collect.py fails): Issue individual CLI calls in parallel. Use runtime discovery to find available commands:
GET /api/v1/search?query=<data needed>
This returns matching CLI commands dynamically — do not assume hardcoded command names.
Server setup: See the alphameta skill for alphameta --ibkr startup and command execution syntax via POST /api/v1/execute.
Error Handling
| Situation | LLM Response |
|---|
| Server not reachable (health check fails) | Ask the user to start the AlphaMeta server with alphameta --ibkr. |
| collect.py fails / Python unavailable | Fall back to individual CLI calls in parallel. Use runtime discovery: GET /api/v1/search?query=<data needed>. |
| Partial N/A data sections | Work with available data. Flag gaps in the summary card (e.g. "Segment data unavailable — skipping segment breakdown"). |
| All data sources N/A | Use web search with clear source labeling. Label every data point as "[source: web search]". |
Related Skills
For lighter or differently-framed asks, defer to a sibling:
If the user wants the full earnings report plus one of the above (e.g. "earnings update on TSLA and how it compares to MSFT"), do this skill first, then chain to the other.
Reference Files
| File | Contents | When to Read |
|---|
| pre-earnings.md | Pre-earnings preview: 6 analysis modules + three-tier output (inline / Markdown / DOCX) | Pre-earnings (upcoming release) |
| full-report.md | Full-report workflow: analysis framework, Markdown report structure, quality checklist | Full report mode only |
| report-structure.md | Page-by-page DOCX template (12 pages) with tables, charts, formatting | DOCX upgrade path only |
| valuation-methodologies.md | DCF, trading comps, precedent transactions — full methodology | Full report valuation step |
| scripts/collect.py | Parallel data collector (lite + --full), pure stdlib | Never — just run it |
| scripts/generate_report.py | DocxBuilder + ChartBuilder — DOCX generation library (optional, needs pip) | DOCX upgrade path only |
File Layout
alphameta-earnings/
├── SKILL.md
├── commands/
│ └── earnings.md # /earnings <SYMBOL> slash command
├── references/
│ ├── pre-earnings.md # Pre-earnings preview workflow
│ ├── full-report.md # Full report analysis framework
│ ├── report-structure.md # DOCX 12-page template (upgrade path)
│ └── valuation-methodologies.md # DCF + comps + precedent
├── scripts/
│ ├── collect.py # Parallel data collector (pure stdlib)
│ └── generate_report.py # DOCX generator (python-docx + matplotlib + numpy)
