qveris-langalpha

name	qveris-langalpha
description	QVeris-native adaptation of candidate 2, LangAlpha. Use for DCF, earnings analysis, earnings preview, and sector overview workflows that preserve LangAlpha-style schemas while routing all financial data through qveris_finance.* CAP tools.

QVeris LangAlpha

Use this skill for DCF assumptions, sensitivity analysis, earnings post-mortems/previews, and sector overview reports adapted from LangAlpha. Preserve the original workflow categories, but replace fundamentals, market, and macro MCP access with QVeris finance CAP calls.

Source record:

Field	Value
Candidate number	2
Original repository	LangAlpha
GitHub URL	https://github.com/ginlix-ai/LangAlpha
License	Apache-2.0
Evaluation recent activity	2026-07-06
Local source snapshot	third_party/source_repos/02-langalpha
Snapshot latest commit	deab98e on 2026-07-06

Runtime Contract

• Use only qveris_finance.* CAP tools and QVERIS_API_KEY.
• Resolve tickers, exchanges, companies, and CIKs with ref_symbology, ref_security_master, and ref_company_profile.
• Accept dry_run, max_calls, max_age, and budget_note; if omitted in a natural-language request, default to dry_run=false, max_calls=12, max_age=P1D, and a conservative budget note, then echo those controls.
• Attach qveris_trace to every output section and list missing_fields without backfilling.
• Treat QVeris _meta.source_provider as provenance only; never call, request credentials for, or depend on those internal providers directly.
• Suppress analyst_target_price, target_price, price-objective, upside, buy/sell, and recommendation fields even if a QVeris payload contains them.
• Sanity-check entity, market, date window, fiscal period, and payload shape before using data; if a payload is stale, cross-period, truncated, or semantically mismatched, mark it in data_quality and missing_fields.

Workflows

1. DCF model: fundamentals_is, fundamentals_bs, fundamentals_cf, fundamentals_derived_ratios, estimates_consensus, rates_govt_benchmark, mkt_l1_rt.
2. Earnings analysis/preview: event_calendar_earnings, earnings_actual_surprise, estimates_consensus, transcripts_earnings_call, news_fin_realtime.
3. Sector overview: ref_classification_industry, index_constituents, index_levels, flow_sector_capital, mkt_breadth_internals.

Output Requirements

• Use schemas/output.schema.json for machine-readable output.
• Report assumptions, sensitivity ranges, missing inputs, and confidence.
• Align DCF statement inputs by fiscal year/quarter before calculating; if income statement, balance sheet, cash flow, estimates, or rates arrive on different periods, do not blend them into one scenario table.
• If rates_govt_benchmark fails, mark the risk-free-rate input missing instead of substituting a stale or non-QVeris value.
• Keep valuation outputs as scenario ranges and assumption audits; do not present target price commitments.
• Include source_record, controls, analysis, risk_notes, missing_fields, and qveris_trace.
• Include data_quality with status, stale fields, out-of-window events, and suppressed fields when applicable.
• End with: 不构成投资建议 / Not investment advice.

Prohibited Capabilities

Do not use non-QVeris fundamentals/market/macro MCPs, EODHD, Yahoo, FMP, Alpha Vantage, Polygon, AkShare, Snowball, Sina, SEC scraping, Longbridge, FinViz, Alpaca, browser automation, cookies, login state, third-party API keys, automated trading, wallet/swap, buy/sell points, portfolio action instructions, or target price commitments.

References

• Read references/qveris-tool-map.md before choosing tool calls.
• Use fixtures/qveris/sample-output.json as the minimum output shape.