qveris-day1global-skills

v2026.09.25

QVeris-native adaptation of candidate 7, Day1Global Skills. Use for global and technology investment memo templates rebuilt as trace-backed QVeris research with market, fundamentals, estimates, macro, geography, and research evidence.

GitHub
安装命令
npx skhub add qverisai/qveris-day1global-skills
Markdown
SKILL.md

QVeris Day1Global Skills

Use this skill to turn Day1Global Skills into a QVeris-native global/technology memo template. Treat the original repository as methodology and template reference only; do not import its execution chain.

Source record:

FieldValue
Candidate number7
Original repositoryDay1Global Skills
GitHub URLhttps://github.com/star23/Day1Global-Skills
LicenseMIT
Evaluation recent activity2026-04-15
Local source snapshotthird_party/source_repos/07-day1global-skills
Snapshot latest commit562c14b on 2026-04-15

Runtime Contract

  • Use only qveris_finance.* CAP tools and QVERIS_API_KEY.
  • Resolve the company, security, market, country, and industry with QVeris reference tools first.
  • 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.
  • Include qveris_trace for every market, fundamental, estimate, macro, and research claim.
  • List missing_fields for geography, segment, or macro gaps.
  • 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, country, region, 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. Global/tech memo: ref_security_master, ref_company_profile, mkt_l1_rt, fundamentals_is, fundamentals_bs, fundamentals_cf, estimates_consensus, news_fin_tagged, research_analyst_reports.
  2. Sector/geography context: ref_classification_industry, index_metadata, index_levels, macro_indicators, fx_spot.
  3. Optional technology context: ref_classification_theme, alt_patents, alt_job_postings, alt_supply_chain.

Output Requirements

  • Use schemas/output.schema.json.
  • Preserve memo structure: company context, market/geography backdrop, business drivers, evidence table, contrary evidence, risks, and next checks.
  • If FX, macro, or research payloads fail or return the wrong region/content type, mark them missing or low confidence; do not use OpenAlex-style academic rows as sell-side research.
  • Do not present a recommendation, position decision, or target price commitment.
  • 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 import original code, non-QVeris data adapters, 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.
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版本
最新版本元数据

版本

v2026.09.25

发布时间

Sep 25, 2026

分类

未分类

许可证

MIT

源路径

qveris-day1global-skills

默认分支

main

最新提交

bb4e480

Tree SHA

adcc8d1