Bigdata Catalyst Monitor

作者 Bigdata-com2a52a0013662无许可证2 个星标收录于 2026年10月8日更新于 2026年10月8日仓库昨天更新

Map the dated events that could move a public company over the next few quarters, using Bigdata.com data (events calendar, filings, news, tearsheet). Covers scheduled catalysts — earnings, investor days, index reviews, lock-up and patent expiries, regulatory decision dates — and foreseeable unscheduled ones — litigation milestones, product cycles, contract renewals, refinancings. Each catalyst carries a date or window, likely direction, magnitude, confidence, and what to watch, ranked by expected impact rather than by date alone. Triggers: "catalyst monitor for X", "what's coming up for X", "upcoming catalysts for X", "what could move X", "key dates for X", "event calendar for X", "what should I watch on X".

AI 生成的概览

为上市公司构建未来可能影响股价的有日期事件日历,并按预期影响排序。

功能
使用 Bigdata.com 插件工具为上市公司生成前瞻性催化剂日历。它会解析公司身份,获取已排定的财报与会议日期,采集基准预期与估值倍数,并针对可确定日期的事件进行定向检索,例如监管决定、诉讼节点、产品周期、合同续约、再融资、专利到期和指数评审。每项催化剂按日期或时间窗口、类型、可能方向、影响幅度、置信度及观察要点进行评级,然后按预期影响排序并另附时间顺序列表。输出遵循报告模板,默认 Markdown,可选 Word 或演示文稿版本。
适用场景
当问题关注公司接下来会发生什么,而非过去已发生什么时使用。适用于查询即将到来的催化剂、关键日期、事件日历,或未来几个季度可能推动股价的因素。不适用于回顾期总结、深度财报前瞻、概率加权情景分析,或没有解决日期的长期风险。
运行要求
需要访问 Bigdata.com 插件工具:find_securities、bigdata_events_calendar、bigdata_company_tearsheet 和 bigdata_search,且除 search 与 fetch 外每次调用都需将 plugin_slug 设为该技能名称。不附带脚本,仅为说明文档,另含报告模板与参考文件。

Bigdata Catalyst Monitor

Forward calendar of what could move the name, ranked by impact. Use Bigdata.com plugin tools for every fact.

Use this skill when the question is "what's coming". Not this skill when:

RequestUse instead
What already happened over the past monthCompany brief
Deep analysis of the next earnings printEarnings preview
Probability-weighted outcomes and valuesScenario analysis
Risks rated by likelihood and impactRisk assessment

Catalyst vs risk: a catalyst is a dated or datable event that resolves something. A standing risk with no resolution date belongs in a risk assessment.

Data foundation (plugin tools)

ToolPurposePrerequisite
find_securitiesResolve company name → RavenPack entity_idNone
bigdata_events_calendarScheduled earnings and conferencesfind_securities
bigdata_company_tearsheetBaseline financials, estimates, what the price embedsfind_securities
bigdata_searchRegulatory dates, litigation milestones, product cycles, filingsNone

Required on every call: pass plugin_slug: "bigdata-catalyst-monitor" in the request parameters of every Bigdata.com plugin tool call made while running this skill. The value is always the skill name, bigdata-catalyst-monitor, regardless of the company or query.

Exceptions: the search and fetch tools do not accept plugin_slug — omit it there.

If the company name is ambiguous after find_securities, ask:

"I found multiple companies named [X]. Did you mean [Company A] in [Industry] or [Company B] in [Industry]?"

Workflow

Step 1 — Identify the company

Call find_securities with the company name to get the entity_id.

Step 2 — Scheduled events

Call bigdata_events_calendar for earnings dates and conferences over the next 2–4 quarters. Note the fiscal calendar so quarter-ends and guidance updates land correctly.

Step 3 — Baseline: what is already priced

Call bigdata_company_tearsheet. A catalyst only matters relative to expectations — capture consensus estimates, current multiples, and sentiment so each catalyst can be framed against what the market already assumes.

Step 4 — Search for datable events

Run 6–8 targeted searches:

  • "[Company] upcoming product launch roadmap timeline"
  • "[Company] regulatory decision approval date FDA / FTC / EU"
  • "[Company] lawsuit trial date court ruling expected"
  • "[Company] investor day capital markets day guidance update"
  • "[Company] contract renewal expiry major customer"
  • "[Company] debt maturity refinancing schedule"
  • "[Company] patent expiry exclusivity loss"
  • "[Company] index inclusion review lock-up expiry"

Include the industry-specific ones that apply — clinical readouts, license renewals, rate cases, spectrum auctions, model launches.

Step 5 — Rate each catalyst

For every catalyst record:

  • Date or window — exact where known, quarter where not; mark undated but expected items as such
  • Type — scheduled or foreseeable
  • Likely direction — positive / negative / two-sided
  • Magnitude — high / medium / low, tied to a value driver where possible ("~$Nm revenue", "~Xbps margin", "removes overhang on segment Y")
  • Confidence — how sure the date and the outcome are; these are different, and both matter
  • What to watch — the specific signal that tells you which way it resolved

Step 6 — Rank and sequence

Rank by expected impact, not chronology — a large two-sided event in nine months usually matters more than a routine print next week. Then give the chronological calendar separately, so the reader gets both views.

Close with the 2–3 catalysts that dominate the next few quarters and what each would change.

Output

Follow assets/report-template.md exactly — section order, tables, sources, and footer.

  • Add inline citations [1], [2] immediately after claims, hyperlinked to the document URL.
  • Every deliverable ends with the Powered by Bigdata.com line and the Disclaimer, verbatim.
  • Default format is Markdown; offer a Word (.docx) or presentation version if useful.

Quality bar

Non-negotiables:

  • Every catalyst carries a date or window — undated speculation is not a catalyst
  • Direction, magnitude, and confidence given separately; date confidence distinguished from outcome confidence
  • Magnitude tied to a value driver, not just labelled "high"
  • Ranked by impact and listed chronologically
  • Standing risks with no resolution date excluded — they belong in a risk assessment
  • Facts separated from analysis and implications

来源与署名

来源:Bigdata-com/bigdata-plugins-marketplace位于plugins/bigdata-com/skills/bigdata-catalyst-monitor提交2a52a00

许可证: 无许可证

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