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events

Skill17 starsChanged 7 days agoMITView on GitHub

Measures how the market priced a dated event such as earnings, guidance, or a rating change. Use when asked how a stock reacted to news, whether a move after an announcement was abnormal or significant, or for an event study, abnormal return, CAR, or post announcement reaction.

17 other projects use a version of this.

Adopted by

Most recently taken up by

Used with

Projects with it also use influencer-marketing, marketing-council and 10 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
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    marketing-loops

    When the user wants to set up a recurring, self-running marketing workflow — a repeatable loop an AI agent runs on a cadence (weekly, daily, on a trigger) rather than a one-off task. Also use when the user mentions 'marketing loop,' 'recurring marketing workflow,' 'automate my marketing,' 'marketing on autopilot,' 'weekly marketing review,' 'ad fatigue check,' 'weekly ads check,' 'content refresh loop,' 'churn watch,' 'ranking drop alert,' 'run my SEO,' 'SEO operator,' 'daily SEO agent,' 'run my outbound,' 'AI SDR,' 'reply triage,' 'always-on marketing,' 'marketing automation workflow,' or 'run this every week.' Use this to pick, adapt, and schedule an ongoing marketing loop that orchestrates the other marketing skills. For one-off marketing ideas, see marketing-ideas. For the experimentation loop specifically, see ab-testing.

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    When the user wants help with public relations, earned media, press coverage, journalist outreach, or media strategy (not pull requests). Also use when the user mentions 'PR,' 'public relations,' 'press,' 'press release,' 'press coverage,' 'media outreach,' 'pitch a journalist,' 'get featured,' 'media list,' 'media kit,' 'press kit,' 'newsjacking,' 'news hijack,' 'HARO,' 'Qwoted,' 'Featured,' 'Help A Reporter,' 'reporter request,' 'tech press,' 'TechCrunch,' 'earned media,' 'thought leadership placement,' 'op-ed,' 'guest article,' 'press contacts,' or 'how do I get press.' Use this for earned media work — finding journalists, pitching stories, newsjacking, and responding to press requests. For startup/SaaS/AI directory submissions, see directory-submissions. For product launches, see launch. For social-media engagement, see social. For cold-email outreach to prospects, see cold-email.

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    popups

    When the user wants to create or optimize popups, modals, overlays, slide-ins, or banners for conversion purposes. Also use when the user mentions "exit intent," "popup conversions," "modal optimization," "lead capture popup," "email popup," "announcement banner," "overlay," "collect emails with a popup," "exit popup," "scroll trigger," "sticky bar," or "notification bar." Use this for any overlay or interrupt-style conversion element. For forms outside of popups, see cro. For general page conversion optimization, see cro.

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  • roerohanSkill

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    When the user wants to create or optimize an email sequence, drip campaign, automated email flow, or lifecycle email program. Also use when the user mentions "email sequence," "drip campaign," "nurture sequence," "onboarding emails," "welcome sequence," "re-engagement emails," "email automation," "lifecycle emails," "trigger-based emails," "email funnel," "email workflow," "what emails should I send," "welcome series," or "email cadence." Use this for any multi-email automated flow. For cold outreach emails, see cold-email. For in-app onboarding, see onboarding.

    Also in 7 projects that use this

  • +6 more from Cesarjoquin

The file

We split this skill into the rules it gives an agent

3 rules. 0 are common, 2 are distinctive, and 1 is very specific to this project.

  • 0

    Common

    Most of the 15 edited versions we read keep these.

  • 2

    Distinctive

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    If the user names a stock and date but provides no prices, ask for them.

    line 22
  2. Very specific:

    Use --window 0 0 for the announcement day alone, or a longer window such as 0 5 to study drift.

    line 36
  3. Distinctive:

    If any check fails, report it and stop.

    line 45

“Common” is measured: kept by at least 6 in 10 of the 15 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Event study

Answers one question: how did the market price this news? Uses the market model event study (MacKinlay, 1997). Every number comes from the scripts.

Inputs

  • prices.csv: a date column, then total return price levels (dividends reinvested) for the security and for a market index. Daily, complete, no gaps.
  • The security column, the market column, and the event date.
  • Day 0 is the first trading session on or after the event date. If the news came after the close, use the next session's date as the event date and say so.
  • At least 270 trading days of prices before the event.

If the user names a stock and date but provides no prices, ask for them. Never fill gaps or invent prices.

Procedure

  1. Resolve the security, the market index, and the event date (adjust for releases after the close).
  2. Fit the market model on 250 days ending 20 days before the event:
    python scripts/fit_market_model.py --prices prices.csv --asset XYZ --market INDEX \
  --event-date 2025-05-01 --out fit.json
  3. Compute AR, CAR and t over the event window (default days -1 to +1):
    python scripts/car.py --fit fit.json --window -1 1
    Use --window 0 0 for the announcement day alone, or a longer window such as 0 5 to study drift.
  4. Report CAR, t, the windows used, and the check results.

Checks

  • The estimation window is complete and ends before the event window begins (windows never overlap).
  • Prices are positive, complete total return levels.
  • The event window lies inside the data, and day 0 matches the fit.

If any check fails, report it and stop.

Output

car.py returns results.car, results.t_stat, results.p_value_normal, the daily abnormal returns, and the fitted alpha, beta and residual volatility, plus checks and passed.

Explaining the result

  • State the CAR as a percentage, the window, and the t statistic. Values of t beyond about 2 in absolute size are unusual under normal day to day noise.
  • Separate the abnormal move from the market move: the market model removes what the index explains.
  • One event is a single observation. Confounding news in the same window (another announcement, an index change) can drive the result; mention it when known.

For the mathematics and design choices, see references/methodology.md.

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