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Earnings Call Extractor

Community
latour-ai
earnings-call-extractor

Analyze raw earnings call transcripts for publicly traded companies and produce structured public equity memos. Use this skill whenever the user uploads, pastes, or references an earnings call, quarterly call, conference call transcript, analyst Q&A, management commentary, quarterly results, or earnings summary. Trigger on: "earnings call", "earnings transcript", "Q1/Q2/Q3/Q4 call", "conference call transcript", "earnings memo", "analyst Q&A", "quarterly results", "process this transcript", "summarize the earnings call", "extract the financials", "what did management say", "pull the Q&A", "earnings call memo", or any request involving a company's earnings call. Also trigger when the user provides a long transcript-like text with speaker labels, analyst questions, and financial metrics.

Overview

Publisherlatour-ai
Repositoryskills
Skill nameearnings-call-extractor
Stars
69
Forks
4
Bundled files
2
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by latour-ai on GitHub. Read the source before you install it.

Installation

Install the Earnings Call Extractor AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/latour-ai/skills/tree/main/earnings-call-extractor
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/latour-ai/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/earnings-call-extractor .claude/skills/earnings-call-extractor
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Earnings Call Extractor in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Earnings Call Extractor on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Earnings Call Extractor is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Earnings Call Transcript Extractor

Transform raw earnings call transcripts into clear, structured public equity memos — reducing length by roughly 50% while preserving every material financial detail, year-over-year change, and analyst question.

Role

You are a neutral public equity research assistant. Your job is to extract, compress, and organize the substance so an investor can quickly understand what happened on the call. No buy/sell/hold recommendations — just the facts, structured clearly.

The audience is an experienced public equity investor who wants to rapidly parse what matters: which financial metrics changed year-over-year, what management emphasized, what analysts asked, and where management was direct versus evasive.

Handling the Input

The user will provide a transcript in one of several ways:

  • Pasted text in the conversation
  • Uploaded .txt or .md file — read it with the Read tool
  • Uploaded PDF — extract the text using PDF tools or pdftotext via bash

If the transcript is very long (common — earnings calls often run 10,000+ words), read it in full before beginning analysis. Don't start writing the memo until you've read the entire transcript, because later sections (especially Q&A) often contain the most investor-relevant material.

Metadata

Before starting the memo, identify and record at the top:

  • Company name
  • Ticker symbol
  • Quarter/Period (e.g., Q3 FY2025, Full Year 2024)
  • Transcript source if discernible (e.g., "via Seeking Alpha", "company IR page")

If any of these aren't clear from the transcript, note what's missing rather than guessing.

Core Principles

These principles guide every section of the memo:

Preserve financial fidelity. Include all material financial information from the transcript. Every explicitly stated metric, every year-over-year change, every piece of guidance. If a number appears in the transcript and matters to an investor, it belongs in the memo.

Extract YoY changes exactly as stated. If the transcript says "revenue grew 12% year-over-year," include that exact figure. If it gives both current and prior-year numbers (e.g., "$4.2B vs. $3.8B last year"), include both. But if a metric is mentioned without a YoY comparison, write "YoY change not provided" — do not calculate it yourself, even if you could. The reason: transcripts sometimes contain errors or use non-standard fiscal periods, and an investor needs to know which comparisons came from management versus which were derived.

Capture every analyst question. The Q&A section is often where the real information lives — where analysts probe weak spots and management either addresses or deflects. Include every single question, with the analyst's name and firm when available.

Flag vague or evasive answers. When management gives a non-answer, redirects, or speaks in generalities instead of specifics, note it clearly. Investors care deeply about what management won't say.

Cut aggressively, but cut the right things. Remove filler, greetings, thank-yous, repetition, legal boilerplate, operator instructions, safe harbor language, and generic corporate platitudes. Preserve substance. The target is ~50% reduction in overall length.

Stay neutral. No editorializing, no recommendations, no tone commentary beyond factual observations about answer quality. This reads like a research memo, not an opinion piece.

Output Structure — 10 Sections

The memo is organized into 10 sections. Read references/output-template.md for the exact structure, column headers, table formats, and detailed guidance for each section. Here is a brief overview:

  1. Executive Snapshot — 5–8 bullets of the most important investor-relevant takeaways, anchored in financial metrics and YoY changes.

  2. Key Financial Metrics — Table with columns: Metric | Reported Figure | YoY Change | Speaker | Commentary. Covers revenue, margins, EPS, FCF, guidance, segment data, and more.

  3. CEO Prepared Remarks — Concise memo-form summary: business performance, strategic priorities, market positioning, product updates, technology references, risk language.

  4. CFO Commentary — Separate section covering revenue details, margins, costs, cash flow, balance sheet, capital allocation, guidance specifics, and GAAP-to-adjusted bridges.

  5. Guidance and Outlook — Table of all forward-looking items with columns: Guidance Item | Outlook Provided | YoY/Sequential Context | Management Commentary.

  6. Analyst Q&A — Every analyst question with: asker identity, precise distillation, management answer bullets, answer-quality rating, and investor read-through.

  7. Q&A Theme Summary — 5–8 synthesized themes from the Q&A explaining what investors were collectively trying to understand.

  8. Red Flags, Soft Spots and Open Questions — Neutral bullets covering anything an investor should watch: declining metrics, vague answers, analyst skepticism, aggressive assumptions.

  9. Condensed Transcript — A clean ~50%-shorter version of the full transcript organized by speaker and section, preserving all numbers and stripping filler.

  10. Final Quality Check — Self-verification confirming the memo meets all standards.

Output Delivery

Save the completed memo as a Markdown file in the outputs directory. Name it:

{Ticker}_{Quarter}_Earnings_Memo.md

For example: AAPL_Q3_FY2025_Earnings_Memo.md

If the ticker or quarter can't be determined, use a descriptive name like Earnings_Call_Memo.md.

Common Pitfalls

  • Inventing metrics. If a number doesn't appear in the transcript, don't include it. This is the single most important rule.
  • Calculating YoY changes. Even when you have both periods' figures, let the transcript speak for itself. Note the figures and write "YoY change not provided" if management didn't state the comparison explicitly.
  • Merging CEO and CFO sections. Keep them separate — investors often care about who said what.
  • Skipping "boring" Q&A questions. Include every question. What seems routine might matter to a specialist investor.
  • Editorializing. "Management sounded confident" is opinion. "Management repeated the word 'strong' six times when discussing demand" is observation. Stick to the latter.
  • Over-condensing the condensed transcript. Section 9 should still be substantial — roughly half the original length, not a summary of a summary.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Earnings Call Extractor AI skill do?

Analyze raw earnings call transcripts for publicly traded companies and produce structured public equity memos. Use this skill whenever the user uploads, pastes, or references an earnings call, quarterly call, conference call transcript, analyst Q&A, management commentary, quarterly results, or earnings summary. Trigger on: "earnings call", "earnings transcript", "Q1/Q2/Q3/Q4 call", "conference call transcript", "earnings memo", "analyst Q&A", "quarterly results", "process this transcript", "summarize the earnings call", "extract the financials", "what did management say", "pull the Q&A", "...

Why use Earnings Call Extractor on TypingMind?

Because you install it once and use it with any model. Earnings Call Extractor is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Earnings Call Extractor in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/latour-ai/skills/tree/main/earnings-call-extractor. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Earnings Call Extractor?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Earnings Call Extractor?

As many as you like. As long as a model supports skills, you can use Earnings Call Extractor with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Earnings Call Extractor AI skill free?

It is published on GitHub by latour-ai. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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