Earnings Analysis logo

Earnings Analysis

OrganizationPopular
ginlix-ai
earnings-analysis

Post-print earnings update for a covered name: beat/miss decomposition, EPS quality, transcript debate map, estimate revisions, thesis impact. Also the call-only ask that wants the transcript Q&A and the debate map alone. Triggers on earnings update, post-earnings report, analyze quarterly results, Q[N] update, what management said on the call.

Overview

Publisherginlix-ai
RepositoryLangAlpha
Skill nameearnings-analysis
Stars
1.8K
Forks
288
Bundled files
3
LicenseApache-2.0
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.

  • 3 bundled files

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

  • Open source

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

Installation

Install the Earnings Analysis 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.
  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/ginlix-ai/LangAlpha.git /tmp/LangAlpha
mkdir -p .claude/skills
cp -r /tmp/LangAlpha/plugins/langalpha_research/skills/earnings-analysis .claude/skills/earnings-analysis
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Earnings Analysis 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 Analysis 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 Analysis 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 Update

A post-print report on a company already under coverage: what changed this quarter, whether the change recurs, what it does to estimates, and what it does to the thesis. Eight to twelve pages of DOCX, inside 48 hours of the release, written for a reader who already knows the company.

Route elsewhere when the request is a first-time initiation (.agents/skills/initiating-coverage/SKILL.md), a pre-print setup (.agents/skills/earnings-preview/SKILL.md), or a same-morning reaction blurb (.agents/skills/morning-note/SKILL.md).

Evidence labels, source tiers, staleness, the readiness posture and the intake limits: .agents/skills/research-conventions/SKILL.md, read before the first deliverable.

Output modes

ModeFires whenContract
Deep divethe defaultevery phase present, the length budget below, assembled as a DOCX through .agents/skills/docx/SKILL.md
One-pagerthe user asks for a one-pager, quick take or flash noteone page, in this order: decision box, beat/miss table with revenue and EPS variance, EPS-quality verdict, debate map, changed estimate lines with old against new. Delivered in chat unless the user asked for a document, and as a DOCX through .agents/skills/docx/SKILL.md when they did. No chart minimum
Debate map alonethe whole ask is the call or the Q&Athe transcript Q&A map and the debate map, each side carrying a falsifier tied to a dated catalyst. Delivered in chat, no report and no charts around them

A mode is chosen once and holds, and a shorter mode is rebuilt at that depth rather than truncated (.agents/skills/research-conventions/references/depth.md). Missing inputs never shorten the note: a missing artifact stays visible as a labelled gap in the section that wanted it, using the absence vocabulary below.

The length budget, the chart count and the DOCX assembly belong to the deep dive. A short mode is complete on the contract in its own row; the freshness gate, the evidence contract and the tier 1 hard fails in references/best-practices.md bind every mode.

Evidence contract

Every user-facing number and every quote carries a findable citation: the artifact plus a location pointer that puts a reader on the figure in under thirty seconds. A location pointer is a page plus table, a page plus section heading, a slide number, or a transcript line range with the speaker. The document name alone is not a citation.

Sources resolve down one ladder, highest first:

  1. The filed 10-Q or 10-K for the quarter.
  2. The 8-K exhibit that carried the results.
  3. The earnings press release.
  4. The investor deck and the prepared remarks.
  5. The transcript, which is narrative support and never the source for a filed number.

When a document was reissued, cite the final version and keep the original timestamp beside it.

  • Guidance that lives only in call commentary and in no filed document is labelled call-only guidance wherever it appears.
  • Every non-GAAP figure appears with its closest GAAP comparable and the reconciliation source that bridges them.
  • Consensus names the estimate set and its as-of timestamp, or states that the timestamp is unavailable.
  • Absence is one of four words, never a blank and never a bare n/a: not guided (the company declined to guide it), not disclosed (the company does not publish it), not provided (it exists but is absent from the materials in hand), source not found (searched and unresolved, which is the needs-source label in .agents/skills/research-conventions/references/evidence.md).
  • Every source ships as a hyperlink with display text, so a reader sees "10-Q" rather than the raw address, and SEC links point at the EDGAR viewer. The closing Sources section lists every material with its date and its link.

The delivered document is self-contained: a reader holding only the DOCX can follow every number in it without opening the model or the chart folder.

The run

Five phases. Each ends on its stated criterion; the detail behind each lives in references/workflow.md.

Phase 1: Freshness gate

Training data is old and the wrong quarter is the most expensive mistake this skill can make. Write down today's date, search for the most recent release rather than assuming which quarter is latest, and open the actual materials.

Complete when today's date, the release date, the transcript date and the filing date are all written down; the release is within 90 days of today; and every artifact names the same fiscal period, taken verbatim from the event name per .agents/skills/research-conventions/references/market-data-rules.md.

Phase 2: Extraction and beat/miss

Pull reported results, pre-print consensus and our own prior estimates into one comparison, then decompose the variance by segment, geography, product and channel.

Complete when every headline metric has reported, expected and variance side by side; each cell carries a findable citation; every rate variance is stated in basis points; and reported and constant-currency figures sit in separate columns.

Phase 3: Quality and drivers

The analytical core: the EPS-quality screen, the two or three load-bearing drivers, the cash-quality check, the guidance read, the transcript Q&A map and the debate map.

Complete when the EPS-quality screen has either produced a recurring-EPS bridge or recorded "no material trigger identified"; two to three drivers are named with what moved, why it moved and what it does to forward expectations; the cash-quality module reconciles earnings to cash; and the debate map carries a falsifier on each side tied to a dated catalyst.

Phase 4: Estimates, valuation and model update

Revise forward estimates, restate or move the price target, and produce the model update in packet form unless the user supplied a workbook and asked for it to be written.

Complete when every changed line shows old, new and a one-clause reason; the price target is explicitly changed or explicitly maintained with its reason; and the update mode (packet or apply) is stated in the delivery message.

Phase 5: Charts, report and gates

In the deep dive, build eight to twelve charts and assemble the DOCX through .agents/skills/docx/SKILL.md. In a short mode, skip the charts and the document. Either way, run the three quality gates in references/best-practices.md.

Complete when the hard-fail list is clean, the delivery checklist is ticked for everything the chosen mode produces, the judgement gate passes on a note that answers what changed rather than summarising the quarter, and one posture from the ladder in .agents/skills/research-conventions/SKILL.md is stated near the top.

Length budget (deep dive)

DimensionTarget
Pages8 to 12
Words3,000 to 5,000
Summary tables1 to 3, never a full P&L
Charts8 to 12, quarterly trends and changes
Typographyset by .agents/skills/docx/SKILL.md

Deliverable

[Company]_Q[X]_[Year]_Earnings_Update.docx, for example Nike_Q2_FY24_Earnings_Update.docx. Charts come from Python (matplotlib, pandas). A workbook update is optional and follows the packet-or-apply rule in Phase 4.

Reference files

  • The phase you are running, its steps and its tables: references/workflow.md.
  • Writing a page or a section of the report, or the exact shape of the decision box, the recurring-EPS bridge, the Q&A map or the debate map: references/report-structure.md.
  • Before delivery, and whenever a headline or a claim needs calibrating: references/best-practices.md.

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 Analysis AI skill do?

Post-print earnings update for a covered name: beat/miss decomposition, EPS quality, transcript debate map, estimate revisions, thesis impact. Also the call-only ask that wants the transcript Q&A and the debate map alone. Triggers on earnings update, post-earnings report, analyze quarterly results, Q[N] update, what management said on the call.

Why use Earnings Analysis on TypingMind?

Because you install it once and use it with any model. Earnings Analysis 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 Analysis in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ginlix-ai/LangAlpha/tree/main/plugins/langalpha_research/skills/earnings-analysis. 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 Analysis?

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 Analysis?

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

Is the Earnings Analysis AI skill free?

Yes. It is published on GitHub by ginlix-ai under the Apache-2.0 license. 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇