Model Update logo

Model Update

OrganizationPopular
anthropics
model-update

Update financial models with new data — quarterly earnings, management guidance, macro changes, or revised assumptions. Adjusts estimates, recalculates valuation, and flags material changes. Use after earnings, guidance updates, or when assumptions need refreshing. Triggers on "update model", "plug earnings", "refresh estimates", "update numbers for [company]", "new guidance", or "revise estimates".

Overview

Publisheranthropics
Repositoryfinancial-services
Skill namemodel-update
Stars
38.4K
Forks
5.5K
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by anthropics on GitHub. Read the source before you install it.

Installation

Install the Model Update 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/anthropics/financial-services/tree/main/plugins/agent-plugins/earnings-reviewer/skills/model-update
  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/anthropics/financial-services.git /tmp/financial-services
mkdir -p .claude/skills
cp -r /tmp/financial-services/plugins/agent-plugins/earnings-reviewer/skills/model-update .claude/skills/model-update
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Model Update 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 Model Update 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 Model Update 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.

Model Update

Workflow

Step 1: Identify What Changed

Determine the update trigger:

  • Earnings release: New quarterly actuals to plug in
  • Guidance change: Company updated forward outlook
  • Estimate revision: Analyst changing assumptions based on new data
  • Macro update: Interest rates, FX, commodity prices changed
  • Event-driven: M&A, restructuring, new product, management change

Step 2: Plug New Data

After Earnings

Update the model with reported actuals:

Line ItemPrior EstimateActualDeltaNotes
Revenue
Gross Margin
Operating Expenses
EBITDA
EPS
[Key metric 1]
[Key metric 2]

Segment Detail (if applicable):

  • Update each segment's revenue and margin
  • Note any segment mix shifts

Balance Sheet / Cash Flow Updates:

  • Cash and debt balances
  • Share count (buybacks, dilution)
  • Capex actual vs. estimate
  • Working capital changes

Step 3: Revise Forward Estimates

Based on the new data, adjust forward estimates:

Old FY EstNew FY EstChangeOld Next FYNew Next FYChange
Revenue
EBITDA
EPS

Key Assumption Changes:

  • What assumptions are you changing and why?
  • Revenue growth rate: old → new (reason)
  • Margin assumption: old → new (reason)
  • Any new items (restructuring charges, one-time gains, etc.)

Step 4: Valuation Impact

Recalculate valuation with updated estimates:

Valuation MethodPriorUpdatedChange
DCF fair value
P/E (NTM EPS × target multiple)
EV/EBITDA (NTM EBITDA × target multiple)
Price Target

Step 5: Summary & Action

Estimate Change Summary:

  • One paragraph: what changed, why, and what it means for the stock
  • Is this a thesis-changing event or noise?

Rating / Price Target:

  • Maintain or change rating?
  • New price target (if changed) with methodology
  • Upside/downside to current price

Step 6: Output

  • Updated Excel model (if user provides the existing model)
  • Estimate change summary (markdown or Word)
  • Updated price target derivation

Important Notes

  • Always reconcile your estimates to the company's reported figures before projecting forward
  • Note any non-recurring items and whether your estimates are GAAP or adjusted
  • Track your estimate revision history — it shows your analytical progression
  • If the quarter was noisy, separate signal from noise in your estimate changes
  • Check consensus after updating — how do your revised estimates compare to the Street?
  • Share count matters — dilution from stock comp, converts, or buybacks can materially affect EPS

Frequently asked questions

What does the Model Update AI skill do?

Update financial models with new data — quarterly earnings, management guidance, macro changes, or revised assumptions. Adjusts estimates, recalculates valuation, and flags material changes. Use after earnings, guidance updates, or when assumptions need refreshing. Triggers on "update model", "plug earnings", "refresh estimates", "update numbers for [company]", "new guidance", or "revise estimates".

Why use Model Update on TypingMind?

Because you install it once and use it with any model. Model Update 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 Model Update in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/anthropics/financial-services/tree/main/plugins/agent-plugins/earnings-reviewer/skills/model-update. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Model Update?

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 Model Update?

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

Is the Model Update AI skill free?

Yes. It is published on GitHub by anthropics 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.

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