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Estimate Analysis

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himself65
estimate-analysis

Deep-dive into analyst estimates and revision trends for any stock using Yahoo Finance data. Use when the user wants to understand analyst estimate direction, how EPS or revenue forecasts changed over time, compare estimate distributions, or analyze growth projections across periods. Triggers: "estimate analysis for AAPL", "analyst estimate trends for NVDA", "EPS revisions for TSLA", "how have estimates changed for MSFT", "estimate revisions", "EPS trend", "revenue estimates", "consensus changes", "analyst estimates", "estimate distribution", "growth estimates for", "estimate momentum", "revision trend", "forward estimates", "next quarter estimates", "annual estimates", "estimate spread", "bull vs bear estimates", "estimate range", or any request about tracking or comparing analyst estimates/revisions. Use this skill when the user asks about estimates beyond a simple lookup — if they want context, trends, or analysis, this is the right skill.

Overview

Publisherhimself65
Repositoryfinance-skills
Skill nameestimate-analysis
Stars
3.3K
Forks
378
Bundled files
1
LicenseMIT
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.

  • 1 bundled files

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

  • Open source

    Published by himself65 on GitHub. Read the source before you install it.

Installation

Install the Estimate 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/himself65/finance-skills.git /tmp/finance-skills
mkdir -p .claude/skills
cp -r /tmp/finance-skills/plugins/market-analysis/skills/estimate-analysis .claude/skills/estimate-analysis
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Estimate 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 Estimate 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 Estimate 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.

Estimate Analysis Skill

Deep-dives into analyst estimates and revision trends using Yahoo Finance data via yfinance. Covers EPS and revenue estimate distributions, revision momentum, growth projections, and multi-period comparisons — the full picture of where the street thinks a company is heading.

Important: Data is for research and educational purposes only. Not financial advice. yfinance is not affiliated with Yahoo, Inc.


Step 1: Ensure yfinance Is Available

Current environment status:

!`python3 -c "exec('try:\n import yfinance\n print(\'yfinance \' + yfinance.__version__ + \' installed\')\nexcept Exception:\n print(\'YFINANCE_NOT_INSTALLED\')')"`

If YFINANCE_NOT_INSTALLED, install it:

python
import subprocess, sys
subprocess.check_call([sys.executable, "-m", "pip", "install", "-q", "yfinance"])

If already installed, skip to the next step.


Step 2: Identify the Ticker and Gather Estimate Data

Extract the ticker from the user's request. Fetch all estimate-related data in one script.

python
import yfinance as yf
import pandas as pd

ticker = yf.Ticker("AAPL")  # replace with actual ticker

# --- Estimate data ---
earnings_est = ticker.earnings_estimate      # EPS estimates by period
revenue_est = ticker.revenue_estimate        # Revenue estimates by period
eps_trend = ticker.eps_trend                 # EPS estimate changes over time
eps_revisions = ticker.eps_revisions         # Up/down revision counts
growth_est = ticker.growth_estimates         # Growth rate estimates

# --- Historical context ---
earnings_hist = ticker.earnings_history      # Track record
info = ticker.info                           # Company basics
quarterly_income = ticker.quarterly_income_stmt  # Recent actuals

What each data source provides

Data SourceWhat It ShowsWhy It Matters
earnings_estimateCurrent EPS consensus by period (0q, +1q, 0y, +1y)The estimate levels — what analysts expect
revenue_estimateCurrent revenue consensus by periodTop-line expectations
eps_trendHow the EPS estimate has changed (7d, 30d, 60d, 90d ago)Revision direction — rising or falling expectations
eps_revisionsCount of upward vs downward revisions (7d, 30d)Revision breadth — are most analysts raising or cutting?
growth_estimatesGrowth rate estimates vs peers and sectorRelative positioning
earnings_historyActual vs estimated for last 4 quartersCalibration — how good are these estimates historically?

Step 3: Route Based on User Intent

The user might want different levels of analysis. Route accordingly:

User RequestFocus AreaKey Sections
General estimate analysisFull analysisAll sections
"How have estimates changed"Revision trendsEPS Trend + Revisions
"What are analysts expecting"Current consensusEstimate overview
"Growth estimates"Growth projectionsGrowth Estimates
"Bull vs bear case"Estimate rangeHigh/low spread analysis
Compare estimates across periodsMulti-periodPeriod comparison table

When in doubt, provide the full analysis — more context is better.


Step 4: Build the Estimate Analysis

Section 1: Estimate Overview

Present the current consensus for all available periods from earnings_estimate and revenue_estimate:

EPS Estimates:

PeriodConsensusLowHighRange Width# AnalystsYoY Growth
Current Qtr (0q)$1.42$1.35$1.50$0.15 (10.6%)28+12.7%
Next Qtr (+1q)$1.58$1.48$1.68$0.20 (12.7%)25+8.3%
Current Year (0y)$6.70$6.50$6.95$0.45 (6.7%)30+10.2%
Next Year (+1y)$7.45$7.10$7.85$0.75 (10.1%)28+11.2%

Revenue Estimates:

PeriodConsensusLowHigh# AnalystsYoY Growth
Current Qtr$94.3B$92.1B$96.8B25+5.4%
Next Qtr$102.1B$99.5B$105.0B22+6.1%

Calculate and flag:

  • Range width as % of consensus — wide ranges (>15%) signal high uncertainty
  • Analyst coverage — fewer than 5 analysts means thin coverage, note this
  • Growth trajectory — is growth accelerating or decelerating across periods?

Section 2: Revision Trends (EPS Trend)

This is often the most actionable section. From eps_trend, show how estimates have moved:

PeriodCurrent7 Days Ago30 Days Ago60 Days Ago90 Days Ago
Current Qtr$1.42$1.41$1.40$1.38$1.35
Next Qtr$1.58$1.57$1.56$1.55$1.54
Current Year$6.70$6.68$6.65$6.58$6.50
Next Year$7.45$7.43$7.40$7.35$7.28

Summarize the trend: "Current quarter EPS estimates have risen 5.2% over the last 90 days, with most of the increase in the last 30 days — accelerating upward revision momentum."

Key interpretation:

  • Rising estimates ahead of earnings = positive setup (the bar is rising)
  • Falling estimates = analysts cutting numbers, often a negative signal
  • Flat estimates = no new information being priced in
  • Recent acceleration/deceleration matters more than the total move

Section 3: Revision Breadth (EPS Revisions)

From eps_revisions, show the up vs. down count:

PeriodUp (last 7d)Down (last 7d)Up (last 30d)Down (last 30d)
Current Qtr51123
Next Qtr3285

Calculate a revision ratio: Up / (Up + Down). Ratios above 0.7 are strongly bullish; below 0.3 are bearish.

Section 4: Growth Estimates

From growth_estimates, compare the company's expected growth to benchmarks:

EntityCurrent QtrNext QtrCurrent YearNext YearPast 5Y Annual
AAPL+12.7%+8.3%+10.2%+11.2%+14.5%
Industry+9.1%+7.0%+8.5%+9.0%
Sector+11.3%+8.8%+10.0%+10.5%
S&P 500+7.5%+6.2%+8.0%+8.5%

Highlight whether the company is expected to grow faster or slower than its peers.

Section 5: Historical Estimate Accuracy

From earnings_history, assess how reliable estimates have been:

QuarterEstimateActualSurprise %Direction
Q3 2024$1.35$1.40+3.7%Beat
Q2 2024$1.30$1.33+2.3%Beat
Q1 2024$1.52$1.53+0.7%Beat
Q4 2023$2.10$2.18+3.8%Beat

Calculate:

  • Beat rate: X of 4 quarters
  • Average surprise: magnitude and direction
  • Trend in surprise: Are beats getting bigger or smaller? A shrinking surprise with rising estimates could mean the bar is catching up to reality.

Step 5: Synthesize and Respond

Present the analysis with clear structure:

  1. Lead with the key insight: "AAPL estimates are trending higher across all periods, with positive revision breadth (80% of recent revisions are upward)."

  2. Show the tables for each section the user cares about

  3. Provide interpretive context:

    • Is the revision trend confirming or contradicting the stock's recent price action?
    • How does the growth outlook compare to what's priced into the current P/E?
    • What's the relationship between estimate accuracy history and current estimate levels?
  4. Flag risks and nuances:

    • Estimates cluster around consensus — the "real" distribution of outcomes is wider than low/high suggests
    • Revision momentum can reverse quickly on a single data point (guidance change, macro event)
    • Yahoo Finance estimates may lag behind real-time consensus providers by hours or days
    • Growth estimates for out-years (+1y) are inherently less reliable

Caveats to always include

  • Analyst estimates reflect a consensus view, not certainty
  • Estimate revisions are a signal but not a guarantee of future performance
  • This is not financial advice

Reference Files

  • references/api_reference.md — Detailed yfinance API reference for all estimate-related methods

Read the reference file when you need exact return formats or edge case handling.

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 Estimate Analysis AI skill do?

Deep-dive into analyst estimates and revision trends for any stock using Yahoo Finance data. Use when the user wants to understand analyst estimate direction, how EPS or revenue forecasts changed over time, compare estimate distributions, or analyze growth projections across periods. Triggers: "estimate analysis for AAPL", "analyst estimate trends for NVDA", "EPS revisions for TSLA", "how have estimates changed for MSFT", "estimate revisions", "EPS trend", "revenue estimates", "consensus changes", "analyst estimates", "estimate distribution", "growth estimates for", "estimate momentum", "re...

Why use Estimate Analysis on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/himself65/finance-skills/tree/main/plugins/market-analysis/skills/estimate-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 Estimate 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 Estimate Analysis?

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

Is the Estimate Analysis AI skill free?

Yes. It is published on GitHub by himself65 under the MIT 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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