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Deep-dive into analyst estimates and revision trends for any

Estimate Analysis Skill

by himself65 · MIT license · ★ 3,347 on GitHub · updated Aug 27

Estimate Analysis Skill cover

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…

---
name: estimate-analysis
description: >
  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

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.

Manual install (npx, ZIP)
Claude Codenpx skills add himself65/finance-skills --skill estimate-analysis -g -a claude-code
Claude Code (manual)mkdir -p ~/.claude/skills && curl -sL https://codeload.github.com/himself65/finance-skills/tar.gz/0a5759bca1ea \ | tar -xz -C ~/.claude/skills --strip-components=4 finance-skills-0a5759bca1ea/plugins/market-analysis/skills/estimate-analysis
Windows (PowerShell)New-Item -ItemType Directory -Force "$HOME\.claude\skills" | Out-Null; Invoke-WebRequest "https://m.agentalley.io/zip/4775668c1a73f48bc8e5/estimate-analysis.zip" -OutFile "$env:TEMP\estimate-analysis.zip"; Expand-Archive "$env:TEMP\estimate-analysis.zip" -DestinationPath "$HOME\.claude\skills" -Force
Claude.ai / desktopDownload the ZIP and upload it under Settings → Capabilities → Skills.
Codex, Cursor, other agentsPoint them at SKILL.md. The other files are plain text.

Connects with Agent Alley MCP · agentalley.io/mcp

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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:

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.

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 Source What It Shows Why It Matters
earnings_estimate Current EPS consensus by period (0q, +1q, 0y, +1y) The estimate levels — what analysts expect
revenue_estimate Current revenue consensus by period Top-line expectations
eps_trend How the EPS estimate has changed (7d, 30d, 60d, 90d ago) Revision direction — rising or falling expectations
eps_revisions Count of upward vs downward revisions (7d, 30d) Revision breadth — are most analysts raising or cutting?
growth_estimates Growth rate estimates vs peers and sector Relative positioning
earnings_history Actual vs estimated for last 4 quarters Calibration — how good are these estimates historically?

Step 3: Route Based on User Intent

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

User Request Focus Area Key Sections
General estimate analysis Full analysis All sections
"How have estimates changed" Revision trends EPS Trend + Revisions
"What are analysts expecting" Current consensus Estimate overview
"Growth estimates" Growth projections Growth Estimates
"Bull vs bear case" Estimate range High/low spread analysis
Compare estimates across periods Multi-period Period 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:

Period Consensus Low High Range Width # Analysts YoY 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:

Period Consensus Low High # Analysts YoY Growth
Current Qtr $94.3B $92.1B $96.8B 25 +5.4%
Next Qtr $102.1B $99.5B $105.0B 22 +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?

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

Period Current 7 Days Ago 30 Days Ago 60 Days Ago 90 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:

Period Up (last 7d) Down (last 7d) Up (last 30d) Down (last 30d)
Current Qtr 5 1 12 3
Next Qtr 3 2 8 5

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:

Entity Current Qtr Next Qtr Current Year Next Year Past 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:

Quarter Estimate Actual Surprise % 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.

1---
2name: estimate-analysis
3description: >
4 Deep-dive into analyst estimates and revision trends for any stock using Yahoo Finance data.
5 Use when the user wants to understand analyst estimate direction,
6 how EPS or revenue forecasts changed over time, compare estimate distributions,
7 or analyze growth projections across periods.
8 Triggers: "estimate analysis for AAPL", "analyst estimate trends for NVDA",
9 "EPS revisions for TSLA", "how have estimates changed for MSFT",
10 "estimate revisions", "EPS trend", "revenue estimates",
11 "consensus changes", "analyst estimates", "estimate distribution",
12 "growth estimates for", "estimate momentum", "revision trend",
13 "forward estimates", "next quarter estimates", "annual estimates",
14 "estimate spread", "bull vs bear estimates", "estimate range",
15 or any request about tracking or comparing analyst estimates/revisions.
16 Use this skill when the user asks about estimates beyond a simple lookup —
17 if they want context, trends, or analysis, this is the right skill.
18---
19 
20# Estimate Analysis Skill
21 
22Deep-dives into analyst estimates and revision trends using Yahoo Finance data via [yfinance](https://github.com/ranaroussi/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.
23 
24**Important**: Data is for research and educational purposes only. Not financial advice. yfinance is not affiliated with Yahoo, Inc.
25 
26---
27 
28## Step 1: Ensure yfinance Is Available
29 
30**Current environment status:**
31 
32```
33!`python3 -c "exec('try:\n import yfinance\n print(\'yfinance \' + yfinance.__version__ + \' installed\')\nexcept Exception:\n print(\'YFINANCE_NOT_INSTALLED\')')"`
34```
35 
36If `YFINANCE_NOT_INSTALLED`, install it:
37 
38```python
39import subprocess, sys
40subprocess.check_call([sys.executable, "-m", "pip", "install", "-q", "yfinance"])
41```
42 
43If already installed, skip to the next step.
44 
45---
46 
47## Step 2: Identify the Ticker and Gather Estimate Data
48 
49Extract the ticker from the user's request. Fetch all estimate-related data in one script.
50 
51```python
52import yfinance as yf
53import pandas as pd
54 
55ticker = yf.Ticker("AAPL") # replace with actual ticker
56 
57# --- Estimate data ---
58earnings_est = ticker.earnings_estimate # EPS estimates by period
59revenue_est = ticker.revenue_estimate # Revenue estimates by period
60eps_trend = ticker.eps_trend # EPS estimate changes over time
61eps_revisions = ticker.eps_revisions # Up/down revision counts
62growth_est = ticker.growth_estimates # Growth rate estimates
63 
64# --- Historical context ---
65earnings_hist = ticker.earnings_history # Track record
66info = ticker.info # Company basics
67quarterly_income = ticker.quarterly_income_stmt # Recent actuals
68```
69 
70### What each data source provides
71 
72| Data Source | What It Shows | Why It Matters |
73|---|---|---|
74| `earnings_estimate` | Current EPS consensus by period (0q, +1q, 0y, +1y) | The estimate levels — what analysts expect |
75| `revenue_estimate` | Current revenue consensus by period | Top-line expectations |
76| `eps_trend` | How the EPS estimate has changed (7d, 30d, 60d, 90d ago) | Revision direction — rising or falling expectations |
77| `eps_revisions` | Count of upward vs downward revisions (7d, 30d) | Revision breadth — are most analysts raising or cutting? |
78| `growth_estimates` | Growth rate estimates vs peers and sector | Relative positioning |
79| `earnings_history` | Actual vs estimated for last 4 quarters | Calibration — how good are these estimates historically? |
80 
81---
82 
83## Step 3: Route Based on User Intent
84 
85The user might want different levels of analysis. Route accordingly:
86 
87| User Request | Focus Area | Key Sections |
88|---|---|---|
89| General estimate analysis | Full analysis | All sections |
90| "How have estimates changed" | Revision trends | EPS Trend + Revisions |
91| "What are analysts expecting" | Current consensus | Estimate overview |
92| "Growth estimates" | Growth projections | Growth Estimates |
93| "Bull vs bear case" | Estimate range | High/low spread analysis |
94| Compare estimates across periods | Multi-period | Period comparison table |
95 
96When in doubt, provide the full analysis — more context is better.
97 
98---
99 
100## Step 4: Build the Estimate Analysis
101 
102### Section 1: Estimate Overview
103 
104Present the current consensus for all available periods from `earnings_estimate` and `revenue_estimate`:
105 
106**EPS Estimates:**
107 
108| Period | Consensus | Low | High | Range Width | # Analysts | YoY Growth |
109|---|---|---|---|---|---|---|
110| Current Qtr (0q) | $1.42 | $1.35 | $1.50 | $0.15 (10.6%) | 28 | +12.7% |
111| Next Qtr (+1q) | $1.58 | $1.48 | $1.68 | $0.20 (12.7%) | 25 | +8.3% |
112| Current Year (0y) | $6.70 | $6.50 | $6.95 | $0.45 (6.7%) | 30 | +10.2% |
113| Next Year (+1y) | $7.45 | $7.10 | $7.85 | $0.75 (10.1%) | 28 | +11.2% |
114 
115**Revenue Estimates:**
116 
117| Period | Consensus | Low | High | # Analysts | YoY Growth |
118|---|---|---|---|---|---|
119| Current Qtr | $94.3B | $92.1B | $96.8B | 25 | +5.4% |
120| Next Qtr | $102.1B | $99.5B | $105.0B | 22 | +6.1% |
121 
122Calculate and flag:
123- **Range width** as % of consensus — wide ranges (>15%) signal high uncertainty
124- **Analyst coverage** — fewer than 5 analysts means thin coverage, note this
125- **Growth trajectory** — is growth accelerating or decelerating across periods?
126 
127### Section 2: Revision Trends (EPS Trend)
128 
129This is often the most actionable section. From `eps_trend`, show how estimates have moved:
130 
131| Period | Current | 7 Days Ago | 30 Days Ago | 60 Days Ago | 90 Days Ago |
132|---|---|---|---|---|---|
133| Current Qtr | $1.42 | $1.41 | $1.40 | $1.38 | $1.35 |
134| Next Qtr | $1.58 | $1.57 | $1.56 | $1.55 | $1.54 |
135| Current Year | $6.70 | $6.68 | $6.65 | $6.58 | $6.50 |
136| Next Year | $7.45 | $7.43 | $7.40 | $7.35 | $7.28 |
137 
138Summarize 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."
139 
140**Key interpretation:**
141- Rising estimates ahead of earnings = positive setup (the bar is rising)
142- Falling estimates = analysts cutting numbers, often a negative signal
143- Flat estimates = no new information being priced in
144- Recent acceleration/deceleration matters more than the total move
145 
146### Section 3: Revision Breadth (EPS Revisions)
147 
148From `eps_revisions`, show the up vs. down count:
149 
150| Period | Up (last 7d) | Down (last 7d) | Up (last 30d) | Down (last 30d) |
151|---|---|---|---|---|
152| Current Qtr | 5 | 1 | 12 | 3 |
153| Next Qtr | 3 | 2 | 8 | 5 |
154 
155Calculate a revision ratio: Up / (Up + Down). Ratios above 0.7 are strongly bullish; below 0.3 are bearish.
156 
157### Section 4: Growth Estimates
158 
159From `growth_estimates`, compare the company's expected growth to benchmarks:
160 
161| Entity | Current Qtr | Next Qtr | Current Year | Next Year | Past 5Y Annual |
162|---|---|---|---|---|---|
163| AAPL | +12.7% | +8.3% | +10.2% | +11.2% | +14.5% |
164| Industry | +9.1% | +7.0% | +8.5% | +9.0% | — |
165| Sector | +11.3% | +8.8% | +10.0% | +10.5% | — |
166| S&P 500 | +7.5% | +6.2% | +8.0% | +8.5% | — |
167 
168Highlight whether the company is expected to grow faster or slower than its peers.
169 
170### Section 5: Historical Estimate Accuracy
171 
172From `earnings_history`, assess how reliable estimates have been:
173 
174| Quarter | Estimate | Actual | Surprise % | Direction |
175|---|---|---|---|---|
176| Q3 2024 | $1.35 | $1.40 | +3.7% | Beat |
177| Q2 2024 | $1.30 | $1.33 | +2.3% | Beat |
178| Q1 2024 | $1.52 | $1.53 | +0.7% | Beat |
179| Q4 2023 | $2.10 | $2.18 | +3.8% | Beat |
180 
181Calculate:
182- **Beat rate**: X of 4 quarters
183- **Average surprise**: magnitude and direction
184- **Trend in surprise**: Are beats getting bigger or smaller? A shrinking surprise with rising estimates could mean the bar is catching up to reality.
185 
186---
187 
188## Step 5: Synthesize and Respond
189 
190Present the analysis with clear structure:
191 
1921. **Lead with the key insight**: "AAPL estimates are trending higher across all periods, with positive revision breadth (80% of recent revisions are upward)."
193 
1942. **Show the tables** for each section the user cares about
195 
1963. **Provide interpretive context**:
197 - Is the revision trend confirming or contradicting the stock's recent price action?
198 - How does the growth outlook compare to what's priced into the current P/E?
199 - What's the relationship between estimate accuracy history and current estimate levels?
200 
2014. **Flag risks and nuances**:
202 - Estimates cluster around consensus — the "real" distribution of outcomes is wider than low/high suggests
203 - Revision momentum can reverse quickly on a single data point (guidance change, macro event)
204 - Yahoo Finance estimates may lag behind real-time consensus providers by hours or days
205 - Growth estimates for out-years (+1y) are inherently less reliable
206 
207### Caveats to always include
208- Analyst estimates reflect a consensus view, not certainty
209- Estimate revisions are a signal but not a guarantee of future performance
210- This is not financial advice
211 
212---
213 
214## Reference Files
215 
216- `references/api_reference.md` — Detailed yfinance API reference for all estimate-related methods
217 
218Read the reference file when you need exact return formats or edge case handling.
219 
View all files on GitHub →

Details

Updated
Aug 27, 2026
License
MIT
Stars on the repo
★ 3,347
Checked
Source
himself65/finance-skills ↗
Creator
himself65 · 24 listings
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