Download plugins/market-analysis/skills/estimate-analysis/SKILL.md from the publisher, or copy the path and open the repository. PlugMCP lists the skill and does not host the file.
Where does estimate-analysis live?
The publisher keeps it at https://github.com/himself65/finance-skills.
Which agents can use estimate-analysis?
Claude, Cursor, and Codex can read a SKILL.md you add from the publisher's repository.
>
name: estimate-analysis
description: >
Analyze sell-side analyst estimates and how they are changing, using Yahoo Finance
data (yfinance): EPS and revenue consensus by period, estimate ranges and dispersion,
revision trends over 7/30/60/90 days and up/down revision breadth, growth estimates
vs industry, sector, and the S&P 500, and historical estimate accuracy. Use this
skill when the user wants more than a single estimate lookup: estimate revisions or
momentum, EPS trend, consensus changes, forward or next-quarter and annual
estimates, the bull vs bear estimate spread, or growth projections across periods.
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 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
Match the depth of the analysis to the question:
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
A general request gets the full analysis; a narrow question gets the matching sections.
Step 4: Build the Estimate Analysis
Section 1: Estimate Overview
Present the current consensus for every available period (0q, +1q, 0y, +1y) from earnings_estimate and revenue_estimate: consensus, low, high, range width (as a % of consensus), analyst count, and YoY growth. Flag:
Range width — ranges wider than 15% of consensus signal high uncertainty
Analyst coverage — fewer than 5 analysts means thin coverage
Growth trajectory — whether growth accelerates or decelerates across periods
Section 2: Revision Trends (EPS Trend)
Often the most actionable section. From eps_trend, show each period's current estimate against its value 7, 30, 60, and 90 days ago, and summarize the direction and whether the recent moves are accelerating.
How to read it:
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 or deceleration matters more than the total move
Section 3: Revision Breadth (EPS Revisions)
From eps_revisions, show up vs down revision counts over the last 7 and 30 days for each period, and the 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 for each period (and its past 5-year annual growth) with its industry, sector, and the S&P 500, and say whether it is expected to grow faster or slower than its peers.
Section 5: Historical Estimate Accuracy
From earnings_history, show estimate vs actual EPS and the surprise % for the last four quarters, then assess:
Beat rate — how many of the four quarters beat
Average surprise — magnitude and direction
Trend in surprise — are beats getting bigger or smaller? A shrinking surprise with rising estimates can mean the bar is catching up to reality.
Step 5: Synthesize and Respond
Lead with the key insight — the direction and breadth of revisions across periods — then show the tables for the sections the user cares about. Interpret rather than just tabulate: does the revision trend confirm or contradict the stock's recent price action, how does the growth outlook compare with what the current P/E prices in, and what does the estimate-accuracy history say about today's consensus?
Flag the nuances that apply: estimates cluster around consensus, so the real distribution of outcomes is wider than low/high suggests; revision momentum can reverse on a single guidance change or macro event; Yahoo Finance estimates can lag real-time consensus providers by hours or days; out-year (+1y) estimates are inherently less reliable. Close with the standing caveats: analyst estimates reflect a consensus view, not certainty; revisions are a signal, not a guarantee; 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.
How do I install estimate-analysis?
Download plugins/market-analysis/skills/estimate-analysis/SKILL.md from the publisher, or copy the path and open the repository. PlugMCP lists the skill and does not host the file.
Where does estimate-analysis live?
The publisher keeps it at https://github.com/himself65/finance-skills.
Which agents can use estimate-analysis?
Claude, Cursor, and Codex can read a SKILL.md you add from the publisher's repository.