analyst-estimates

Retrieve analyst financial estimates including Revenue and EPS projections with low/high ranges and analyst coverage. Use when analyzing forward expectations, consensus estimates, valuation inputs, or comparing projections to historical performance.

27 stars

Best use case

analyst-estimates is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Retrieve analyst financial estimates including Revenue and EPS projections with low/high ranges and analyst coverage. Use when analyzing forward expectations, consensus estimates, valuation inputs, or comparing projections to historical performance.

Teams using analyst-estimates should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/analyst-estimates/SKILL.md --create-dirs "https://raw.githubusercontent.com/OctagonAI/skills/main/skills/analyst-estimates/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/analyst-estimates/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How analyst-estimates Compares

Feature / Agentanalyst-estimatesStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Retrieve analyst financial estimates including Revenue and EPS projections with low/high ranges and analyst coverage. Use when analyzing forward expectations, consensus estimates, valuation inputs, or comparing projections to historical performance.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# Analyst Estimates

Retrieve analyst financial estimates for public companies using Octagon MCP.

## Prerequisites

Ensure Octagon MCP is configured in your AI agent (Cursor, Claude Desktop, Windsurf, etc.). See [references/mcp-setup.md](references/mcp-setup.md) for installation instructions.

## Query Format

```
Retrieve analyst financial estimates for <TICKER> for the annual period, limited to <N> records on page 0.
```

**MCP Call:**

```json
{
  "server": "octagon-mcp",
  "toolName": "octagon-agent",
  "arguments": {
    "prompt": "Retrieve analyst financial estimates for AAPL for the annual period, limited to 10 records on page 0"
  }
}
```

## Output Format

The agent returns a table with analyst estimates across future periods:

| Fiscal Year Ending | Revenue Estimate (Low to High) | Revenue Avg | EPS Estimate (Low to High) | EPS Avg | # Revenue Analysts | # EPS Analysts |
|-------------------|-------------------------------|-------------|---------------------------|---------|-------------------|----------------|
| 2030-09-27 | $540.64B - $600.88B | $566.24B | $12.01 - $13.78 | $12.77 | 9 | 6 |
| 2029-09-27 | $520.95B - $578.99B | $545.62B | $10.62 - $12.17 | $11.28 | 13 | 6 |
| 2028-09-27 | $515.19B - $520.48B | $517.84B | $8.96 - $11.18 | $10.20 | 18 | 15 |
| 2027-09-27 | $474.27B - $531.94B | $490.97B | $8.41 - $9.77 | $9.23 | 31 | 30 |
| 2026-09-27 | $445.03B - $483.54B | $460.35B | $7.84 - $8.92 | $8.42 | 24 | 29 |

**Data Source:** octagon-financials-agent

## Key Observations Pattern

After receiving data, generate observations:

1. **Growth trajectory**: Calculate implied revenue and EPS CAGR
2. **Estimate dispersion**: Analyze spread between low and high estimates
3. **Analyst coverage**: Note number of analysts covering each period
4. **Near vs far-term**: Compare confidence in near-term vs long-term estimates
5. **Historical comparison**: Compare estimates to actual historical performance

## Metrics Reference

| Metric | Definition |
|--------|------------|
| Revenue Estimate (Low to High) | Range of analyst revenue projections |
| Revenue Avg | Consensus average revenue estimate |
| EPS Estimate (Low to High) | Range of analyst EPS projections |
| EPS Avg | Consensus average EPS estimate |
| # Revenue Analysts | Number of analysts providing revenue estimates |
| # EPS Analysts | Number of analysts providing EPS estimates |

## Analysis Tips

### Implied Growth Rate
```
Implied CAGR = (Future Estimate / Current)^(1/Years) - 1
```
Example: ($566B / $416B)^(1/5) - 1 = 6.4% revenue CAGR

### Estimate Dispersion
```
Dispersion = (High - Low) / Average × 100
```
- Narrow dispersion (<10%) = High consensus
- Wide dispersion (>20%) = Significant uncertainty

### Analyst Coverage Quality
- More analysts = more reliable consensus
- Declining coverage = less institutional interest
- <5 analysts = thin coverage, use caution

### Forward P/E Calculation
```
Forward P/E = Current Price / EPS Estimate
```
Use for valuation relative to growth expectations.

### Estimate Revisions (with follow-up)
Track changes over time:
- Upward revisions = positive momentum
- Downward revisions = negative momentum
- Frequency of revisions matters

## Valuation Applications

### DCF Inputs
Use estimates for:
- Revenue projections
- Margin assumptions (with historical data)
- Terminal growth rate guidance

### Relative Valuation
Compare:
- Forward P/E to historical average
- Forward P/E to peers
- PEG ratio (P/E / Growth rate)

### Earnings Surprise Potential
Compare estimates to:
- Management guidance
- Historical beat/miss rate
- Recent operating trends

## Confidence Assessment

### High Confidence Estimates
- Near-term (1-2 years out)
- Many analysts covering
- Narrow dispersion
- Stable business model

### Low Confidence Estimates
- Long-term (5+ years out)
- Few analysts covering
- Wide dispersion
- Rapidly changing industry

## Follow-up Queries

Based on results, suggest deeper analysis:

- "What factors are driving the projected revenue growth from [YEAR1] to [YEAR2]?"
- "How do these estimates compare to [COMPANY]'s historical financial performance?"
- "What are the key risks to achieving the upper end of these revenue estimates?"
- "Retrieve analyst price targets and ratings for [TICKER]"

Related Skills

sec-analyst-master

27
from OctagonAI/skills

Comprehensive SEC filing analyst skill that orchestrates all Octagon SEC analysis skills. Use when conducting due diligence, regulatory compliance review, M&A analysis, or creating comprehensive company assessments based on SEC disclosures.

market-analyst-master

27
from OctagonAI/skills

Comprehensive market analyst skill that orchestrates all Octagon stock performance and market data skills. Use when conducting stock analysis, creating market reports, evaluating valuations, comparing sectors, or performing technical and sentiment analysis.

financial-analyst-master

27
from OctagonAI/skills

Comprehensive equity research analyst skill that orchestrates all Octagon financial analysis skills. Use when conducting full company analysis, writing initiation of coverage reports, performing due diligence, or creating investment recommendations with quantitative support.

earnings-analyst-questions

27
from OctagonAI/skills

Identify key themes and concerns raised by analysts during earnings calls, including specific analyst attribution and topic categorization.

earnings-analyst-master

27
from OctagonAI/skills

Comprehensive earnings call analyst skill that orchestrates all Octagon earnings analysis skills. Use when analyzing earnings calls, extracting management insights, tracking guidance, and creating earnings-focused research reports.

stock-quote

27
from OctagonAI/skills

Retrieve real-time stock quotes using Octagon MCP. Use when you need current price, day range, 52-week range, volume, market cap, and moving averages for any publicly traded stock.

stock-price-change

27
from OctagonAI/skills

Retrieve stock price change statistics across multiple time periods using Octagon MCP. Use when analyzing short-term and long-term returns, comparing performance across timeframes, and evaluating momentum and historical growth.

stock-performance

27
from OctagonAI/skills

Retrieve stock price data and performance metrics using Octagon MCP. Use when analyzing daily closing prices, trading volume, price trends, historical performance, and comparing stock movements over specific time periods.

stock-historical-index

27
from OctagonAI/skills

Retrieve full historical end-of-day price data for market indices using Octagon MCP. Use when analyzing index performance over time, tracking market trends, calculating returns, and understanding market context for individual stock analysis.

stock-grades

27
from OctagonAI/skills

Retrieve the latest stock grades and ratings from top analysts and financial institutions using Octagon MCP. Use when tracking analyst upgrades, downgrades, rating changes, and institutional sentiment over time.

sector-performance-snapshot

27
from OctagonAI/skills

Retrieve a snapshot of market sector performance using Octagon MCP. Use when analyzing sector-wide metrics including revenue, EBITDA, net income, market cap, and enterprise value for companies within a specific sector and exchange.

sector-pe-ratios

27
from OctagonAI/skills

Retrieve sector P/E ratios using Octagon MCP. Use when comparing company valuations to sector benchmarks, analyzing sector valuations across exchanges, and understanding market-wide valuation trends.