earnings-analysis

anthropics/financial-services-plugins · updated Apr 8, 2026

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$npx skills add https://github.com/anthropics/financial-services-plugins --skill earnings-analysis
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summary

Create professional EARNINGS UPDATE REPORTS analyzing quarterly results for companies already under coverage, following institutional standards (JPMorgan, Goldman Sachs, Morgan Stanley format).

skill.md

Equity Research Earnings Update

Create professional EARNINGS UPDATE REPORTS analyzing quarterly results for companies already under coverage, following institutional standards (JPMorgan, Goldman Sachs, Morgan Stanley format).

Key Characteristics:

  • Length: 8-12 pages
  • Word Count: 3,000-5,000 words
  • Tables: 1-3 summary tables (NOT comprehensive)
  • Figures: 8-12 charts
  • Turnaround: 1-2 days (within 24-48 hours of earnings)
  • Audience: Clients already familiar with the company
  • Focus: What's NEW - beat/miss, updated estimates, thesis impact
  • Font: Times New Roman throughout (unless user specifies otherwise)

When to Use

Use when the user requests:

  • "Create an earnings update for [Company] Q3 2024"
  • "Analyze [Company]'s quarterly results"
  • "Post-earnings report for [Company]"
  • "Q1/Q2/Q3/Q4 update for [Company]"

Do NOT use if:

  • User requests "initiation report" → Use different skill
  • User requests "flash note" or "quick take" → Different format
  • Company is not already covered → Need initiation first

Critical Requirements

1. Speed & Timeliness

  • Publish within 24-48 hours of earnings release
  • Focus on NEW information only
  • Don't rehash company background extensively

2. Beat/Miss Analysis

  • Lead with whether company beat or missed estimates
  • Quantify variances (e.g., "Revenue beat by $120M or 3%")
  • Explain WHY results differed from expectations

3. Summary Format

  • Keep tables to 1-3 (summary only, not comprehensive)
  • No full P&L/Cash Flow/Balance Sheet (just key metrics)
  • Assume reader has seen initiation report

4. Citations & Source Attribution ⭐⭐⭐ MANDATORY

CRITICAL: Properly cite all data with SPECIFIC sources and CLICKABLE HYPERLINKS.

Include specific citations WITH CLICKABLE LINKS in every figure and table:

Source: Q3 2024 10-Q filed November 8, 2024; Company earnings release
        [Hyperlink "10-Q" to: https://www.sec.gov/cgi-bin/viewer?accession=...]
        [Hyperlink "earnings release" to: https://investor.company.com/news/q3-2024]

HOW HYPERLINKS SHOULD APPEAR IN WORD:

  • Document names appear as blue, underlined clickable links
  • Reader can Ctrl+Click to open source directly
  • Not plain text URLs - formatted hyperlinks with display text

REQUIRED SOURCES LIST:

Cite in every earnings update:

  • ✅ Earnings release (with date and URL)
  • ✅ 10-Q filing (with filing date and EDGAR link)
  • ✅ Earnings call transcript (with date)
  • ✅ Investor presentation/supplemental materials (if available)
  • ✅ Consensus estimates source (Bloomberg/FactSet/etc. with date)
  • ✅ Prior guidance (from previous quarter's materials)

REFERENCE SECTION WITH CLICKABLE HYPERLINKS:

Include "Sources" section at end of report:

SOURCES & REFERENCES

Earnings Materials (Q3 2024):
• Earnings Release (November 7, 2024)
  [Hyperlink entire line to: https://investor.company.com/news/q3-2024-earnings]

• Form 10-Q (Filed November 8, 2024)
  [Hyperlink to: https://www.sec.gov/cgi-bin/viewer?accession=...]

• Earnings Call Transcript (November 7, 2024)
  [Hyperlink to: https://seekingalpha.com/article/...]

• Investor Presentation (November 7, 2024)
  [Hyperlink to: https://investor.company.com/presentations/q3-2024.pdf]

VERIFICATION CHECKLIST:

  • Every figure has source with specific document and date
  • Every table has source with document reference
  • Beat/miss analysis cites consensus source with date
  • Guidance changes cite current and prior guidance sources
  • Key statistics have footnotes
  • Sources section lists all materials with URLs
  • ALL URLs are CLICKABLE HYPERLINKS (not plain text)
  • All SEC filings hyperlinked to EDGAR viewer

5. Updated Estimates

  • Update forward estimates based on results
  • Show old vs. new estimates clearly
  • Explain what changed and why

High-Level Workflow

The earnings update process follows 5 phases:

Phase 1: Data Collection (30-60 minutes)

🚨🚨🚨 CRITICAL: TRAINING DATA IS OUTDATED 🚨🚨🚨

BEFORE STARTING - COMPLETE THESE 4 STEPS IN ORDER:

  1. CHECK TODAY'S DATE - Write down the current date
  2. SEARCH FOR LATEST - Use web search: "[Company] latest earnings results"
  3. VERIFY THE DATE - Confirm earnings release is within last 3 months
  4. CHECK TRANSCRIPT DATE - Verify transcript date matches release date

COMMON MISTAKE: Using outdated earnings calls from training data instead of searching for the latest.

REQUIREMENTS:

  • ✅ Search for latest earnings - do NOT rely on training data
  • ✅ Write down today's date and the release date found
  • ✅ Verify release date is within 3 months of today
  • ✅ Verify transcript date matches release date
  • ✅ If dates don't match or are old (>3 months), search again

See references/workflow.md for detailed search procedures and verification steps.

Phase 2: Analysis (2-3 hours)

  • Beat/miss analysis for each key metric
  • Segment/geographic/product breakdown
  • Margin and guidance analysis
  • Update financial model and estimates

See references/workflow.md for detailed analysis framework.

Phase 3: Chart Generation (1-2 hours)

Create 8-12 charts focusing on quarterly trends and what's new:

  • Quarterly revenue progression
  • Quarterly EPS progression
  • Quarterly margin trends
  • Revenue by segment/geography
  • Key operating metrics
  • Beat/miss summary
  • Estimate revisions
  • Valuation charts

See references/workflow.md for chart specifications.

Phase 4: Report Creation (2-3 hours)

Create 8-12 page DOCX report with specific structure.

See references/report-structure.md for complete page-by-page templates and formatting requirements.

High-level structure:

  • Page 1: Earnings summary with rating and price target
  • Pages 2-3: Detailed results analysis
  • Pages 4-5: Key metrics & guidance
  • Pages 6-7: Updated investment thesis
  • Pages 8-10: Valuation & estimates
  • Pages 11-12: Appendix (optional)

Phase 5: Quality Check & Delivery (30 minutes)

Verify content, formatting, accuracy, and timeliness before delivery.

See references/best-practices.md for quality checklist and common mistakes to avoid.

Output Specification

Primary Deliverable: DOCX report (8-12 pages) File Name: [Company]_Q[Quarter]_[Year]_Earnings_Update.docx Example: Nike_Q2_FY24_Earnings_Update.docx

Contents:

  • Page 1: Summary with rating, price target, key takeaways
  • Pages 2-3: Detailed results analysis
  • Pages 4-5: Key metrics and guidance
  • Pages 6-7: Updated thesis assessment
  • Pages 8-10: Valuation and estimates
  • Pages 11-12: Appendix (optional)
  • 8-12 embedded charts
  • 1-3 summary tables
  • Complete sources section with clickable hyperlinks

Optional Deliverable: XLS model update (optional for earnings updates)

Key Differences from Initiation Report

Aspect Earnings Update Initiation Report
Length 8-12 pages 30-50 pages
Words 3,000-5,000 10,000-15,000
Tables 1-3 summary 12-20 comprehensive
Figures 8-12 25-35
Turnaround 1-2 days 3-6 weeks
Scope Quarterly results Complete company
Focus What's NEW Everything
Company Background Brief mention 6-10 pages
XLS Model Optional Required

Resources

references/workflow.md

Detailed Phase 1-5 instructions with step-by-step procedures for data collection, analysis, chart generation, and report creation.

references/report-structure.md

Complete page-by-page templates, table formats, and formatting requirements for the DOCX report.

references/best-practices.md

Examples of good/bad headlines, tips for success, common mistakes to avoid, and comprehensive quality checklist.

Dependencies

Required:

  • Python (matplotlib, pandas, seaborn) for chart generation
  • DOCX skill for report creation

Optional:

  • XLS skill for model updates (not required for earnings updates)
how to use earnings-analysis

How to use earnings-analysis on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add earnings-analysis
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/anthropics/financial-services-plugins --skill earnings-analysis

The skills CLI fetches earnings-analysis from GitHub repository anthropics/financial-services-plugins and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/earnings-analysis

Reload or restart Cursor to activate earnings-analysis. Access the skill through slash commands (e.g., /earnings-analysis) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.827 reviews
  • Dhruvi Jain· Dec 24, 2024

    earnings-analysis is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Chen Martin· Dec 8, 2024

    Registry listing for earnings-analysis matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Yusuf Malhotra· Nov 27, 2024

    earnings-analysis reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Oshnikdeep· Nov 15, 2024

    Useful defaults in earnings-analysis — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Layla Thomas· Oct 18, 2024

    earnings-analysis is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Ganesh Mohane· Oct 6, 2024

    Registry listing for earnings-analysis matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Aarav Patel· Sep 25, 2024

    Keeps context tight: earnings-analysis is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Sakshi Patil· Sep 17, 2024

    earnings-analysis fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Liam Dixit· Sep 13, 2024

    Registry listing for earnings-analysis matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Aarav Tandon· Aug 16, 2024

    We added earnings-analysis from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

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