earnings-preview-single

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

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

Generate a concise, professional equity research earnings preview for a single company. The output is a self-contained HTML file targeting 4-5 printed pages. The report is dense with figures and data, with tight narrative that gets straight to the point.

skill.md

Single-Company Earnings Preview

Generate a concise, professional equity research earnings preview for a single company. The output is a self-contained HTML file targeting 4-5 printed pages. The report is dense with figures and data, with tight narrative that gets straight to the point.

Data Sources (ZERO EXCEPTIONS): The ONLY permitted data sources are Kensho Grounding MCP (search) and S&P Global MCP (kfinance). Absolutely NO other tools, data sources, or web access of any kind. Specifically:

  • Do NOT use WebSearch, WebFetch, web_search, brave_search, google_search, or ANY generic web/internet search tool — even if Kensho is slow, returns no results, or is temporarily unavailable.
  • Do NOT use any browser, URL fetch, or web scraping tool.
  • If Kensho Grounding returns no results for a query, try rephrasing the query or note "data not available" in the report. NEVER fall back to web search as an alternative.
  • Every piece of information in the report must be traceable to either a kfinance MCP function call or a Kensho search call. If it cannot be sourced to one of these two, it must not appear in the report.

Critical Rule: You MUST complete ALL research and data collection (Phases 1-5) BEFORE writing any part of the report.

Intermediate File Rule: All raw data from MCP tool calls MUST be written to files in /tmp/earnings-preview/ immediately after each tool call returns — before moving to the next call. This protects data from context window compression. Do NOT hold data only in memory. At the start of Phase 1, run mkdir -p /tmp/earnings-preview to create the directory. Before generating the HTML report (Phase 7), you MUST read ALL intermediate files back into context using cat commands. The files — not your memory of earlier conversation — are the single source of truth for every number, quote, and source URL in the report. If you skip reading the files, the report WILL contain errors.

Fiscal Quarter Rule: NEVER infer the fiscal quarter from the calendar report date. Many companies have non-standard fiscal years (e.g., Walmart's FY ends Jan 31, so a Feb 2026 report covers Q4 FY2026, not Q4 2025 or Q1 2026). Always use the fiscal quarter and fiscal year exactly as stated in the earnings call name returned by get_next_earnings_from_identifiers or get_earnings_from_identifiers (e.g., "Walmart Q4 FY2026 Earnings Call" means the quarter is Q4 FY2026). Use that verbatim in the report title, headers, tables, and all references. If the call name is ambiguous, cross-reference with get_financial_line_item_from_identifiers period labels.

Length Rule: The report must be concise. Target 4-5 pages when printed. Do NOT write long multi-paragraph narratives. Use tight, punchy bullet points. Every sentence must earn its place. If you can say it in fewer words, do so.

Verbatim Quote Rule: When quoting management in <blockquote> tags, the text MUST be copied exactly from the transcript — word for word, including filler words and sentence fragments. Do NOT paraphrase, rearrange, combine sentences from different parts of the transcript, or "clean up" quotes. If you cannot find the exact phrase in the transcript, do NOT present it as a direct quote. Instead, paraphrase in your own narrative voice without blockquote formatting (e.g., "Management noted that data center demand remains significant"). Every blockquote must be a verbatim, copy-paste excerpt that can be verified against the transcript.

Calculation Integrity Rule: For any multi-step calculation (implied quarterly figures from annual guidance, LTM P/E, y/y growth rates, segment y/y changes), write out each step explicitly and verify intermediate results before using them in the next step. If you state A + B + C = X, verify X is arithmetically correct before using X in a subsequent formula. If the appendix shows a sum that does not equal its stated components, the report is wrong. When in doubt, recompute from raw data rather than reusing a previously calculated intermediate.

Ratio Nomenclature Rule: All valuation ratios must be explicitly labeled as LTM (Last Twelve Months) or NTM (Next Twelve Months). Never use "trailing" or "forward" — always use LTM or NTM. LTM ratios use the sum of the most recent 4 reported quarters. NTM ratios use the sum of the next 4 quarterly consensus mean EPS estimates from get_consensus_estimates_from_identifiers — NOT a single annual figure. Both LTM and NTM P/E must be computed and displayed in the competitor comparison table.

Hyperlink Rule (STRICTLY ENFORCED): Every claim in the report — numeric AND non-numeric — MUST be wrapped in an <a href="#ref-N" class="data-ref"> hyperlink pointing to the corresponding entry in the Appendix. This is not optional. Every single number in the report must be a clickable link. This includes: revenue figures, EPS, margins, growth rates, market caps, P/E ratios, stock returns, price targets, segment revenue, and any other financial metric. It also includes qualitative claims from transcripts or Kensho searches. If you state it as fact, it must link to a source. Assign each unique claim a sequential reference ID (ref-1, ref-2, etc.). The hyperlink style is subtle — navy color, no underline, dotted underline on hover. Do NOT write any number in the report body without wrapping it in an <a> tag. Example: write <a href="#ref-1" class="data-ref">$152.3B</a>, NEVER write $152.3B as plain text.


Phase 1: Company Profile & Setup

  1. Parse the single company ticker from $ARGUMENTS (strip whitespace).
  2. Run mkdir -p /tmp/earnings-preview to create the working directory.
  3. Call get_latest() to establish current reporting period context.
  4. Call get_info_from_identifiers — record market cap, industry.
  5. Call get_company_summary_from_identifiers — record business description.
  6. Call get_next_earnings_from_identifiers — record upcoming earnings date and fiscal quarter name.

Immediately write /tmp/earnings-preview/company-info.txt:

TICKER: [ticker]
COMPANY: [full name]
INDUSTRY: [industry]
MARKET_CAP: [value] (as of [date])
NEXT_EARNINGS_DATE: [date]
NEXT_EARNINGS_QUARTER: [Q# FY#### exactly as returned by API]
BUSINESS_DESCRIPTION: [2-3 sentence summary]

Phase 2: Earnings Transcript Analysis (MANDATORY — COMPLETE BEFORE WRITING)

  1. Call get_latest_earnings_from_identifiers to get the most recent completed earnings call key_dev_id.
  2. Call get_transcript_from_key_dev_id for that transcript.
  3. Immediately write /tmp/earnings-preview/transcript-extracts.txt with the following sections. Write this file WHILE you still have the transcript in context — do not wait:
TRANSCRIPT_SOURCE: [Call Name, e.g., "Q3 2025 Earnings Call"]
KEY_DEV_ID: [key_dev_id]
CALL_DATE: [date]
FISCAL_QUARTER: [Q# FY####]

=== VERBATIM QUOTES (copy-paste exactly — do NOT paraphrase) ===
QUOTE_1: "[exact text from transcript]"
SPEAKER_1: [Name], [Title]
CONTEXT_1: [1 sentence on where this appeared — prepared remarks or Q&A]

QUOTE_2: "[exact text from transcript]"
SPEAKER_2: [Name], [Title]
CONTEXT_2: [context]

QUOTE_3: "[exact text from transcript]"
SPEAKER_3: [Name], [Title]
CONTEXT_3: [context]

QUOTE_4: "[exact text from transcript]"
SPEAKER_4: [Name], [Title]
CONTEXT_4: [context]

=== GUIDANCE (quantitative only) ===
- [metric]: [range or point estimate as stated by management]
- [metric]: [range or point estimate]

=== KEY DRIVERS ===
- [driver 1 with supporting data point]
- [driver 2 with supporting data point]
- [driver 3 with supporting data point]

=== HEADWINDS & RISKS ===
- [risk 1 with quantification if available]
- [risk 2]

=== ANALYST Q&A THEMES ===
- [theme 1: what analysts pushed on]
- [theme 2]
- [theme 3]

=== SYNTHESIS: THEMES TO WATCH NEXT QUARTER ===
- [theme 1]
- [theme 2]
- [theme 3]

Phase 3: Competitor Analysis

  1. Call get_competitors_from_identifiers with competitor_source="all".
  2. Select top 5-7 most relevant public competitors.
  3. For the company AND all selected competitors, gather:
    • get_prices_from_identifiers with periodicity="day", last 12 months
    • get_financial_line_item_from_identifiers for diluted_eps, period_type="quarterly", num_periods=8
    • get_capitalization_from_identifiers with capitalization="market_cap" (latest)
    • get_consensus_estimates_from_identifiers with period_type="quarterly", num_periods_forward=4 — this returns consensus mean EPS estimates for the next 4 quarters, which are summed to compute NTM EPS

After each tool call returns, immediately append the raw data to the appropriate intermediate file:

Write /tmp/earnings-preview/prices.csv — one row per (ticker, date, close). Include the source column with the exact MCP function call. Write the subject company's prices first, then each competitor's as you fetch them:

ticker,date,close,source
D,2025-02-19,55.67,get_prices_from_identifiers(identifier='D',periodicity='day')
D,2025-02-20,55.82,get_prices_from_identifiers(identifier='D',periodicity='day')
...
DUK,2025-02-19,111.79,get_prices_from_identifiers(identifier='DUK',periodicity='day')
...

Note: the source value is the same for all rows from a single call — write it on every row so it's always available.

Write /tmp/earnings-preview/peer-eps.csv — one row per (ticker, period, eps). Write immediately after each diluted_eps call:

ticker,period,diluted_eps,source
D,Q4 2024,1.09,get_financial_line_item_from_identifiers(identifier='D',line_item='diluted_eps',period_type='quarterly')
D,Q1 2025,-0.11,get_financial_line_item_from_identifiers(identifier='D',line_item='diluted_eps',period_type='quarterly')
...
DUK,Q4 2024,1.52,get_financial_line_item_from_identifiers(identifier='DUK',line_item='diluted_eps',period_type='quarterly')
...

Write /tmp/earnings-preview/peer-market-caps.csv — one row per ticker. Write immediately after each market_cap call:

ticker,market_cap,retrieval_date,source
D,55900000000,2026-02-19,get_capitalization_from_identifiers(identifier='D',capitalization='market_cap')
DUK,98300000000,2026-02-19,get_capitalization_from_identifiers(identifier='DUK',capitalization='market_cap')
...

Write /tmp/earnings-preview/consensus-eps.csv — one row per (ticker, period, consensus mean EPS). Write immediately after each get_consensus_estimates_from_identifiers call:

ticker,period,consensus_mean_eps,num_estimates,source
D,Q4 2025,0.88,12,get_consensus_estimates_from_identifiers(identifier='D',period_type='quarterly',num_periods_forward=4)
D,Q1 2026,0.72,10,get_consensus_estimates_from_identifiers(identifier='D',period_type='quarterly',num_periods_forward=4)
D,Q2 2026,0.91,9,get_consensus_estimates_from_identifiers(identifier='D',period_type='quarterly',num_periods_forward=4)
D,Q3 2026,1.05,8,get_consensus_estimates_from_identifiers(identifier='D',period_type='quarterly',num_periods_forward=4)
DUK,Q4 2025,1.48,14,get_consensus_estimates_from_identifiers(identifier='DUK',period_type='quarterly',num_periods_forward=4)
...
  1. Do NOT calculate P/E or returns yet. The raw data is now on disk. Calculations happen in Phase 6 (Verification), reading from these files.

Date Consistency Rule (stock returns): When computing comparative stock returns (YTD %, 1-yr %, 30d %, 90d %), ALL tickers MUST use the exact same start and end dates. After writing all price data to prices.csv, identify the first trading date that appears in ALL tickers' data and use that as the common base date. Do NOT use different base dates for different tickers (e.g., the subject from Feb 19 and peers from Feb 28). If a ticker's data starts later than others, use the first overlapping date for ALL calculations. State the common base date in the appendix for every return calculation.

P/E Currency Rule (LTM P/E): When computing LTM P/E for each company, use that company's most recent 4 reported quarters from peer-eps.csv — not a fixed calendar window applied to all. If a peer has already reported Q4 2025 while the subject company has only reported through Q3 2025, the peer's LTM EPS should include Q4 2025. Check the latest reported period for each company and use the 4 most recent periods per company. Note in the appendix which 4 quarters were used for each P/E calculation.

Market Cap Date-Stamp: When reporting market cap, use the retrieval_date from peer-market-caps.csv. If it differs from the report date, note this in the appendix.


Phase 4: News, Estimates & Sector Intelligence (via Kensho Grounding)

Run these search queries for each category below. Do NOT skip any.

CRITICAL — Capture Source URLs: Every Kensho search result includes a source URL for the underlying article, report, or data page. You MUST record the URL alongside each finding.

After EACH search call, immediately append the results to /tmp/earnings-preview/kensho-findings.txt using the format below. Do NOT wait until all searches are done — write after each one:

=== SEARCH: "[query used]" ===
DATE_RUN: [today's date]
CATEGORY: [estimates|analyst_ratings|risks|news|sector]

FINDING_1: [key finding or excerpt]
URL_1: [source URL from search result]
SOURCE_1: [publication name, date if available]

FINDING_2: [key finding or excerpt]
URL_2: [source URL]
SOURCE_2: [publication name, date]

[...continue for all relevant results from this search...]

Earnings estimates & analyst sentiment:

  1. search for "[TICKER] earnings estimates consensus EPS revenue upcoming quarter"
    • Record: consensus EPS, consensus revenue, estimate revision direction over last 90 days.
    • Append to kensho-findings.txt immediately.
  2. search for "[TICKER] analyst ratings price target upgrades downgrades"
    • Record: recent upgrades/downgrades, price target range, bull/bear thesis summaries.
    • Append to kensho-findings.txt immediately.
  3. search for "[TICKER] risks bear case concerns investors"
    • Record: key debates, bear arguments, swing factors for the upcoming print.
    • Append to kensho-findings.txt immediately.

Recent news (MANDATORY — do not skip): 4. search for "[TICKER] [company name] recent news developments"

  • Record: material news from the last 60 days — M&A, product launches, executive changes, regulatory actions, partnerships, legal developments, tariffs, or any event that could affect the upcoming earnings print or forward guidance.
  • For each item, note the date, headline, potential earnings impact.
  • Append to kensho-findings.txt immediately.

Sector context: 5. search for "[company industry/sector] sector outlook trends"

  • Record: sector-level tailwinds/headwinds, macro data, competitive dynamics.
  • Append to kensho-findings.txt immediately.

Phase 5: Financial Data Collection

Quarterly financials (last 8 quarters): get_financial_line_item_from_identifiers with period_type="quarterly", num_periods=8 for: revenue, gross_profit, operating_income, ebitda, net_income, diluted_eps

After each line item call returns, immediately append to /tmp/earnings-preview/financials.csv. Write the raw values exactly as returned — do NOT round or convert yet. Include the source column with the exact MCP function call and parameters:

ticker,period,line_item,value,source
D,Q4 2024,revenue,3941000000,get_financial_line_item_from_identifiers(identifier='D',line_item='revenue',period_type='quarterly')
D,Q1 2025,revenue,3400000000,get_financial_line_item_from_identifiers(identifier='D',line_item='revenue',period_type='quarterly')
D,Q2 2025,revenue,4076000000,get_financial_line_item_from_identifiers(identifier='D',line_item='revenue',period_type='quarterly')
D,Q3 2025,revenue,3810000000,get_financial_line_item_from_identifiers(identifier='D',line_item='revenue',period_type='quarterly')
D,Q4 2024,diluted_eps,1.09,get_financial_line_item_from_identifiers(identifier='D',line_item='diluted_eps',period_type='quarterly')
D,Q1 2025,diluted_eps,-0.11,get_financial_line_item_from_identifiers(identifier='D',line_item='diluted_eps',period_type='quarterly')
...

Do NOT calculate margins or growth rates yet. Write raw data only. Calculations happen in Phase 6.

Segment data:

  • get_segments_from_identifiers with segment_type="business", period_type="quarterly", num_periods=8
  • You need 8 quarters (not 4) so you have the year-ago quarter for y/y comparisons. To calculate y/y for Q3 2025, you need Q3 2024 — which is the 5th quarter back. If the prior-year quarter's segment data is not available in the API response, do NOT estimate or fabricate it. State "y/y not available" in the report.

Immediately write /tmp/earnings-preview/segments.csv:

ticker,period,segment_name,revenue,source
D,Q3 2024,Dominion Energy Virginia,2762000000,get_segments_from_identifiers(identifier='D',segment_type='business',period_type='quarterly')
D,Q3 2024,Dominion Energy South Carolina,848000000,get_segments_from_identifiers(identifier='D',segment_type='business',period_type='quarterly')
D,Q3 2024,Contracted Energy,260000000,get_segments_from_identifiers(identifier='D',segment_type='business',period_type='quarterly')
D,Q3 2025,Dominion Energy Virginia,3311000000,get_segments_from_identifiers(identifier='D',segment_type='business',period_type='quarterly')
D,Q3 2025,Dominion Energy South Carolina,945000000,get_segments_from_identifiers(identifier='D',segment_type='business',period_type='quarterly')
D,Q3 2025,Contracted Energy,297000000,get_segments_from_identifiers(identifier='D',segment_type='business',period_type='quarterly')
...

Earnings history (for stock chart annotations):

  • get_earnings_from_identifiers
how to use earnings-preview-single

How to use earnings-preview-single 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-preview-single
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-preview-single

The skills CLI fetches earnings-preview-single 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-preview-single

Reload or restart Cursor to activate earnings-preview-single. Access the skill through slash commands (e.g., /earnings-preview-single) 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.

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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)
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general reviews

Ratings

4.836 reviews
  • Pratham Ware· Dec 28, 2024

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

  • Chinedu Garcia· Dec 28, 2024

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

  • Neel Tandon· Dec 24, 2024

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

  • Sakshi Patil· Nov 19, 2024

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

  • Amelia Kapoor· Nov 19, 2024

    Solid pick for teams standardizing on skills: earnings-preview-single is focused, and the summary matches what you get after install.

  • Neel Jackson· Nov 15, 2024

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

  • Xiao Verma· Nov 7, 2024

    earnings-preview-single has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Neel Okafor· Oct 26, 2024

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

  • Chaitanya Patil· Oct 10, 2024

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

  • Xiao Choi· Oct 10, 2024

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

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