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fund-summarizer

✓ Official★ 4,081

by openai · part of openai/plugins

Use when summarizing a fund or ETF with Morningstar ratings, returns, risk, holdings, fees, and caveats.

🧩 One of 7 skills in the openai/plugins package — works on its own, and pairs well with its siblings.

This is the playbook your agent receives when the skill activates — you don't need to read it to use the skill, but it's here to audit before installing.

Fund Summarizer

Create a concise fund summary or report using the connected Morningstar app as the data source.

Guardrails

  • Use only data returned by the Morningstar app in the current session.
  • Do not infer missing values, add outside research, predict performance, or give investment advice.
  • Show unavailable values as N/A and distinguish missing data from tool failure.
  • Supported investment types are ETFs, open-end funds, and closed-end funds. If the user asks for an equity or unsupported security, explain that this skill is fund-focused and ask for a supported fund.
  • Preserve Morningstar terminology for ratings, categories, benchmarks, and analyst research.

Workflow

For broad summaries, detailed reports, or any HTML report, read references/full-workflow.md before retrieving data. It preserves Morningstar's partner-authored datapoint map, missing-data rules, structured report inputs, and renderer contract.

  1. Resolve the fund from ticker, name, or Morningstar identifier. Ask only if the match is ambiguous.
  2. Retrieve core profile data: name, ticker, category, investment type, inception date, benchmark, active/passive status, assets, fees, yield, manager tenure, and fund status.
  3. Retrieve ratings and research context: medalist rating, star rating, pillar ratings when available, portfolio risk score, analyst summary, and relevant disclosures.
  4. Retrieve performance and risk context: trailing returns, calendar-year returns, category ranks, standard deviation, Sharpe ratio, upside/downside capture, and flows when available.
  5. Retrieve portfolio context: asset allocation, sector/geography exposure, market-cap style, top holdings, turnover, and sustainability data when available.
  6. Build the smallest useful deliverable for the user request. Use Markdown by default; create self-contained HTML only if the user explicitly asks for an HTML report.

HTML Report Support

When creating an HTML report, use scripts/render.py. It reads assets/template.html, assets/icons/, and the Morningstar logo asset, with visual guidance in references/design_guide.md.

Report rendering always creates the HTML report and attempts a sibling PDF copy when the local environment supports it. If PDF export is unavailable, deliver the HTML report. For command-line PDF export from an existing HTML report, run scripts/export_report.py against the rendered report HTML.

Output

Use this order:

  1. Morningstar disclosure: AI-generated analysis using Morningstar data; informational only, not investment advice.
  2. Fund snapshot.
  3. Ratings and analyst context.
  4. Performance and category-rank context.
  5. Risk and portfolio context.
  6. Fees, flows, and operational details.
  7. Data-availability notes and caveats.

Keep the summary factual and skimmable. For broad requests, include the main tables and a short neutral narrative. For narrow questions, answer only the requested metric or section.