fund-summarizer
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npx mdskill add openai/plugins/fund-summarizerCreate a concise fund summary or report using the connected Morningstar app as the data source.
SKILL.md
.github/skills/fund-summarizerView on GitHub ↗
--- name: fund-summarizer description: Use when summarizing a fund or ETF with Morningstar ratings, returns, risk, holdings, fees, and caveats. --- # 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.