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Summarize a long document into an executive summary, key points, and role-specific implications. Use when the user says "summarize this document", "give me the key points", "do I need to read this?", or drops a long PDF, report, or contract and asks what matters in it.
Condense long articles, reports, and documents into clear, concise summaries.
Distills longer documents into one-page executive summaries with key findings, recommendations, and decision-ready formatting for senior leadership. Use when the user needs to summarize a report, proposal, or analysis for executive review. Do NOT use for full business reports (use `business-report`), stakeholder updates (use `stakeholder-update`), or meeting notes (use `meeting-notes`).
Use this skill when the task involves analyzing data to compute statistics, aggregations, or summaries, and presenting the results as a report. Suitable for tasks like "summarize this dataset", "calculate averages and totals", "generate a weekly sales report", or "show me trends in this data". Do NOT use for simple format conversion between file types.
Turn a long article, email thread, document, or transcript into a tight summary you can act on — the gist, the key points, and what it means for you. Use when asked for a TL;DR, to summarize this, give me the gist, or the key takeaways. Produces a one-line TL;DR, the key points as scannable bullets, any decisions/action items with owners, and open questions — faithful to the source, with nothing invented and important caveats kept.
Summarize or extract text/transcripts from URLs, podcasts, and local files (great fallback for “transcribe this YouTube/video”).
Write a business report in Word with a fixed structure (executive summary, background, analysis, recommendations, appendix), sourced numbers and a consistent voice. Use when the user asks for a report, memo, review, assessment or analysis document, or to turn notes or data into one.
Use when the user asks to describe, summarize, analyze, compare, explain, or report on something (text, data, events, systems) without asking for recommendations or next steps.
Implement text summarization using extractive and abstractive approaches. Use this skill when the user needs to condense long documents, build an automatic summarization pipeline, or compare summarization strategies — even if they say 'summarize this document', 'TLDR', or 'key points extraction'.
Turn raw research, notes, links, or a conversation into a polished, shareable Sparkpage-style report page with an executive summary, structured sections, tables, and citations.
Turns findings you already have into a report a marketer can read in 30 seconds. It has a short summary with key findings and how sure each one is, the first move, decisions needed, at most six next moves, what was not checked, and dated sources. It checks length and wording before handing over, then saves the report to the brand. Use alone when you already have the findings and their sources; otherwise run the playbook for the job (e.g. research-market, research-competitors, plan-campaign).
Use when the user asks for a summary, TL;DR, or condensed version of any content.
When user asks to summarize text, articles, documents, meetings, emails, YouTube transcripts, books, PDFs, reports, conversations, or any long content. Also handles bullet points, key takeaways, action items, TL;DR, ELI5, executive summaries, chapter summaries, comparison summaries, translation summaries, thread summaries, and custom-length summaries. 20-feature AI summarizer with multiple formats, languages, and export options. All processing happens locally — NO external API calls, NO network
Summarize a large Chrome DevTools performance trace into a compact markdown report so it can be reasoned about without loading the whole file. Use when given a Chrome/DevTools/Performance-panel trace (a multi-MB `Trace-*.json` or `*.json` with `traceEvents`) and asked to find what is slow, what runs too often, long tasks, jank, or hot JS functions.
Compress a document, thread, transcript, or pasted text into a scannable TL;DR.
Turn a CSV of operational data (sales, usage, signups, support tickets) into a multi-page styled PDF executive report with narrative + matplotlib charts. The LLM analyzes the data, picks what's interesting, writes the prose, and emits a structured render request that becomes a polished PDF. Use when given a CSV and asked for a report, summary, or analysis.
Turn scattered work progress into a structured weekly or monthly report with results, data, risks and next week's plan, delivered as a Word document
Render agent findings as a styled HTML report in the browser. Use when asked for "full report", "detailed report", "show in browser", or when CLI output exceeds the 40-line budget.
Read findings JSONL files from cluster 1-4 skills, deduplicate by fingerprint, group by severity, and compose a deliverable- grade markdown vulnerability report with per-finding sections (title, severity, target, detail, remediation, evidence) and a top-level summary table. The canonical written artifact a customer receives at engagement close; precise, reproducible, machine- checkable against source findings. Use when: closing an engagement, generating an interim report, regenerating after CVE
Reads the input content, identifies the key signals (decisions, action items, facts, commitments), and produces a compressed summary at the target length. Preserves entropy -- maximum meaning per unit of output.