Transform audio recordings into professional Markdown documentation with intelligent summaries using LLM integration
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概要と使いどころ
Transform audio recordings into professional Markdown documentation with intelligent summaries using LLM integration
日本語の概要は準備中です。原文の説明を表示しています。
Notion API + ntn CLI: pages, databases, markdown, Workers.
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Karpathy's LLM Wiki: build/query interlinked markdown KB.
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Plan mode: write markdown plan to .fetih/plans/, no exec.
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Listing factory (上架工作台, Busabase App-in-Skill) for a cross-border e-commerce seller. Use when the user invokes $kelly-listing or /kelly-listing, mentions 上架, listing, Amazon title bullets, A+ content, flat file, TikTok Shop listing, listing compliance, 禁用词, multi-locale listings, wants platform listings drafted from product source material or a kelly-picks brief, deterministic compliance checks against per-platform rule sets, a review queue for approving drafts, or approved listings exported as Markdown/CSV for upload.
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Investigate a question against high-trust primary sources and capture the findings as a Markdown file in the repo. Use when the user wants a topic researched, docs or API facts gathered, or reading legwork delegated to a background agent.
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Captures web sources and compiles deterministic Markdown reference bundles with metadata and checksums. Use when asked to capture URLs, research web links, bundle search results, or archive web pages into an LLM-ready reference document.
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Classify financial PDFs by extracting company identity, document type, and date metadata. Validates ticker via Yahoo Finance with multi-step fallback. Uses a Python orchestration script for validation, file modification, and writing markdown.
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Extract balance sheet, income statement, shares outstanding, organic growth, and GAAP reconciliation data from financial PDFs. Standardize via Tiger-Transformer and append structured output to the document markdown.
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Execute a single deterministic Python script to compute WACC, DCF assumptions, projected free cash flows, enterprise value, and intrinsic value per share. Updates the metadata markdown.
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Extracts the WACC, assumptions, and DCF projections from the markdown metadata file and converts them into a clean, structured JSON file for the frontend interactive viewer.
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Fetch a URL and save the body to a temp file so you can pipe it through rg / jq / awk instead of dumping the whole page into the conversation. Use this whenever a user gives you a URL to read - articles, docs, PRs, gists, README files, blog posts, anything HTTP. Returns a path on disk; the agent then greps the path for the relevant section and quotes only what matters. Strongly preferred over the built-in webfetch tool when (a) the page is more than a few KB, (b) the user wants to find a specific phrase / table / code block on the page, (c) you need to keep the long page out of context, or (d) you want to inspect raw HTML alongside rendered markdown. Triggers: 'fetch', 'fetch this URL', 'fetch and save', 'fetch the page', 'pull this', 'grab this article', 'read this URL', 'read the docs at', 'mirror this page', 'page is too long', 'save the page so I can grep it', 'put it on disk so I can rg', 'cache this URL'.
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Produce a single consolidated hardening report for the DSH node. Runs audit.sh, dome-check.sh, and machine-probe.py, parses their outputs, and writes one markdown file listing every passing check, every failure with a specific remediation command, and a top-line verdict (ALL GREEN / N ISSUES). Non-destructive — reads only, no auto-fix.
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Repository-grounded threat modeling that enumerates trust boundaries, assets, attacker capabilities, abuse paths, and mitigations, and writes a concise Markdown threat model. Trigger only when the user explicitly asks to threat model a codebase or path, enumerate threats/abuse paths, or perform AppSec threat modeling. Do not trigger for general architecture summaries, code review, or non-security design work.
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Take a markdown file of raw material and shape it into an article through a conversational session — drafting candidate openings, growing the piece paragraph by paragraph, arguing about format (lists, tables, callouts, quotes) at each step. Use when the user has a pile of notes, fragments, or a rough draft and wants help turning it into something publishable.
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Create a recurring AI-generated PostHog report — schedule a free-text prompt to run on a cron, with the LLM-synthesized markdown delivered to email or Slack on each tick. Use when the user wants a recurring AI summary of X on any cadence (daily, weekly, monthly, yearly) rather than a one-off report. (To attach an AI summary to an existing insight/dashboard subscription instead of a free-text prompt, see `managing-subscriptions` and its `summary_enabled` option.)
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Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats. This skill should be used when analyzing any scientific data file to understand its structure, content, quality, and characteristics. Automatically detects file type and generates detailed markdown reports with format-specific analysis, quality metrics, and downstream analysis recommendations. Covers chemistry, bioinformatics, microscopy, spectroscopy, proteomics, metabolomics, and general scientific data formats.
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Review and rank an entire SCF round end-to-end from a CSV export. Supports Open Track, Integration Track, and RFP Track with track-specific scoring dimensions. Reads submission data directly from an Airtable CSV, auto-detects columns, categorizes, runs parallel batch reviews, cross-batch calibration, and final ranking. All output is markdown. Use when you have a CSV export of an SCF Build Award round and need to review, score, and rank every submission.
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Convert PDF to Markdown using opendataloader-pdf — extracts text, tables, headings, and images with correct reading order
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Integrate Iteration Layer APIs for document, website, image, and sheet processing. Composable APIs — Document Extraction, Website Extraction, Document to Markdown, Image Transformation, Image Generation, Document Generation, and Sheet Generation — share one credit pool and chain together into workflows.
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Generate CSV, Markdown, and XLSX spreadsheets from structured tabular data.
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Convert a document to markdown and classify it with an LLM in a single pipeline.
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Convert external documents — specs, contracts, reports — to markdown for knowledge base ingestion.
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Convert a document to clean markdown suitable for chunking and embedding in a RAG pipeline.
日本語の概要は準備中です。原文の説明を表示しています。