Results for “markdown”

Transform audio recordings into professional Markdown documentation with intelligent summaries using LLM integration

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Plan mode: write markdown plan to .fetih/plans/, no exec.

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Karpathy's LLM Wiki: build/query interlinked markdown KB.

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Notion API + ntn CLI: pages, databases, markdown, Workers.

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Convert the canonical markdown+JSON deliverable of any SfSkills runtime agent into Excel, PDF, CSV, Notion card, ServiceNow ticket, or similar downstream format WITHOUT polluting the consumer's project with new dependencies or regenerating from the agent's source logic. NOT for authoring new agent output formats (use DELIVERABLE_CONTRACT.md). NOT for data-export SOQL (use bulk-api-2-patterns).

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Audit and improve website readiness for AI agents using the Cloudflare "Is It Agent Ready?" scanner (isitagentready.com). Covers scanning via API, interpreting results, generating implementation prompts, and fixing every check. Use when the user mentions "agent ready", "isitagentready", "AI agent scan", "agent readiness", "agent-ready score", "MCP server card", "agent skills index", "markdown for agents", "content signals", "web bot auth", "agent discovery", "RFC 9727", "RFC 8288", "RFC 9728", "SEP-1649", "WebMCP", "x402", "UCP", "ACP", or wants to make a website discoverable and usable by AI agents.

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Answer Engine Optimization (AEO) skill — optimize content to be cited by AI language models (ChatGPT, Perplexity, Claude, Gemini, Mistral) as authoritative sources. Distinct from SEO — AEO optimizes for citation in LLM-generated responses, not search rankings. Use when planning content for AI-first search audiences, auditing existing content for E-E-A-T signals, tracking which pages get cited by which LLMs, or building a citation-friendly content strategy. Triggers — 'AEO audit', 'optimize for ChatGPT', 'get cited by Perplexity', 'LLM citation strategy', 'answer engine optimization', 'content for AI search', 'E-E-A-T audit'. Output is a markdown audit report (default) or JSON for pipeline integration. Stdlib-only Python tools.

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Download and transcribe videos from YouTube, Bilibili, TikTok and 1000+ platforms. Use when user requests video download, transcription (转录/字幕提取), or converting video to text/markdown. Supports quality selection, audio extraction, playlist downloads, cookie-based authentication, and AI-powered transcription via SiliconFlow API (免费转录).

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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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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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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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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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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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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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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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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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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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