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Extract clean markdown or text content from specific URLs via the Tavily CLI. Use this skill when the user has one or more URLs and wants their content, says "extract", "grab the content from", "pull the text from", "get the page at", "read this webpage", or needs clean text from web pages. Handles JavaScript-rendered pages, returns LLM-optimized markdown, and supports query-focused chunking for targeted extraction. Can process up to 20 URLs in a single call.

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Guides collaborative requirements discovery before implementation. Creates task directory, seeds PRD, asks high-value questions one at a time, researches technical choices, and converges on MVP scope. Use when requirements are unclear, there are multiple valid approaches, or the user describes a new feature or complex task.

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。

Wrap up the current session: verify quality gate passed, remind user to commit, archive completed tasks, and record session progress to the developer journal. Use when done coding and ready to end the session.

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。

Requires OFOX_API_KEY — create one at https://app.ofox.ai. Generate a clean, catalog-style e-commerce product video from a real product photo (or, for a generic or fictional product, a text description) using the Ofox video API (Seedance 2.5) — runs a short creative brief (product photo, target platform and aspect ratio, background, camera orbit or turntable) when the request leaves them open, writes a plain-background, literal-accuracy prompt (precise product description, a simple camera orbit or turntable motion, no dramatic cinematography), shows a cost estimate, then calls ofox-video-core to submit, poll, download, and report the real cost. Use when a user asks to turn a product photo into catalog/listing footage, e.g. "make this product photo a 360-degree white-background showcase", "turn this photo into a white-background product video", "make a clean turntable video of this item", or "give me a 5-second white-background rotation video of this product for my listing". Do not use for cinematic brand/mood advertising (see seedance-ad-creative) or for anything involving people/dialogue (see seedance-short-drama).

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。

Orchestrates the full multi-channel outreach cadence for all active leads. Tracks where each lead is, generates appropriate content, manages daily caps, handles weekends, and coordinates across email, LinkedIn, Instagram, and phone.

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。

Turns client wins into structured case studies in multiple formats. Gathers before/after metrics, structures as Problem-Solution-Results, and outputs landing page copy, one-pager, social snippets, and email insert.

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。

Repurpose one piece of content across six formats while maintaining brand voice

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。

Full pipeline from signal detection to scored lead in CRM. Chains signal-scanner, lead-scorer, and crm-writer into one flow.

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。

Generate structured call scripts by persona and lead context

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。

Extract and codify brand voice from existing content for consistent messaging

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。

Two-phase Apollo.io prospecting: free People Search to discover ICP-matching leads, then selective enrichment to reveal emails/phones (credits per contact). Deduplicates against CRM (Google Sheets). Supports Apollo API and MCP tools.

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。

One-time CRM initialization. Reads agency.config.json, creates Google Sheet tabs with correct headers, and deploys the webhook Apps Script. Run this once after agency-setup to bootstrap your CRM.

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。

ScepticAgent internal architecture reference. Use whenever the user asks how the extension works, wants to add a new AI provider, add a new highlight category, debug communication between components, understand the agent loop or streaming, or work with provider routing and the Gemini CORS proxy.

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。

Undo a haircut trim. Puts back the skills, commands, subagents, MCP servers, hooks, plugins and files that haircut parked into a dated bag, using the receipt it wrote at the time. Use this skill whenever the user says undo the cleanup, undo the haircut, put my config back, restore what was trimmed, I need that MCP server again, that skill is gone and I want it back, roll the trim back, what is in the bag, or which files were moved. Use it also when something stopped working right after a config cleanup and they suspect the cleanup caused it, and whenever they ask how to reverse a config cleanup they regret.

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。

Create polished, validated architecture, workflow, sequence, data-flow, and lifecycle/state diagrams as explorable standalone HTML with inline SVG, dark/light themes, optional trace motion, and PNG/JPEG/WebP/SVG/WebM export. Accept plain-language requirements or pasted Mermaid flowchart, sequenceDiagram, and stateDiagram input; inspect repository evidence when the diagram must reflect real code. Use when the user asks to visualize system architecture, infrastructure, cloud/security/network topology, technical workflows, API call sequences, request lifecycles, data pipelines, ETL/ELT, data lineage, state machines, or to convert/beautify Mermaid.

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。

Turn a Git repository URL or local path into one elegant, self-contained HTML learning page that teaches the project's value, architecture, and operating principles top-down with diagrams. Use when the user wants to understand, learn, onboard, explain, map, or study a repository.

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。

Use before declaring any coding task complete, done, or finished.

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。

Guides an agent through reproducing, root-causing, fixing, and validating flaky tests in the PostHog monorepo. Use when a test fails intermittently in CI but passes on rerun or locally, when `hogli ci:insights` or the debugging-ci-failures skill classifies a failure as a flaky test, when given a GitHub Actions URL for a flaky job, when asked to check Trunk Flaky Tests for a test, PR, or master, or when asked to deflake, stabilize, or fix a flaky Jest, pytest, or Playwright test. Core discipline: reproduce locally before changing anything, fix the root cause (never mask it with sleeps, retries, or bigger timeouts), and prove the fix with an N-run validation loop sized to the observed failure rate. Stabilizing is not the only valid outcome — the skill also gates whether the test should exist, so deleting a test that catches nothing real, or re-leveling one that flakes because of the level it runs at, are first-class endings.

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。

Use when asked to deploy, release, publish, or ship OpenMeld to production — workers, Web, CLI, or local components. Routes to the guarded release lanes; never deploy raw.

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。