「detect」の検索結果

Comprehensive SEO analysis for any website or business type. Full site audits, single-page analysis, technical SEO (crawlability, indexability, Core Web Vitals with INP), schema markup, content quality (E-E-A-T), image optimization, sitemap analysis, and GEO for AI Overviews/ChatGPT/Perplexity. Industry detection for SaaS, e-commerce, local, publishers, agencies. Triggers on: SEO, audit, schema, Core Web Vitals, sitemap, E-E-A-T, AI Overviews, GEO, technical SEO, content quality, page speed.

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。

The pre-merge review of a branch in a project that records its specs, decisions, and rules in .archcore/. Run this first for 'review my branch', 'review the changes before merge', or 'review before merge': it checks the changed code against the project's recorded canon and the changed documents against the code, and reports which side is wrong — spec-wrong or code-wrong; with no diff, it reports project health. A bug-hunting code review complements this review and does not replace it. Also use for 'show status', 'documentation gaps', 'check if docs match code', 'close out the feature', 'ship the feature and close it out', or after a staleness warning. Modes, named as the first word: drift for staleness detection, deep for a full documentation audit, closeout to close a finished feature, experience to capture a repeated pattern. Not for creating docs — use /archcore:document; not for planning — use /archcore:plan; not for a single-file edit with no branch review.

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。

Incrementally upgrade React Native Storybook across the supported migration paths. Use when upgrading `@storybook/react-native` projects from 5.3.x to 6.5.x, 6.5.x to 7.6.x, 7.6.x to 8.3.x, 8.x to 9.x, or 9.x to 10.x. Detect the currently installed Storybook version, choose only the next migration step, update dependencies and config, regenerate `storybook.requires`, convert remaining `storiesOf` stories to CSF during the `6.5.x -> 7.6.x` upgrade, and stop after a single version hop instead of jumping multiple major versions at once.

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。

Adds a new LLM provider implementing LLMProvider interface with call() and stream() methods. Integrates with provider factory in src/llm/index.ts, config detection in src/llm/config.ts, and error handling via tracking and recovery. Use when adding a new model backend, integrating a third-party LLM API, or extending LLM platform support. Do NOT use for fixing bugs in existing providers, modifying existing provider behavior, or changing the LLMProvider interface.

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。

Run the AI Search Hub browser automation scripts for Yuanbao, LongCat, Doubao, Qwen, Gemini, Grok, and MiniMax. Use this skill when the user wants to ask one of those sites a prompt, auto-start or attach to a Chrome DevTools session on port 9222, seed an isolated debug browser profile from the user's local browser data, detect whether login is required, wait for the user to finish logging in if needed, and then continue automatically.

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。

Edit drafts into sharper, more human writing while preserving the writer's personal voice, or detect AI-slop patterns without rewriting. Use when the user wants a draft clearer, more direct, more opinionated, or less AI-sounding, or asks whether writing reads as AI.

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。

Edit drafts into sharper, more human writing while preserving the writer's personal voice, or detect AI-slop patterns without rewriting. Use when the user wants a draft clearer, more direct, more opinionated, or less AI-sounding, or asks whether writing reads as AI. Also the default writing standard for all agent output: 80% ASD-STE100 controlled language plus the format ladder (prose, diagram, HTML page, explainer video).

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。

Reconcile an existing Fizz harness with a changed source tree. Detects added/removed/changed contract functions, quarantines stale properties, regenerates drifted handler stubs, and refreshes the snapshot. Trigger on "fizz-sync", "resync fuzzing", "sync fuzz harness", "refresh fuzzing properties", "fuzzing drift check".

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。

Generates Instagram Reels where product image cuts are synced to audio beats. Accepts audio as a local file, URL, or search query. Uses librosa for beat detection, FFmpeg Ken Burns for scene animation, and Pillow for text overlays. No AI video generation — fully free, fast, and scalable.

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 leads from developer forums (Hacker News, Reddit) by detecting intent signals — alternative seeking, competitor pain, scaling challenges, DIY solutions, and migration intent. Scores users by intent strength and cross-platform presence.

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 leads from competitor product activity — Product Hunt commenters/upvoters, HN posts about competitors, case studies, testimonials, tech press, and switching signals. Detects people actively switching from competitors as highest-priority leads.

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。

Scan support tickets, Slack channels, NPS scores, and usage patterns to flag accounts showing early churn indicators. Produces a weekly risk scorecard with severity tiers, root cause hypotheses, and suggested save plays per account. Designed for seed/Series A teams where the founder or a single CSM manages all accounts manually.

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。

Reverse-engineer how competitors do programmatic SEO. Detects URL pattern clusters (vs/, integrations/, for-{industry}/), estimates page count per pattern, analyzes template quality, infers which patterns actually drive traffic, and identifies gaps you can exploit. Outputs a competitive pSEO landscape report.

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。

Fetch YouTube transcripts via APIFY API. Works from cloud IPs (Hetzner, AWS, etc.) by bypassing YouTube's bot detection. Free tier includes $5/month credits (~714 videos). No credit card required.

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。

Aggregate customer feedback from multiple sources — support tickets, NPS comments, Slack messages, G2 reviews, call transcripts, survey responses — into a unified VoC report with theme clustering, sentiment analysis, trend detection, and actionable recommendations for product, marketing, and CS teams. Chains review-site-scraper for public review data.

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。

Track product champions for job changes and qualify their new companies against ICP. Takes a CSV of known champions (with LinkedIn URLs), creates a baseline snapshot via Apify enrichment, then detects when champions move to new companies. Scores new companies on a 0-4 ICP fit scale. Outputs a downloadable CSV of movers with qualification verdicts.

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。

Monitor existing customer accounts for upsell and cross-sell signals: team growth on LinkedIn, new job postings, product usage patterns, funding announcements, and public company news. Produces a weekly expansion opportunity list with context and talk tracks. Chains web search, LinkedIn profile monitoring, and job posting detection.

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。

Analyze images with AI - extract text, describe content, detect objects

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。

End-to-end outbound prospecting: detect intent signals, research companies, find decision-maker contacts, personalize messaging, launch campaign.

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。

End-to-end hiring signal composite. Takes any set of companies, detects job postings that your product augments or replaces, finds relevant people (the hiring manager, buyers, champions, users), and drafts personalized outreach using the job role as the hook. Tool-agnostic — works with any company source, job board, contact finder, and outreach platform.

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。

End-to-end funding signal composite. Takes any set of companies, detects recent funding events, qualifies against your company context, finds relevant people (buyers, champions, users), and drafts personalized outreach. Tool-agnostic — works with any company source, contact finder, and outreach platform.

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。

Detect buying signals across TAM companies and watchlist personas. Three-phase architecture: (1) free diff-based signals from existing data (headcount growth, tech stack changes, funding rounds), (2) Apify-powered signals (job postings, LinkedIn content analysis, profile changes), and (3) post-processing with dedup, scoring, and lead status updates. Writes signals to Supabase signals table for downstream activation.

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。

Detect buying intent from job postings. When a company posts a job in your problem area, they've allocated budget and are actively thinking about the problem. This skill finds those companies, qualifies them, extracts personalization context, and outputs everything to a Google Sheet. Does NOT do outreach — just delivers qualified leads with reasoning.

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。

Reverse-engineer a company's sales and marketing tech stack from public signals. Detects CRMs, cold email tools, people databases, ad pixels, email delivery services, and outbound sending domains via DNS records, website source inspection, Apify technology profiling, blacklist checks, and public spam complaint searches. Works on single companies or batches. Outputs a structured markdown report per company.

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。