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概要と使いどころ
When the user wants to optimize post-signup onboarding, user activation, first-run experience, or time-to-value. Also use when the user mentions "onboarding flow," "activation rate," "user activation," "first-run experience," "empty states," "onboarding checklist," "aha moment," or "new user experience." For signup/registration optimization, see signup-flow-cro. For ongoing email sequences, see email-sequence.
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NanoTube includes functions for the processing, quality control, analysis, and visualization of NanoString nCounter data. Analysis functions include differential analysis and gene set analysis methods, as well as postprocessing steps to help understand the results. Additional functions are included to enable interoperability with other Bioconductor NanoString data analysis packages.
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Precise knowledge on the binding sites of an RNA-binding protein (RBP) is key to understand (post-) transcriptional regulatory processes. Here we present a workflow that describes how exact binding sites can be defined from iCLIP data. The package provides functions for binding site definition and result visualization. For details please see the vignette.
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This package implements a metabolic network analysis pipeline to identify an active metabolic module based on high throughput data. The pipeline takes as input transcriptional and/or metabolic data and finds a metabolic subnetwork (module) most regulated between the two conditions of interest. The package further provides functions for module post-processing, annotation and visualization.
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apeglm provides Bayesian shrinkage estimators for effect sizes for a variety of GLM models, using approximation of the posterior for individual coefficients.
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A post hoc cell type classification tool to fine-tune cell type annotations generated by any cell type classification procedure with semi-supervised learning algorithm AdaSampling technique. The current version of scReClassify supports Support Vector Machine and Random Forest as a base classifier.
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BEER implements a Bayesian model for analyzing phage-immunoprecipitation sequencing (PhIP-seq) data. Given a PhIPData object, BEER returns posterior probabilities of enriched antibody responses, point estimates for the relative fold-change in comparison to negative control samples, and more. Additionally, BEER provides a convenient implementation for using edgeR to identify enriched antibody responses.
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epigraHMM provides a set of tools for the analysis of epigenomic data based on hidden Markov Models. It contains two separate peak callers, one for consensus peaks from biological or technical replicates, and one for differential peaks from multi-replicate multi-condition experiments. In differential peak calling, epigraHMM provides window-specific posterior probabilities associated with every possible combinatorial pattern of read enrichment across conditions.
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。
Expert knowledge for Azure Migrate development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when sizing and migrating VMs/SQL/PostgreSQL, configuring appliances/Arc, using AppCAT/CAST, or Private Link, and other Azure Migrate related development tasks. Not for Azure Database Migration Service (use azure-database-migration), Azure Site Recovery (use azure-site-recovery), Azure Virtual Machines (use azure-virtual-machines), SQL Server on Azure Virtual Machines (use azure-sql-virtual-machines).
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。
Expert knowledge for Azure Osconfig development including troubleshooting, security, configuration, and integrations & coding patterns. Use when running OSConfig via IoT for commands/networking, SSH Posture Control, agent health, or Windows security baselines, and other Azure Osconfig related development tasks. Not for Azure Update Manager (use azure-update-manager), Azure Automation (use azure-automation), Azure Policy (use azure-policy).
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。
Expert knowledge for Azure Database Migration Service development including troubleshooting, decision making, limits & quotas, security, integrations & coding patterns, and deployment. Use when planning Azure DMS migrations for MySQL, PostgreSQL, MongoDB, SSIS-to-Azure SQL, or hybrid workloads, and other Azure Database Migration Service related development tasks. Not for Azure Migrate (use azure-migrate), Azure SQL Database (use azure-sql-database), Azure SQL Managed Instance (use azure-sql-managed-instance), SQL Server on Azure Virtual Machines (use azure-sql-virtual-machines).
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。
Expert knowledge for Azure Cosmos DB development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using NoSQL/Mongo/Cassandra APIs, change feed, multi-region HA, vector search, or Cosmos DB for PostgreSQL, and other Azure Cosmos DB related development tasks. Not for Azure Table Storage (use azure-table-storage), Azure SQL Database (use azure-sql-database), Azure SQL Managed Instance (use azure-sql-managed-instance), Azure Data Explorer (use azure-data-explorer).
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Build crafted, highly detailed, interactive isometric SVG figures that explain a technical idea as a real physical object (a test rig, a dial gauge, a cabinet of drawers, a plotter, a lock), with many small precise solid parts, four-tone shading, correct painter's order, a framed card with title, hint and live readout, and calm eased motion. Use this whenever someone wants an isometric illustration, an explanatory diagram or figure for docs, a blog post, a landing page or an article, a "Linear-style" or "premium" technical drawing, an interactive explainer, or an SVG that shows how a system, algorithm or component works, even if they never say "isometric". Works framework-free (TypeScript/JS that writes SVG and HTML) and in React/Next.js, and can add realistic WebGL shaders (fire, exhaust, water, caustics, lamp light) that live inside the drawing in the same isometric perspective.
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Digest of the most interesting new posts on [REPLACE: TOPIC] from RSS feeds and the open web
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Draft copy-paste-ready X replies - two options per reply-worthy tweet from tracked accounts, topics, or lists (default), or ready-to-post responses to engagement opps in recent logs (from-logs)
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Recap of everything shipped since the last run - cross-repo PRs, security fixes, star deltas, and X traction, synthesized into a digest article and a ready-to-post shiplog in your voice.
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Mention/keyword sweep on social platforms for [REPLACE: KEYWORDS] — trends, sentiment, top posts
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Produces a copy-ready sentence about a company's funding round, and the list-building filters that select companies which have ever raised or raised recently. Triggers on "raised a round", "recently funded", "companies that raised", "Series A list", "VC backed", "post funding outreach", "who just raised". Outputs funding_line, a COMPLETE sentence including the leading "Saw" and the trailing period, so an empty value renders as nothing at all.
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META skill — build the largest possible qualified lead list for any request, end to end. Orchestrates discovery (Prospeo + GetLeads + Blitz + Google Maps + lookalike snowball from /list-expander), AI qualification with live-website verify, uncapped contact pull (GetLeads → Blitz → Prospeo), and provider emails (GetLeads/Blitz/Prospeo) validated with MillionVerifier before sending. Use for any "build me a list", "get me leads for X", "find everyone who...", client list request, or campaign list build. Starts with a 7-line intake brief; outputs to Postgres, Google Sheet, and/or CSV (asks which).
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Run QA validation on a pull request — boots the local environment, tests acceptance criteria, and optionally posts the report as a PR comment. Standalone entry point for the qa-engineer agent.
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Groom a single GitHub issue — produce an implementation spec and optionally post the grooming summary as a GitHub comment. Standalone entry point for the grooming-agent.
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Build a User Generated Content (UGC) strategy — getting customers to create content, review generation, UGC briefs for creators, social campaigns, contest mechanics, legal rights management, and repurposing UGC in paid ads. Use when the user says "UGC", "user generated content", "get customers to create content", "customer content", "UGC ads", "UGC creators", "organic UGC", "UGC strategy", "how do we get customers to post about us", "brand hashtag", "UGC brief", "influencer UGC", "UGC for ads", "customer photos and videos", or wants to use real customer content to build trust and reduce content production costs.
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Designs products around price using the 9 rules from Ramanujam and Tacke - WTP conversations, needs-based segmentation, Good/Better/Best configuration, monetization models, behavioral pricing, and price integrity. Use when designing new products, validating pricing for SaaS/B2B/B2C launches, choosing between subscription/usage/freemium models, fixing post-launch sales below plan, diagnosing failed launches as Feature Shock/Minivation/Hidden Gem/Undead, or when product teams say 'let's price it later'. Not for pure commodities or cost-plus regulated environments.
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Use when the user already has a FINISHED video and wants Higgsfield to repurpose, restyle, clip or analyze it: Shorts Studio (restyle one 4–120s source video into a set of short-form clips from a style preset), Clipify (turn one YouTube video into subtitled clips), Virality Predictor (predict virality, hook strength, attention, retention risk with a dashboard), or Video Analysis (scene-by-scene breakdown of an uploaded video or a YouTube link). Also use when the user asks which of these cost credits and which are free, 'turn my video into shorts', 'will this go viral', 'score my ad', 'break this video down scene by scene'. Names: shorts_studio_create, shorts_studio_create_preset, shorts_studio_list_presets, shorts_studio_status, clipify, virality_predictor, brain_activity, video_analysis_create, video_analysis_status.
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。