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All Skills β€” productivity

9 skills in "productivity" matching "review"

πŸ¦€ ClawHub
287 dl
claude-review
Self-review quality gate using Claude CLI. When the user says 'review your work', 'use review-work', or 'check your output', run review-work with the task su...
πŸ¦€ ClawHub
510 dl
Reveal Reviewer skills
Review products on Reveal as an AI agent reviewer. Browse available review tasks, navigate target websites using agent-browser, take screenshots, record obse...
πŸ¦€ ClawHub
107 dl
Amazon Review Management
Amazon review management and response agent. Write professional responses to negative reviews, analyze review patterns to find product improvements, build co...
πŸ¦€ ClawHub
Grab Your Reviews
Grab Your Reviews integration. Manage Reviews, Businesses, Users. Use when the user wants to interact with Grab Your Reviews data.
πŸ¦€ ClawHub
19.3k dl
Agent Team Orchestration
Orchestrate multi-agent teams with defined roles, task lifecycles, handoff protocols, and review workflows. Use when: (1) Setting up a team of 2+ agents with different specializations, (2) Defining task routing and lifecycle (inbox β†’ spec β†’ build β†’ review β†’ done), (3) Creating handoff protocols between agents, (4) Establishing review and quality gates, (5) Managing async communication and artifact sharing between agents.
πŸ¦€ ClawHub
1.7k dl
Readwise & Reader API
Manage Readwise highlights, books, daily review, and Reader documents (save-for-later / read-it-later). Use when the user wants to save articles or URLs to Reader, browse their reading list, search saved documents, review highlights, create or manage highlights and notes, check their daily review, list books/sources, or interact with Readwise/Reader in any way.
πŸ¦€ ClawHub
1.6k dl
Clawver Reviews
Handle Clawver customer reviews. Monitor ratings, craft responses, track sentiment trends. Use when asked about customer feedback, reviews, ratings, or reputation management.
πŸ¦€ ClawHub
868 dl
Peer Review
Multi-model peer review layer using local LLMs via Ollama to catch errors in cloud model output. Fan-out critiques to 2-3 local models, aggregate flags, synthesize consensus. Use when: validating trade analyses, reviewing agent output quality, testing local model accuracy, checking any high-stakes Claude output before publishing or acting on it. Don't use when: simple fact-checking (just search the web), tasks that don't benefit from multi-model consensus, time-critical decisions where 60s lat
πŸ¦€ ClawHub✦ BytesAgain
370 dl
movie review
Write film reviews, get recommendations, and manage watchlists with spoiler control. Use when drafting reviews, getting recs, comparing films side by side.