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Found 1,747 Skills
Run MassGen experiments and analyze logs using automation mode, logfire tracing, and SQL queries. Use this skill for performance analysis, debugging agent behavior, evaluating coordination patterns, and improving the logging structure, or whenever an ANALYSIS_REPORT.md is needed in a log directory.
Build agents specialized in conducting thorough research, gathering information from multiple sources, and synthesizing findings. Covers research planning, source evaluation, and report generation. Use when automating market research, competitive analysis, literature reviews, or intelligence gathering.
Comprehensively reviews Python libraries for quality across project structure, packaging, code quality, testing, security, documentation, API design, and CI/CD. Provides actionable feedback and improvement recommendations. Use when evaluating library health, preparing for major releases, or auditing dependencies.
Score startup idea through S.E.E.D. niche check + STREAM 6-layer analysis + Devil's Advocate inversion, auto-pick stack, and generate PRD with acceptance criteria. Use when user says "validate idea", "score this idea", "should I build this", "go or kill", "generate PRD", or "evaluate opportunity". Do NOT use for deep research (use /research first) or decision-only framework (use /stream).
Retrieve consensus price targets for any stock using Octagon MCP. Use when you need the average, median, high, and low analyst price targets to evaluate upside/downside potential and analyst agreement.
Calculate engagement rates for creator posts and benchmark them against platform and tier averages. This skill should be used when calculating an influencer's engagement rate, benchmarking creator engagement against industry averages, evaluating whether a creator's engagement is above or below average for their tier, comparing engagement rates across platforms, checking if engagement rates suggest fake followers, auditing a creator's engagement quality before a partnership, analyzing engagement by content type (reels, stories, feed posts, TikTok videos), or assessing engagement trends across a creator's recent posts. For estimating fair market rates based on engagement, see creator-rate-estimator. For full creator vetting beyond engagement, see creator-vetting-scorecard. For scoring niche fit, see niche-fit-scorer.
LinkedIn Ads deep analysis for B2B advertising. Evaluates 25 checks across technical setup, audience targeting, creative quality, lead gen forms, and bidding strategy. Includes Thought Leader Ads, ABM, and predictive audiences. Use when user says "LinkedIn Ads", "B2B ads", "sponsored content", "lead gen forms", "InMail", or "LinkedIn campaign".
Microsoft/Bing Ads deep analysis covering search, Performance Max, Audience Network, and Copilot integration. Evaluates 20 checks with focus on Google import validation, unique Microsoft features, and cost advantage assessment. Use when user says "Microsoft Ads", "Bing Ads", "Bing PPC", "Copilot ads", or "Microsoft campaign".
MantaBase T3 Hardware Audit System. Objectively classifies hardware products via Brand Blinding, Triple-Auditor (Tool/Toy/Trash) specialized scoring, and Peer Review based on design theory. Triggers: product links, T3 audit, Tool/Toy/Trash classification, hardware evaluation, VC investment advice
Analyzes Java code against industry best practices and evaluates design principles including SOLID, exception handling, thread safety, and resource management. Reviews naming conventions, Stream API usage, Optional patterns, and general code quality. Use when reviewing Java files, checking code quality, evaluating exception handling, or auditing resource management.
Evaluate, compare, and manage vendor relationships. Trigger with "evaluate this vendor", "compare vendors", "vendor review", "should we renew", "RFP", or when the user is making procurement or vendor decisions.
This skill should be used when auditing a codebase for AI agent readiness, or when guiding improvements to make a codebase work well with agentic coding tools. It applies when users ask to evaluate test coverage, file structure, type system usage, dev environment speed, or automated enforcement -- the five pillars that determine how effectively coding agents can operate in a project. Triggers on "audit my codebase", "make this agent-ready", "improve for AI agents", "agent-friendly", or questions about why agents struggle with a codebase.