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Found 9,298 Skills
Monitor health and availability of systems, services, APIs, and infrastructure endpoints.
Comprehensive ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) profiling for drug candidates. Integrates ADMET-AI predictions, SwissADME drug-likeness, PubChemTox experimental toxicity, ChEMBL clinical data, Lipinski rule-of-five, and CYP interaction data. Use for drug-likeness assessment, BBB penetration, bioavailability, hepatotoxicity prediction, ADME/PK profiling, or screening compound libraries before lab testing.
Audits AI-implemented work for honest completion. Runs independent-evaluator checks against task artifacts, transcripts, tests, CI evidence, requirement-to-test mapping, status front matter, and quality gates; flags skipped tests, weakened assertions, mock-only confidence, snapshot drift, happy-path-only coverage, flaky retries, and status/evidence mismatches. Use when validating completed Compozy tasks, AI-authored PRs, or codex-loop iterations. Do not use for real-user QA, persona/journey testing, exploratory charters, or product usability sessions; use qa-execution for those.
When the user wants to improve their ability to address prospect concerns, overcome resistance, or respond to pushback without being defensive. Also use when the user mentions "handling objections," "overcoming resistance," "common objections," "prospect pushback," "they said no," or "dealing with concerns."
Framework for demonstrating AI capabilities in legal contexts. Provides detailed personas across tenant law, business contracts, startup disputes, employment claims, and consumer protection with progressive complexity scenarios. Use when: (1) Demonstrating AI-powered legal triage or intake systems, (2) Showcasing responsible AI-assisted client interactions, (3) Training staff on appropriate AI use in legal contexts, (4) Creating realistic scenarios for legal tech presentations, (5) Developing educational materials about AI in legal services, or (6) Testing AI-powered legal information systems in controlled environments.
Use version control as a craft — atomic commits, buildable history, useful PRs, bisect-friendly main, recoverable mistakes. Use this skill whenever the task involves writing commits or PRs, choosing a branching model, deciding rebase vs. merge, recovering from a force-push or accidentally-committed secret, debugging a regression with `git bisect`, structuring a long change as a series of small reviewable steps, or judging whether a repo's history is readable. Use it especially when reviewing commit messages, PR descriptions, branching strategies, or merge policies. Built on Tim Pope and Chris Beams on commit messages, Paul Hammant on trunk-based development, Vincent Driessen on GitFlow (and his 2020 note retiring it for SaaS), Linus Torvalds on never rebasing public commits, and the Google Engineering Practices CL guide.
High-level overview of what Sui is, how it works, and what the Sui Stack provides. Use when explaining Sui to someone new, comparing Sui to other blockchains (Ethereum, Solana, Bitcoin), discussing the object-centric data model at a conceptual level, choosing which Sui Stack primitives to use (randomness, zkLogin, Walrus, Nautilus, DeepBook, Kiosk, Seal), or exploring what use cases Sui enables (DeFi, gaming, NFTs, identity, social, supply chain). Also use when migrating from Ethereum or Solana to Sui.
Use when launching cloud VMs, Kubernetes pods, or Slurm jobs for GPU/TPU/CPU workloads, training or fine-tuning models on cloud GPUs, deploying inference servers (vllm, TGI, etc.) with autoscaling, writing or debugging SkyPilot task YAML files, using spot/preemptible instances for cost savings, comparing GPU prices across clouds, managing compute across 25+ clouds, Kubernetes, Slurm, and on-prem clusters with failover between them, troubleshooting resource availability or SkyPilot errors, or optimizing cost and GPU availability.
Helps users discover and install capabilities from the open agent skills ecosystem. Use when users ask "how do I do X" for specialized tasks, request "find a skill for X", want to extend agent capabilities, or need help with specific domains (testing, design, deployment, etc.).
Use this skill for paid advertising campaign strategy, setup, optimization, and reporting. Trigger phrases: "paid ads," "Google Ads campaign," "Meta ads," "Facebook ads," "LinkedIn campaign," "ad campaign strategy," "ROAS," "CPA optimization," "retargeting," "audience targeting," "bidding strategy," "budget allocation," "campaign optimization," "Twitter ads," "paid media."
Patterns and anti-patterns for using OpenAI Codex Goals — the persistent objectives feature introduced in Codex 0.128.0. Use this skill whenever writing, reviewing, or debugging a `/goal` invocation, deciding whether a task should be a Goal at all, drafting a research Goal that needs an evidence ledger, or diagnosing a Goal that completed against the wrong surface. Triggers on `/goal`, "Codex Goal", "Codex goals", "persistent objective", "evidence-based completion", "iteration policy", "blocked stop condition", or any user message describing a multi-turn Codex task with a defined finish line. Trigger even if the user doesn't explicitly mention Goals — if they're typing "/goal" or asking Codex to "keep going until X", this skill applies.
When the user wants to build or improve a sales bot's ability to automatically test message variations to optimize conversion. Also use when the user mentions "message testing," "A/B testing bots," "optimizing bot messages," "testing variations," or "message optimization."