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Found 9,298 Skills
Create Post Incident Records (PIRs) by analysing incidents discovered from PagerDuty. Orchestrates pagerduty-oncall, datadog-analyser, and traffic-spikes-investigator skills to enrich each incident with observability and traffic data, auto-determines severity, and outputs completed PIR forms. Use when asked to "create a PIR", "write a post incident record", "fill out PIR form", "incident report", "analyse incidents", or after on-call shifts need documentation.
Review existing Datadog dashboards for operational readiness. Audits alert threshold markers, threshold proximity to normal traffic, customer-facing section completeness, and zero-knowledge readability. Uses pup CLI to fetch dashboard definitions. Use when auditing dashboards before on-call handoff, after dashboard changes, or during operational reviews. Do not use for: (1) designing new dashboards from scratch, (2) monitor/alert rule design, (3) APM instrumentation or tracing setup, (4) log pipeline configuration.
Deep EVM smart contract security audit system. Use when asked to audit a contract, find vulnerabilities, review code for security issues, or file security issues on a GitHub repo. Covers 500+ non-obvious checklist items across 19 domains via parallel sub-agents. Different from the security skill (which teaches defensive coding) — this is for systematically auditing contracts you didn't write.
Retrieve code review results from DeepSource — issues, vulnerabilities, report cards, and analysis runs. Use when asked about code quality, security findings, dependency CVEs, coverage metrics, or analysis status.
Authors and structures professional-grade agent skills following the agentskills.io spec. Use when creating new skill directories, drafting procedural instructions, or optimizing metadata for discoverability. Don't use for general documentation, non-agentic library code, or README files.
Runs real-time safety analysis for instructions involving destructive operations, permission changes, irreversible actions, prompt injection, or compliance-sensitive operations. Evaluates risk level, destructiveness, and reversibility via backend API. Use when asked for safety check, risk assessment, security audit, destructive check, instruction audit, or Modeio safety scan. Also use proactively before executing any instruction that deletes data, modifies permissions, drops or truncates tables, deploys to production, or alters system state irreversibly. Also supports pre-install Skill Safety Assessment for third-party skill repositories via a static prompt contract.
Smoke test for alicloud-ai-content-aicontent. Validate minimal authentication, API reachability, and one read-only query path.
Smoke test for alicloud-ai-cloud-call-center. Validate minimal authentication, API reachability, and one read-only query path.
Vision-driven HarmonyOS NEXT device automation using Midscene. Operates entirely from screenshots — no DOM or accessibility labels required. Can interact with all visible elements on screen regardless of technology stack. Control HarmonyOS devices with natural language commands via HDC. Perform taps, swipes, text input, app launches, screenshots, and more. Trigger keywords: harmony, harmonyos, 鸿蒙, hdc, huawei device, harmony app, harmony automation, harmony phone, harmony tablet Powered by Midscene.js (https://midscenejs.com)
Background context for the Housing Loan Tax Credit in the Japanese tax filing plugin. It includes eligibility requirements, credit limits, calculation rules, and interactions with furusato-nozei (hometown tax donation) for the current tax year. This skill is not user-invocable — Claude loads it automatically when responding to Housing Loan Tax Credit questions or calculations.
Combining IoT sensor data using algorithms like Kalman filters for improved accuracy and reliability
Run a structured, adversarial multi-agent bug review pipeline on a codebase. Use this skill whenever the user wants to find bugs, audit code quality, review a codebase for issues, or run any kind of bug-finding or code analysis workflow. Also trigger when the user asks to 'review my code for bugs', 'find all issues in this repo', 'audit this codebase', or any similar request. The pipeline uses three sequential phases: a Bug Finder that maximizes issue discovery, a Bug Adversary that challenges false positives, and an Arbiter that issues final verdicts — producing a clean, high-confidence bug report.