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Found 1,954 Skills
Application lifecycle audit worker (L3). Checks bootstrap initialization order, graceful shutdown, resource cleanup, signal handling, liveness/readiness probes. Returns findings with severity, location, effort, recommendations.
Use this skill to analyze an existing PostgreSQL database and identify which tables should be converted to Timescale/TimescaleDB hypertables. **Trigger when user asks to:** - Analyze database tables for hypertable conversion potential - Identify time-series or event tables in an existing schema - Evaluate if a table would benefit from Timescale/TimescaleDB - Audit PostgreSQL tables for migration to Timescale/TimescaleDB/TigerData - Score or rank tables for hypertable candidacy **Keywords:** hypertable candidate, table analysis, migration assessment, Timescale, TimescaleDB, time-series detection, insert-heavy tables, event logs, audit tables Provides SQL queries to analyze table statistics, index patterns, and query patterns. Includes scoring criteria (8+ points = good candidate) and pattern recognition for IoT, events, transactions, and sequential data.
Full-codebase audit using 1M context window. Security, architecture, and dependency analysis in a single pass. Use when you need whole-project analysis.
Analyze messy and unstructured Excel files to identify data quality issues, detect format inconsistencies, find missing values, and generate comprehensive analysis reports. Use when Claude needs to work with Excel files (.xlsx, .xls) for data quality assessment, structure analysis, or when users request data auditing, cleaning recommendations, or statistical summaries of spreadsheet data.
Use this tool to review, audit, or validate the quality and cross-platform/cross-agent compatibility of Claude Code skills. It is triggered by phrases such as "审查 skill", "review skill", "检查 skill 质量", "skill 兼容性检查", "review 兼容性"
Scans source code, configuration files, and git history for hardcoded credentials, API keys, and tokens. Use when auditing repositories for security leaks or ensuring sensitive data is not committed to version control.
Deep Python code review of changed files using git diff analysis. Focuses on production quality, security vulnerabilities, performance bottlenecks, architectural issues, and subtle bugs in code changes. Analyzes correctness, efficiency, scalability, and production readiness of modifications. Use for pull request reviews, commit reviews, security audits of changes, and pre-deployment validation. Supports Django, Flask, FastAPI, pandas, and ML frameworks.
Run /check-posthog, then create GitHub issues for all findings. Each finding becomes a separate, actionable issue with clear acceptance criteria. Invoke for: PostHog audit to issues, analytics backlog creation.
Research-driven code review and validation at multiple levels of abstraction. Two modes: (1) Session review — after making changes, review and verify work using parallel reviewers that research-validate every assumption; (2) Full codebase audit — deep end-to-end evaluation using parallel teams of subagent-spawning reviewers. Use when reviewing changes, verifying work quality, auditing a codebase, validating correctness, checking assumptions, finding defects, reducing complexity. NOT for writing new code, explaining code, or benchmarking.
Comprehensive security and authentication workflow that orchestrates security architecture, identity management, access control, and compliance implementation. Handles everything from authentication system design and authorization frameworks to security auditing and threat protection.
Monitors awesome-copilot releases for drift against the amplihack integration. Checks latest commits on github/awesome-copilot via the GitHub API and reports whether the local integration is current or has drifted behind upstream changes. Use when auditing integration freshness or before updating awesome-copilot features.
Content quality and E-E-A-T analysis with AI citation readiness assessment. Use when user says "content quality", "E-E-A-T", "content analysis", "readability check", "thin content", or "content audit".