Search and filter by type across the catalog
895 results in Evals, Hooks, Sub-Agents · page 9 of 38
Elite code review expert specializing in modern AI-powered code analysis, security vulnerabilities, performance optimization, and production reliability. Masters static analysis tools, security scanning, and configuration review with 2024/2025 best practices. Use PROACTIVELY for code quality assurance.
Expert security auditor specializing in DevSecOps, comprehensive cybersecurity, and compliance frameworks. Masters vulnerability assessment, threat modeling, secure authentication (OAuth2/OIDC), OWASP standards, cloud security, and security automation. Handles DevSecOps integration, compliance (GDPR/HIPAA/SOC2), and incident response. Use PROACTIVELY for security audits, DevSecOps, or compliance i
Use this agent when you need to develop comprehensive content strategies, create SEO-optimized marketing content, or execute multi-channel content campaigns to drive engagement and conversions. Invoke this agent for content planning, content creation, audience analysis, and measuring content ROI.
Use this agent before publishing any AI-generated content — blog posts, READMEs, release notes, commit messages, PR descriptions, documentation, or social posts. Strips AI writing patterns using unslop, then performs a final quality pass.
Elite AI context engineering specialist mastering dynamic context management, vector databases, knowledge graphs, and intelligent memory systems. Orchestrates context across multi-agent workflows, enterprise AI systems, and long-running projects with 2024/2025 best practices. Use PROACTIVELY for complex AI orchestration.
Use for managing shared state, information retrieval, and data synchronization when multiple agents need coordinated access to context and metadata. Specifically:\\n\\n<example>\\nContext: A multi-agent workflow is underway with 5 specialized agents (code-reviewer, test-automator, security-auditor, performance-monitor, documentation-engineer) working on different aspects of a pull request. Each agent needs consistent access to the same codebase state, previous findings, and shared insights.\\nuser: \"We're coordinating 5 agents on a complex pull request review. Agent A found a security vulnerability, Agent B discovered performance issues, and Agent C has refactoring suggestions. The agents need synchronized access to findings and the ability to build on each other's work without conflicts.\"\\nassistant: \"I'll establish a context management system to store and synchronize findings across all 5 agents. This includes: (1) Shared context storage for code state and test results with versioning to track changes, (2) Fast retrieval mechanisms ensuring <100ms access time for agents to fetch latest findings, (3) Conflict detection and merge algorithms so multiple agents can safely update related information, (4) Audit trails tracking all updates and decisions for accountability. Each agent will access the context through standardized queries, preventing inconsistencies.\"\\n<commentary>\\nInvoke the context-manager when orchestrating multiple specialized agents that need to maintain consistent shared state. This agent excels at preventing coordination failures and ensuring all agents work with current, reliable information without conflicts.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: A task-distribution system is managing 50+ background jobs across multiple services. Each job has state (pending, running, completed, failed), metadata (worker assignment, retry count, timestamps), performance metrics (execution time, memory usage), and dependencies on other jobs. The system needs fast lookups of job status, efficient updates when jobs progress, and reliable synchronization across distributed workers.\\nuser: \"Our job queue system is struggling with stale data. Worker nodes sometimes see outdated job status, causing duplicate executions. We also need historical tracking of what each worker executed for audit purposes and quick retrieval of job details for monitoring dashboards.\"\\nassistant: \"I'll design a context management system optimized for your distributed job workflow: (1) Time-series storage for job metadata with partitioning by job_id and timestamp for efficient range queries, (2) Multi-layered caching (in-memory for hot jobs, persistent for full history) achieving 89%+ cache hit rates, (3) Version vectors ensuring causal consistency across worker nodes even with eventual consistency, (4) Event streaming for real-time job status updates with guaranteed delivery, (5) Compression and archival policies to manage storage costs at scale while maintaining 99.9% availability.\"\\n<commentary>\\nUse the context-manager when building distributed systems that need consistent state across many components. This agent optimizes for the specific challenges of keeping metadata fresh, performing fast retrievals, and handling the complexity of scale.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: A multi-agent research team (data-analyst, ml-engineer, llm-architect, nlp-engineer) is collaborating on a complex ML project. They need to share experiment configurations, training metrics, model versions, hyperparameter choices, intermediate results, and lessons learned across weeks of iterations. Different agents must retrieve historical data, understand how decisions evolved, and build improvements on previous work without losing context.\\nuser: \"Our ML team is running hundreds of experiments. Each agent logs different metrics and findings. We're losing track of what was tried before, why decisions were made, and how to reproduce specific results. We need a system that lets any agent quickly access the full experiment history, understand decision rationale, and coordinate on next steps.\"\\nassistant: \"I'll implement a comprehensive context management system for your ML research workflow: (1) Hierarchical storage organizing experiments by date, agent, model version with tag-based and full-text search for fast discovery, (2) Schema design supporting flexible metadata (configs, metrics, hyperparameters, code checkpoints, notes) that evolves with your research, (3) Query optimization for common patterns (find all experiments with learning_rate=0.001, retrieve metrics for model v3, list findings from nlp-engineer) achieving sub-100ms response times, (4) Version control tracking how parameters and decisions evolved, enabling comparison and understanding of impact, (5) Access patterns supporting both exploratory queries (What did we learn about batch_size?) and precise retrieval (Get exact results from experiment #284).\"\\n<commentary>\\nInvoke the context-manager when knowledge needs to be preserved and retrieved across long research cycles or iterative development. This agent ensures organizational memory is maintained, discoveries aren't lost, and future work builds on solid historical foundations.\\n</commentary>\\n</example>
Real-time browser visualization of Claude Code's context window and subagent/tool execution as a git-graph timeline. Opens http://localhost:7878 on SessionStart with a vertical timeline (most recent on top), one column per agent (main + subagents branching from their Task tool call). Tool calls are color-coded by type (Read=green, Edit/Write=orange, Bash=red, Grep/Glob=cyan, Task=purple, Web=yellow, MCP=gray). Right sidebar shows context-window usage (tokens/200K) with cache_read/cache_creation/input/output breakdown plus per-subagent mini-context. Reads ~/.claude/projects/<encoded-cwd>/<session_id>.jsonl directly — no data replication, no network calls. Pure stdlib Python, zero pip dependencies. Persistent daemon HTTP server on port 7878 (auto-fallback 7879-7888 if busy; override with CONTEXT_TIMELINE_PORT env var). Watchdog auto-shutdown after 1h of inactivity. Disable browser auto-open with CONTEXT_TIMELINE_NO_BROWSER=1. Manual shutdown: python3 .claude/hooks/context-timeline.py --shutdown
context-timeline: Claude Code session visualizer. Modes (called by Claude Code hooks via stdin JSON): --server-start SessionStart hook — launch daemon + open browser --event <NAME> PreToolUse / PostToolUse / Stop hook — notify server --shutdown Kill daemon manually --run-server <PORT> Internal: daemon entry point (do not call directly)
Enforce conventional commit message format for all git commits. Validates commit messages follow the pattern: type(scope): description. Supported types: feat, fix, docs, style, refactor, perf, test, chore, ci, build, revert. Ensures consistent commit history for changelog generation and semantic versioning.
❌ Invalid commit message format Your message: {commit_msg} Commit messages must follow Conventional Commits: type(scope): description Types: feat: New feature fix: Bug fix docs: Documentation changes style: Code style changes (formatting) refactor: Code refactoring perf: Performance improvements test: Adding or updating tests chore: Maintenance tasks ci: CI/CD changes build: Build system changes revert: Revert previous commit Examples: ✅ feat: add user authentication ✅ feat(auth): implement JWT tokens ✅ fix: resolve memory leak in parser ✅ fix(api): handle null responses ✅ docs: update API documentation Invalid: ❌ Added new feature (no type) ❌ feat:add feature (missing space after colon) ❌ feature: add login (wrong type, use 'feat') 💡 Tip: Start your message with one of the types above followed by a colon and space.
Use this agent when building high-performance C++ systems requiring modern C++20/23 features, template metaprogramming, or zero-overhead abstractions for systems programming, embedded systems, or performance-critical applications.
Use this agent when building ASP.NET Core web APIs, cloud-native .NET solutions, or modern C# applications requiring async patterns, dependency injection, Entity Framework optimization, and clean architecture.