agent-sh

Use when user asks to "discover tasks", "find next task", "prioritize issues", "what should I work on", or "list open issues". Discovers and ranks tasks from GitHub, GitLab, local files, and custom sources.

Use when preparing releases, validating cross-platform compatibility, or updating installation infrastructure. Meta-skill for maintaining AgentSys's 3-platform architecture.

Use when user asks to "run repo intel", "generate repo map", "analyze repo", "query hotspots", "check ownership", or "bus factor". Unified static analysis - git history, AST symbols, project metadata.

Use when mapping code paths, entrypoints, and likely hot files before profiling.

Use when generating performance hypotheses backed by git history and code evidence.

Cross-tool AI consultation. Use when user asks to 'consult gemini', 'ask codex', 'get second opinion', 'cross-check with claude', 'consult another AI', 'ask opencode', 'copilot opinion', or wants a second opinion from a different AI tool.

Structured AI debate templates and synthesis. Use when orchestrating multi-round debates between AI tools, 'debate topic', 'argue about', 'stress test idea', 'devil advocate'.

Use when user wants to clean AI slop from code. Use for cleanup, remove debug statements, find ghost code, repo hygiene.

Use when user asks to 'lint agent configs', 'validate skills', 'check CLAUDE.md', 'validate hooks', 'lint MCP'. Validates agent configuration files against 437 rules across 10+ AI tools.

Use when user asks to 'lint agent configs', 'validate skills', 'check CLAUDE.md', 'validate hooks', 'lint MCP'. Validates agent configuration files against 437 rules.

Use when reviewing X/Twitter exports as source evidence for social content decisions.

Use when analyzing code quality

Use when testing valid context and agent configuration

Use when testing custom agent validation

Use when testing per-client skill rules

Use when testing per-client skill rules

Use when user asks to "deep review the code", "thorough code review", "multi-pass review", or when orchestrating the review loop in a delivery pipeline. Provides review pass definitions (code quality, security, performance, test coverage), signal detection patterns, and iteration algorithms.

Use when user asks to "prepare delivery", "run quality gates", "review code", "run pre-gates", "deslop and review", "validate before shipping", or has finished implementation and wants quality checks before shipping.

Use when validating test coverage quality for new code. Checks that tests exist, are meaningful, and actually exercise new code - not just path matching.

Use when user asks to "validate delivery", "check readiness", or "verify completion". Runs tests, build, and requirement checks with pass/fail instructions.

Compact conversation transcripts into themed knowledge files.

Analyze accumulated skillers knowledge and suggest skills, hooks, and agents. Use when compacting is done and patterns need classification. Checks existing ecosystem before recommending.

Linux desktop observation and control via the computer-use-linux MCP server: accessibility trees, screenshots, window targeting, and input synthesis (click, type, scroll). Works with any MCP host.

Creates message queues, workers, job workflows, and fan-out broadcasts using glide-mq on Valkey/Redis Streams. Provides API reference, code patterns, and configuration for queues, workers, delayed/priority jobs, schedulers, batch processing, DAG workflows, request-reply, serverless producers, and AI-native primitives (usage tracking, token streaming, suspend/resume, budget caps, fallback chains, dual-axis rate limiting, rolling usage summaries, vector search, HTTP proxy/SSE). Triggers on "glide-mq", "glidemq", "job queue valkey", "background tasks valkey", "message queue redis streams", "glide-mq LLM queue", "glide-mq AI orchestration queue", "glide-mq token rate limiting", "glide-mq model fallback", "glide-mq human-in-the-loop queue", "glide-mq vector search", "glide-mq AI pipeline".

Use when user asks to "contribute", "where can I help", "good first issue", "what needs work", "how to contribute", "where to start contributing", "find tasks", "help with project". Analyzes project needs and matches to developer skills.

Use when user asks to "onboard to project", "what does this project do", "summarize codebase", "get oriented", "new to this repo", "quick overview", "project summary", "codebase tour", "help me understand this code". Collects project data automatically then guides interactively.

Use when the user asks about plan drift, reality check, comparing docs to code, project state analysis, roadmap alignment, implementation gaps, or needs guidance on identifying discrepancies between documented plans and actual implementation state.

Structured AI debate templates and synthesis. Use when orchestrating multi-round debates between AI tools, 'debate topic', 'argue about', 'stress test idea', 'devil advocate'.

Use when coordinating multiple enhancers for /enhance command. Runs analyzers in parallel and produces unified report.

Sync documentation with code. Use when user asks to update docs, check docs, fix stale documentation, update changelog, or after code changes.