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Found 189 Skills
Run configurable BMAD pipeline for story delivery using subagent
Full lifecycle orchestrator - spec/impl/test. Spawn-wait-close pipeline with inline discuss subagent, shared explore cache, fast-advance, and consensus severity routing.
Find dead code using parallel subagent analysis and optional CLI tools, treating code only referenced from tests as dead. Use when the user asks to "find dead code", "find unused code", "find unused exports", "find unreferenced functions", "clean up dead code", or "what code is unused". Analysis-only — does not modify or delete code.
Launch N parallel subagents in isolated git worktrees to compete on the session task.
Comprehensive Test Driven Development guide for engineering subagents with multi-framework support, coverage analysis, and intelligent test generation
Conducts security investigations on SOC Compass. The AI agent reads workspace context, asks the user to run SIEM queries, analyzes results, and writes verdicts. Supports multiple alerts in parallel via subagent dispatch. Use when the user mentions SOC Compass, security investigations, alert triage, SIEM queries, threat analysis, Splunk, Elastic, Sentinel, IOC lookups, investigation workspaces, or multiple alerts. Do not use for general cybersecurity questions not involving the SOC Compass platform.
This skill should be used when users request comprehensive, in-depth research on a topic that requires detailed analysis similar to an academic journal or whitepaper. The skill conducts multi-phase research using web search and content analysis, employing high parallelism with multiple subagents, and produces a detailed markdown report with citations.
Spawn 5 Opus subagents with randomly-generated distinct personas to debate a problem from multiple angles. Use when exploring UX decisions, architecture choices, or any decision that benefits from diverse perspectives arguing creatively.
[Docs] Autonomous subagent variant of documentation. Use when creating or updating technical documentation, API documentation, or inline code documentation.
Uncertainty-aware non-linear reasoning system with recursive subagent orchestration. Triggers for complex reasoning, research, multi-domain synthesis, or when explicit commands `/nlr`, `/reason`, `/think-deep` are used. Integrates think skill (reasoning), agent-core skill (acting), and MCP tools (infranodus, exa, scholar-gateway) in recursive think→act→observe loops. Uses coding sandbox for execution validation and maintains deliberate noisiness via NoisyGraph scaffold. Supports `/compact` mode for abbreviated outputs and `/semantic` mode for rich exploration.
This skill should be used when the user has a completed implementation plan (plan.md) and is ready to execute the tasks defined therein. Actively uses Agent Teams or subagents to execute batches of independent tasks in parallel, following BDD/TDD principles.
Initialize, validate, and troubleshoot Deep Agents projects in Python or JavaScript using the `deepagents` package. Use when users need to create agents with built-in planning/filesystem/subagents, configure middleware/backends/checkpointing/HITL, migrate from `create_react_agent` or `create_agent`, scaffold projects with repo scripts, validate agent config files, and confirm compatibility with current LangChain/LangGraph/LangSmith docs.