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Found 19 Skills
RLM-style large-codebase comprehension — build a mental map of any codebase by dispatching sub-agents to explore regions without bloating main context
Use when the user asks to "find X in codebase", "where is Y defined?", "explore this dir", "list files in src/", "trace definitions", "find usages" — local-only. Local codebase exploration via Octocode Local + LSP. No GitHub; for external repos use octocode-research.
Create a safe implementation plan as both markdown and JSON DAG artifacts. Challenge scope with the user first, explore real code before decomposing, then emit atomic TASK-NNN entries with explicit dependencies, write scope, validation, and assigned agents. Use when the user asks to plan, decompose, or break work into execution-ready tasks.
Generates comprehensive documentation explaining how a codebase works, including architecture, key components, data flow, and development guidelines. Use when user wants to understand unfamiliar code, create onboarding docs, document architecture, or explain how the system works.
Conduct a targeted code exploration of the repository, and document the process of "Ask Questions → Read Code → Draw Conclusions" as searchable evidence for direct reuse when similar questions arise next time. There are three types: question (investigate code around a specific question and provide conclusions), module-overview (sort out the structure, boundaries, entry points, and dependencies of a module), and spike (conduct lightweight technical exploration of multiple possible directions without making final decisions). Trigger scenarios: Users say "Let's explore first", "How is X implemented in this repository", "Quickly get familiar with this module", "Archive the exploration results". Refer to `codestable/reference/system-overview.md` for how to distinguish it from learning / tricks / decisions.
Automate 7-phase feature development with specialized agents (code-explorer, code-architect, code-reviewer). Use for multi-file features, architectural decisions, or encountering ambiguous requirements, integration patterns, design approach errors.
Use this when you are exploring the codebase. It lets you ask the AI who wrote code questions about how things work and why they chose to build things the way they did. Think of it as asking the engineer who wrote the code for help understanding it.