Analyzes a codebase using repomix + sequential-thinking + knowledge-graph. Returns structured tables covering architecture, runtime behaviors, and failure modes. Stores findings in .aim/ for future sessions. Use when onboarding to a new codebase or before planning a feature.
Executes MCP tool calls and CLI tasks while keeping tool schemas out of the main context window. Tries available AI CLIs in priority order before falling back to direct mcporter calls. Use when any agent needs to run an MCP tool without polluting its own context.
Runs the full coding workflow end-to-end for a given goal: clarify → analyze codebase → plan → implement → review → test. Spawns codebase-analyst and mcp-manager as subagents. Use when given a feature request or bug fix that requires understanding the codebase before touching code.
Generic task executor that delegates work to a specified AI CLI tool based on role assignment. Receives structured task with role, tool preference, and context from predecessor tasks. Use when the orchestrator needs to delegate a task to an external tool.
Use when starting work on a codebase you want to remember across sessions, extracting architectural facts, or querying persistent project knowledge instead of re-reading files. Centers the knowledge-graph MCP and project-local .aim storage.
Use code-review-graph through CLI or mcporter for codebase maps, impact analysis, semantic search, graph status, and review context without keeping the MCP server natively registered in Codex.
Use when the user is learning a new concept, wants to build skill through interaction, or asks for explanations alongside solutions. Guides Codex toward high skill-formation interaction modes instead of pure delegation.
Use this agent when a major project step has been completed and needs to be reviewed against the original plan and coding standards. Examples: After sub-phase implementation, after major feature completion, before merging to verify work meets requirements.
Use this agent when implementing a single sub-phase from an RLM plan. The agent should receive the full sub-phase text, current codebase context, and implement the feature with TDD discipline.
Use when RLM requirement involves debugging a bug, test failure, or unexpected behavior. Insert Phase 1.5 between Phase 1 and Phase 2 to perform systematic root cause analysis before attempting any fixes. Trigger phrases: "debug", "investigate", "failing tests", "crash", "root cause".
Master skill for parallel subagent-driven execution with automatic fallback to single-agent sequential mode. Use when implementing plans with multiple independent sub-phases (SP1, SP2...) to dispatch parallel subagents, or when requiring code review between implementation and testing. Trigger phrases: "parallelize"…
Use when implementing any code in RLM Phase 3. Enforces strict RED-GREEN-REFACTOR discipline with The Iron Law - no production code without a failing test first. Trigger phrases: "implement this", "add feature", "fix bug", "write a failing test", "TDD".
Use when starting any RLM requirement to set up an isolated git worktree. REQUIRED before Phase 1 - creates isolated workspace, verifies clean test baseline, and prevents main branch pollution. Trigger phrases: "create worktree", "worktree isolation", "set up worktree", "do not work on main".
Detect the project type from file-presence signals and generate developer roles. No LLM inference — deterministic file checks only.
★not rated 2 5mo agoA24 tokens
originalMIT
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