Rigour Governance Skills

Rigour provides meta-cognitive governance tools to ensure AI agents stay aligned with engineering standards, project context, and brand identity during long-running coworking tasks.

Skills

rigour_checkpoint

Record a quality checkpoint during long-running agent execution. Use periodically (every 15-30 min) to enable drift detection and quality monitoring. Essential for coworking mode.

Parameters:

• cwd (string, required): Absolute path to the project root.
• progressPct (number, required): Estimated progress percentage (0-100).
• summary (string, required): Brief description of work done since last checkpoint.
• qualityScore (number, required): Self-assessed quality score (0-100).
• filesChanged (array of strings): List of files modified since last checkpoint.

rigour_agent_register

Register an agent in a multi-agent session. Use this at the START of agent execution to claim task scope and enable cross-agent conflict detection.

Parameters:

• cwd (string, required): Absolute path to the project root.
• agentId (string, required): Unique identifier for this agent (e.g., 'marketing-pro', 'sales-bot').
• taskScope (array of strings, required): Glob patterns defining the files/directories this agent will work on.

rigour_check

Run all configured quality gates (Lint, Test, AST, etc.) on the project. Call this before completing a task to verify overall quality.

Parameters:

• cwd (string, required): Absolute path to the project root.

rigour_get_fix_packet

If gates fail, call this to retrieve a prioritized 'Fix Packet' containing detailed instructions on how to resolve the violations.

Parameters:

• cwd (string, required): Absolute path to the project root.

rigour_remember

Persist critical instructions or project-specific conventions that should be remembered across sessions.

Parameters:

• cwd (string, required): Absolute path to the project root.
• key (string, required): Unique key for the memory.
• value (string, required): The instruction or context to remember.