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:
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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:
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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.