Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
Version History
- Removed redundant CLAWHUB_SUMMARY.md; summary is now provided in the SKILL.md front-matter.
What This Skill Does
DGR produces a structured JSON artifact for each decision, capturing assumptions, risks, a recommendation, and a consistency check. It runs in three modes (dgr_min, dgr_full, dgr_strict) to match the stakes of the decision. Output is schema-validated and intended for storage in audit logs, tickets, or incident records.
Unlike freeform reasoning, DGR enforces a machine-validated schema so decision records are consistent, searchable, and storable without manual formatting.
When to use it
- Logging vendor selection rationale in a project ticket
- Documenting assumptions before a production deployment
- Recording reasoning behind an incident response call
- Capturing risk analysis for a policy change
- Generating an audit-ready artifact for a budget allocation decision
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: Should we migrate our authentication service to OAuth2? (mode: dgr_full)
- 1Accepts the decision request and mode selection
- 2Identifies decision context and flags any missing key inputs
- 3Enumerates explicit assumptions and risks with associated severity
- 4Generates a recommendation with rationale and sets review_required flag accordingly
- 5Runs a consistency check and returns a schema-valid JSON artifact
A single JSON artifact containing context, assumptions, risks, recommendation, and consistency_check fields, ready for storage in a ticket or audit log