decision-grade-reasoning

by sapenov · View original on ClawHub

Audit-ready decision artifacts for LLM outputs — assumptions, risks, recommendation, and review gating (schema-valid JSON).

Module AI Quality v1.0.4 Audited 2026-02-07
94 Trust

Permissions

File Read Can read project files
File Write Can write and modify files
Network No network access
Shell No shell access

Risk Assessment

Moderate Risk

This skill requests 2 of 4 possible permissions. Moderate scope — review that both permissions are necessary for its stated purpose.

SKILL.md

Purpose: produce an auditable, machine-validated decision record for review and storage.

Slug: dgr · Version: 1.0.4 · Modes: dgr_min / dgr_full / dgr_strict · Output: schema-valid JSON

What this skill does

DGR is a reasoning governance protocol that produces a machine-validated, auditable artifact describing:

  • the decision context,
  • explicit assumptions and risks,
  • a recommendation with rationale,
  • and a consistency check.

This skill is designed for high-stakes or review-required decisions where you want traceability and structured review.

How to use

  1. Ask your question — Provide a decision request or problem context
  2. Pick mode: dgr_min | dgr_full | dgr_strict
  3. Store JSON artifact in ticket / incident / audit log

What this skill is NOT (non-claims)

This skill does NOT guarantee:

  • correctness, optimality, or truth,
  • elimination of hallucinations,
  • legal/medical/financial advice suitability,
  • or regulatory compliance by itself.

DGR improves process quality (clarity, traceability, reviewability) — not outcome certainty.

When to use

Use when you need:

  • an auditable record of reasoning,
  • explicit assumptions/risks surfaced,
  • reviewer-friendly structure,
  • a consistent output format across tasks and models.

Inputs

  • A user request/question (free text).
  • Optional: context identifiers (ticket ID, policy name), and desired mode: dgr_min, dgr_full, or dgr_strict.

Mode Behavior

Mode Speed Detail Level Clarifications Review Required Use Case
dgr_min Fastest Minimal compliant output Only critical gaps Risk-based Quick decisions, low stakes
dgr_full Moderate Fuller decomposition + alternatives More proactive Balanced Standard decision support
dgr_strict Slower Conservative analysis More questioning Default on ambiguity High-stakes, uncertain contexts

Outputs

A single JSON artifact matching schema.json.

Minimum acceptance criteria (see schema.json):

  • at least 1 assumption
  • at least 1 risk
  • recommendation present
  • consistency_check present

Safety / governance boundaries

  • Always ask for clarification if key decision inputs are missing.
  • If the decision is high-risk, escalate via recommendation.review_required = true.
  • If uncertainty is high, explicitly state uncertainty and limit scope.
  • Do not fabricate sources or cite documents you did not see.

Files in this skill

  • prompt.md — operational instructions
  • schema.json — output schema (stub aligned to DGR spec)
  • examples/*.md — example inputs and outputs
  • field_guide.md — how to interpret DGR artifact fields

Quick start

  1. Provide a decision request.
  2. Choose a mode (dgr_min default).
  3. The skill returns a JSON artifact suitable for review and storage.

Changelog

1.0.4 — Remove redundant CLAWHUB_SUMMARY.md; summary now sourced from SKILL.md front-matter.

1.0.3 — Tighten front-matter description for better conversion, add reasoning category, compress identity block for faster scanning.

1.0.2 — Add ClawHub front-matter metadata with emoji and homepage for improved discovery and presentation.

1.0.0 — Initial public release of DGR skill bundle with auditable decision reasoning framework, governance protocols, and structured output format.

Note: This is an opt-in reasoning mode. It is meant to be used alongside human decision-making, not as a replacement.

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Related skills in AI Quality

Why You Need decision-grade-reasoning

When AI agents make decisions — choosing architectures, recommending tools, evaluating trade-offs — their reasoning is usually invisible. You see the conclusion but not the assumptions, risks, or alternatives considered. Decision-Grade Reasoning (DGR) produces auditable, machine-validated decision records that document the full reasoning chain.

With 374 downloads, DGR is used by teams that need governance and accountability for AI-assisted decisions. Each decision artifact includes explicit assumptions, identified risks, a clear recommendation, and review gating — all output as schema-valid JSON that can be stored, queried, and audited.

This skill is particularly valuable in regulated environments, architecture reviews, and any context where you need to explain why a decision was made — not just what was decided.

Common Use Cases

  • Generate audit-ready decision records for architecture and technology choices
  • Document assumptions, risks, and alternatives for team review
  • Produce schema-valid JSON decision artifacts for governance workflows
  • Add review gating to ensure decisions meet quality thresholds before execution
  • Create a searchable archive of past decisions with full reasoning context

Frequently Asked Questions

What format does the decision output use?

Schema-valid JSON with fields for decision context, assumptions, risks, recommendation, and review status. The schema is versioned and machine-parseable for integration with governance tools.

What are the different DGR modes?

DGR supports three modes: dgr_min (lightweight, key fields only), dgr_full (comprehensive with all supporting evidence), and dgr_strict (full mode plus mandatory review gating).

Does it need network access?

No. DGR is a reasoning and documentation skill that runs entirely offline. It reads context from your project files and outputs structured decision records.

Related Guides