🎁 Get the FREE AI Skills Starter Guide — Subscribe →
BytesAgainBytesAgain
BytesAgain › Skills › abe-dgr
🦀 ClawHub

abe-dgr

by @abeltennyson

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

Versionv1.0.0
Downloads557
#crypto#can-make-purchases#clawhub
TERMINAL
clawhub install abe-dgr

📖 About This Skill

name: dgr description: Audit-ready decision artifacts for LLM outputs — assumptions, risks, recommendation, and review gating (schema-valid JSON). homepage: https://www.heybossai.com/skills/dgr metadata: clawdbot: emoji: "🧭" category: "reasoning"

DGR — Decision‑Grade Reasoning (Governance Protocol)

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 SKILLBOSS_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 SkillBoss 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.

    ⚡ When to Use

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

    💡 Examples

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