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abeltennyson

abe-dgr

by AbelTennyson · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ✓ Security Clean
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Install in OpenClaw
/install abe-dgr
Description
Audit-ready decision artifacts for LLM outputs — assumptions, risks, recommendation, and review gating (schema-valid JSON).
README (SKILL.md)

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.

Usage Guidance
This skill appears coherent and limited to producing schema-valid decision artifacts. Before installing: (1) confirm your organization’s policy about storing decision artifacts (they may contain sensitive context or PII); (2) instruct users to avoid pasting secrets into prompts or artifacts; (3) verify the schema and enforcement you need (e.g., add organization-specific fields if required); and (4) remember DGR is a process/traceability tool — do not rely on it as an authoritative legal/medical/financial decision maker without human review.
Capability Analysis
Type: OpenClaw Skill Name: abe-dgr Version: 1.0.0 The 'dgr' skill bundle is a reasoning governance protocol designed to produce structured, auditable JSON artifacts for decision-making. It includes a clear schema (schema.json), comprehensive documentation (SKILL.md, field_guide.md), and a system prompt (prompt.md) that emphasizes safety, human review, and the avoidance of fabricated evidence. No signs of data exfiltration, malicious execution, or harmful prompt injection were found.
Capability Tags
cryptocan-make-purchases
Capability Assessment
Purpose & Capability
Name/description (decision-grade reasoning) matches the included files (prompt, schema, field guide, examples). No unrelated credentials, binaries, or config paths are requested.
Instruction Scope
SKILL.md and prompt.md strictly instruct the agent to produce a JSON artifact conforming to schema.json and to ask clarifying questions when inputs are missing; they do not instruct reading local files, environment variables, or contacting external endpoints.
Install Mechanism
No install spec or code files; instruction-only distribution means nothing is downloaded or written to disk by an installer.
Credentials
The skill declares no required environment variables or credentials. The runtime instructions also do not reference secrets or external service tokens.
Persistence & Privilege
Skill is not marked always:true and uses default model-invocation (agent may call it autonomously), which is appropriate for an opt-in reasoning helper; it does not request system-wide changes or modify other skills.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install abe-dgr
  3. After installation, invoke the skill by name or use /abe-dgr
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
- Initial public release of the DGR (Decision‑Grade Reasoning) skill. - Provides machine‑validated, auditable artifacts for LLM-supported decisions. - Surfaces explicit assumptions, risks, recommendations, and consistency checks in schema-valid JSON. - Supports review gating and multiple modes (`dgr_min`, `dgr_full`, `dgr_strict`) for different decision contexts. - Designed for high-stakes or review-required scenarios needing traceable, reviewer-friendly reasoning.
Metadata
Slug abe-dgr
Version 1.0.0
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 1
Frequently Asked Questions

What is abe-dgr?

Audit-ready decision artifacts for LLM outputs — assumptions, risks, recommendation, and review gating (schema-valid JSON). It is an AI Agent Skill for Claude Code / OpenClaw, with 86 downloads so far.

How do I install abe-dgr?

Run "/install abe-dgr" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is abe-dgr free?

Yes, abe-dgr is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does abe-dgr support?

abe-dgr is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created abe-dgr?

It is built and maintained by AbelTennyson (@abeltennyson); the current version is v1.0.0.

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