dgr
Audit-ready decision artifacts for LLM outputs — assumptions, risks, recommendation, and review gating (schema-valid JSON).
- License 许可
- MIT
- 最近更新
- 2026-08-23
- Stars
- ★ 1
- Forks
- 0
01 前言 Front Matter
- name
- dgr
- homepage
- https://www.clawhub.ai/sapenov/dgr
- metadata
- { "category": "reasoning", "clawdbot": { "emoji": "?" } }
- description
- Audit-ready decision artifacts for LLM outputs — assumptions, risks, recommendation, and review gating (schema-valid JSON).
02 资源文件 7
- 参考文档_meta.json286 B
- 参考文档examples/access_request.md2.2 KB
- 参考文档examples/incident_triage.md3.2 KB
- 参考文档examples/loan_preapproval.md2.6 KB
- 参考文档field_guide.md3.7 KB
- 参考文档prompt.md2.0 KB
- 参考文档schema.json4.5 KB
03 SKILL.md 正文
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
- Ask your question — Provide a decision request or problem context
- Pick mode:
dgr_min|dgr_full|dgr_strict - 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, ordgr_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
recommendationpresentconsistency_checkpresent
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 instructionsschema.json— output schema (stub aligned to DGR spec)examples/*.md— example inputs and outputsfield_guide.md— how to interpret DGR artifact fields
Quick start
- Provide a decision request.
- Choose a mode (
dgr_mindefault). - 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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