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Run a studio-style agent control layer that turns a user goal into an explainable plan, deterministic decision flow, permission verdicts, and browser-first execution after approval. Use when the user
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Baseado na classificação ocupacional SOC
Call the externally deployed DSG QUBO Policy Optimizer API to run a deterministic QUBO/Ising policy optimization. Use when a user wants to run a real optimization against the hosted service rather than the local Kotlin engine: registering/logging in for an api_key, checking account usage, and submitting an optimization run for a regulatory domain under a budget cap. Auth is an `api_key` query parameter that must come from runtime env, never from the repo. Running a real optimization may trigger solver work and may incur cost under the service's pricing tier.
Reason about deterministic QUBO/Ising policy optimization and Z3/SMT-style formal constraint verification using the Compliance-ising-z3-Deterministic engine. Use when a user asks to optimize a policy/control selection under a budget, verify logical constraints (implication, equivalence, mutual exclusion, at-least-one, cost cap), run a what-if counterfactual on budget shifts, or produce a reproducible, hash-chained compliance decision. The underlying engine is native Kotlin (no external Z3 process); constraint checks are Z3/SMT-style, implemented deterministically.
Run a studio-style agent control layer that turns a user goal into an explainable plan, deterministic decision flow, permission verdicts, and browser-first execution after approval. Use when the user
Use this skill when creating, packaging, validating, publishing, or maintaining DSG GitHub Marketplace Actions. It turns DSG control-plane, action-layer permission gates, deterministic GO/NO-GO valida
Use this skill for DSG multi-source governance work that combines UI trust upgrades, action-layer permission gates, deterministic execution, marketplace/enterprise cutover, portable SaaS architecture
Apply the DSG ONE / ProofGate truth boundary and claim policy. Use when writing or reviewing any status, readiness, compliance, or capability statement destined for code, docs, commits, PRs, issues, comments, logs, or a response. Classifies each statement as verified fact / inference / pending / blocked / not verified, and flags forbidden readiness claims that require fresh evidence.
| name | dsg-action-layer-ged |
| version | 1.0.0 |
| author | DSG Team |
| license | MIT |
| description | Run a studio-style agent control layer that turns a user goal into an explainable plan, deterministic decision flow, permission verdicts, and browser-first execution after approval. Use when the user |
DSG ONE / ProofGate Control Plane is not an AI Agent framework. It is an AI Governance Control Plane — the layer that decides what Agents are allowed to do, captures cryptographic proof of every decision, and makes those decisions replayable months later.
"Cloudflare = Control Plane of the Internet. Datadog = Control Plane of Observability. DSG = Control Plane of AI Governance."
| User intent | Use this skill |
|---|---|
| "Gate this action before running it" | ✅ Yes — call gate-evaluate first |
| "Why did the AI decide X six months ago?" | ✅ Yes — use replay governance |
| "Get a compliance evidence pack for our audit" | ✅ Yes — use compliance evidence |
| "Execute this plan with a credential and conformance check" | ✅ Yes — Hermes executor |
| "What governance posture should this AI agent run with?" | ✅ Yes — runtime governance |
| "Build a generic AI feature with no governance requirement" | ❌ Out of scope |
Read the relevant reference file before answering or implementing any governance question.
| Capability | When to use | Reference |
|---|---|---|
| Deterministic Safety Gate | Before any agent action — get PASS/BLOCK/REVIEW + proof hash | <references/gate-evaluate.md> |
| Replayable Governance | Audit question: "Why did AI decide X at time T?" | <references/replay-governance.md> |
| Compliance Evidence Pack | Preparing for SOC2 / ISO27001 / EU AI Act / NIST audit | <references/compliance-evidence.md> |
| Runtime Governance | Lifecycle: Before / During / After agent execution | <references/runtime-governance.md> |
| Hermes Controlled Executor | Full Plan → Conformance Check → Credential Grant → Execute → Evidence | <references/hermes-executor.md> |
When a user asks about an AI agent action:
1. PLAN → What is the goal? What is the risk level?
2. GATE → Call POST /api/dsg/v1/gates/evaluate
→ Get gateStatus: PASS | BLOCK | REVIEW
→ Get proofHash (cryptographic evidence)
3. DECIDE → PASS → proceed to execution
REVIEW → show user the plan, require approval
BLOCK → halt, return reason + proof
4. EXECUTE → If PASS or approved REVIEW:
→ Hermes controlled executor (if credential needed)
→ Or direct execution with audit logging
5. COMMIT → POST /api/spine/execute to commit evidence
→ Runtime evidence stored in Supabase
6. REPLAY → Any future audit can replay from stored proof + policy version
| gateStatus | riskLevel | Action |
|---|---|---|
PASS | any | Proceed. Log proofHash. |
REVIEW | low–medium | Show plan to user. Require explicit approval. |
BLOCK | any | Halt. Return reason. Never proceed. |
UNSUPPORTED | low | Map to REVIEW — request human check |
UNSUPPORTED | medium–critical | Map to BLOCK — refuse execution |
UNSUPPORTED is never PASS.
riskLevel: medium or higher.UNSUPPORTED is safe — it maps to REVIEW or BLOCK, never PASS.https://tdealer01-crypto-dsg-control-plane.vercel.app
Set DSG_CONTROL_PLANE_URL to override for local or staging.
All gate/proof/compliance endpoints require:
Authorization: Bearer <DSG_API_KEY>
Obtain API key from the DSG dashboard → API Keys. Free tier: 50 evaluations/month. Upgrade at /pricing#dsg-gate.