/ai-code-generation-guardrails

name: ai-code-generation-guardrails
description: 'Autonomous AI code generation safety guardrail register: static AST analysis, forbidden import filters, and zero-day vulnerability checks.'
category: engineering
risk: safe
source: self
source_type: self
date_added: "2026-10-01"
author: Ranjeet2063
tags: [ai, security, guardrails, ast, code-quality, devsecops]
tools: []
source_repo: Ranjeet2063/agentic-awesome-skills

AI Code Generation Security Guardrails

**What it is:** Defines automated AST validation filters, forbidden pattern checks, and boundary invariants for code synthesized by generative AI models.

Overview

Provides a standardized, auditable framework and data model for **AI Code Generation Security Guardrails** operations across distributed engineering and decentralized application systems.

When to Use This Skill

• When formalizing architectural contracts, security invariants, or operational limits for AI Code Generation Security Guardrails.
• When cross-functional review is required between protocol developers, smart contract auditors, and AI engineering agents.
• When generating reproducible CSV, SQL DDL, JSON Schema, and Notion property registers for tracking compliance.

How It Works

1. Define the parameters, thresholds, and identity bindings required for the target operational register. 2. Select appropriate boundary enforcement values from validated enum select sets. 3. Export standardized artifacts (CSV table, SQL DDL, JSON Schema) to integrate into validation CI pipelines.

Field Reference

| # | Field Name | Type | SQL Type | JSON Schema Type | Notion Property Type | Example Value | |---|------------|------|----------|------------------|----------------------|---------------| | 1 | Guardrail Policy ID | `id` | `SERIAL PRIMARY KEY` | `integer` | Text | `SEC-001` | | 2 | Target Language Pipeline | `select` | `VARCHAR(32)` | `string` | Select | `Rust (Soroban)` | | 3 | AST Security Scanner | `text` | `VARCHAR(64)` | `string` | Text | `cargo-audit & clippy` | | 4 | Dangerous Primitives Filter | `select` | `VARCHAR(16)` | `string` | Select | `Yes` | | 5 | Disallowed Unsafe Blocks | `select` | `VARCHAR(16)` | `string` | Select | `Enforced Strict` | | 6 | Reentrancy Detection Rule | `select` | `VARCHAR(32)` | `string` | Select | `CEI Pattern Enforced` | | 7 | Prompt Injection Protection | `select` | `VARCHAR(32)` | `string` | Select | `Dual-Layer Boundary` | | 8 | Max Allowed Cyclomatic Complexity | `number` | `INTEGER` | `number` | Number | `15` | | 9 | Pipeline Enforcement Status | `select` | `VARCHAR(32)` | `string` | Select | `Blocking CI Gate` | | 10 | Lead Security Engineer | `text` | `VARCHAR(64)` | `string` | Text | `Ranjeet2063` | | 11 | Policy Verification Date | `date` | `DATE` | `string, format: date` | Date | `2026-10-01` |

Select Options

**Target Language Pipeline**

Rust (Soroban) | TypeScript (React) | Solidity (EVM) | Python (FastAPI)

**Dangerous Primitives Filter**

Yes | No

**Disallowed Unsafe Blocks**

Enforced Strict | Warning Permissive

**Reentrancy Detection Rule**

CEI Pattern Enforced | Mutex Lock | Unchecked

**Prompt Injection Protection**

Dual-Layer Boundary | Heuristic Filter | None

**Pipeline Enforcement Status**

Blocking CI Gate | Advisory Only | Disabled

Relations

• `Audit Reference` -> links to the formal review documentation or test repository.
• `Target Architecture` -> links to the deployed contract or autonomous agent runtime component.

Examples

**Prompt**

How do I configure and track AI Code Generation Security Guardrails for our production environment?

**Recommended Next Step**

> Generate the unified field schema, SQL DDL migration, and JSON validation schema to register into your system catalog. > > Workflow: Define criteria -> Run automated verification -> Record baseline -> Monitor invariants.

Best Practices

• Enforce strict typing on numerical bounds and currency amounts; avoid unstructured free-text fields for critical states.
• Re-run validation test suites on every state-altering commit or parameter change.
• Keep example data synthetic and isolated from production cryptographic keys or private endpoints.

Limitations

• Provides architectural specifications, data models, and verification schemas; does not execute direct transaction signing without authorized external tooling.
• Requires network connectivity and valid RPC credentials when querying on-chain states.

Security & Safety Notes

• All parameters declare `risk: safe`. No unauthorized state modification or privileged credential access is performed.
• Use synthetic dummy keys and mock addresses in test suites and local verification scripts.

Common Pitfalls

• **Problem:** Mismatched decimal precision between contract runtime and database register.

**Solution:** Always verify decimals using the explicit field mapping in this reference.

• **Problem:** Missing authorization checks prior to state update.

**Solution:** Cross-validate against the Security Audit register before deployment.

Related Skills

• @ai-agent-tool-routing - covers tool schema registration and retry policy.
• @ai-agent-evaluation-benchmarking - covers task-completion and cost benchmarking.
• @ai-prompt-regression-testing - covers prompt regression baselines and drift.

Reusable Prompt

I want to establish a verified AI Code Generation Security Guardrails register for our production protocol.
Guide me through the required field parameters and output the corresponding SQL DDL and JSON Schema.