RiskRadar AI is an intelligent code risk review and release-quality gate skill that transforms Git Diffs and code modifications into highly actionable engineering insights and QA evidence.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install riskradar-ai
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install riskradar-ai using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is RiskRadar AI?
RiskRadar AI acts as an automated release-quality gate and security guard for modern software development teams. By analyzing code changes, pull requests, and Git Diffs, this specialized engine generates concrete engineering evidence, highlighting critical risks, functional regression gaps, security vulnerabilities, and dependency impacts before any code hits production. It is a vital component for teams aiming to maintain absolute software integrity throughout the continuous delivery pipeline.
As part of the Openclaw Skills ecosystem, RiskRadar AI excels not just at traditional codebase analysis, but specifically at evaluating complex AI-enabled workloads. It scrutinizes LLM calls, RAG pipelines, agentic workflows, prompt configurations, and tool integrations to flag potential runtime failures, model vulnerabilities, and safety risks. This ensures that both deterministic code and probabilistic AI systems meet rigorous quality standards.
RiskRadar AI Use Cases
- Reviewing Git Diffs and Pull Requests: Automatically inspect incoming code changes before code merge or deployment to identify security vulnerabilities, logical flaws, and runtime risks.
- Establishing Release Quality Gates: Provide teams with clear, actionable release readiness indicators, complete with confidence levels and impact severity scores.
- Auditing AI, Agentic, and RAG Workflows: Safely review prompt templates, LLM API calls, retrieval-augmented generation architectures, model fallbacks, and multi-agent systems.
- Generating Target Test Strategies: Quickly build comprehensive testing blueprints that detail specific requirements for unit, E2E, performance, and adversarial QA validation.
- Assessing Third-Party Dependency Impact: Map out structural and architectural changes to evaluate how dependency updates and code refactoring influence downstream systems.
How RiskRadar AI Works
- Analyze and Scope Code Changes: Locate the exact files, modules, Git Diff records, and boundary APIs affected by the latest code updates.
- Map System Dependencies: Trace state transitions, data structures, network and database calls, third-party libraries, and asynchronous worker actions.
- Isolate AI and LLM Assets: Detect and analyze integrated AI components such as prompt configurations, RAG vectors, agent tooling, and fallback mechanisms.
- Evaluate Multi-Dimensional Risks: Execute checks for functional flaws, runtime anomalies, regression gaps, security bugs, and performance bottlenecks.
- Synthesize Technical Evidence: Correlate every single discovered risk back to actual lines of code, system configuration files, or database schemas.
- Produce QA Test Suites: Outline customized recommendations for unit, integration, resilience, and adversarial testing across the entire codebase.
- Formulate the Risk Radar Report: Output a comprehensive Markdown document outlining executive release status, risk matrixes, and verification plans.
RiskRadar AI Setup
To run RiskRadar AI within your workspace, ensure you have the Openclaw Skills setup and configured.
Step 1: Install and Initialize
Add the skill configuration to your workspace directory:
claw skill install riskradar-ai
Step 2: Configure Environment and References
Ensure you have the required playbook reference available at references/code-risk-review-playbook.md inside your repository directory.
mkdir -p references/
curl -o references/code-risk-review-playbook.md https://raw.githubusercontent.com/openclaw/skills/main/references/code-risk-review-playbook.md
Step 3: Trigger the Code Risk Review
Execute the skill agent by targetting one of the supported triggers via your CLI:
# Trigger the risk review for the current git diff
claw run code-risk-review --diff HEAD~1
RiskRadar AI Data Schema & Taxonomy
RiskRadar AI operates on structured system inputs and outputs. It structures its final delivery inside the following workspace schema:
| File / Directory | Description | Format |
|---|---|---|
references/code-risk-review-playbook.md |
Core playbook specifying risk classification guidelines, priority scoring, and criteria rules. | Markdown |
reports/riskradar-report-[timestamp].md |
Generated audit detailing release recommendations, risk tables, and test guidelines. | Markdown |
Meta-Data Taxonomy
Every generated code risk report conforms to a standardized evaluation schema, including:
- Risk Level: High, Medium, Low.
- Confidence Level: High, Medium, Low (dependent on verifiable code evidence).
- Impact Scope: Functional, performance, security, database schema, AI/RAG behavior.
- Verification Strategy: Specific testing type suggested (e.g., adversarial, unit, regression).
RiskRadar AI Advanced Features
- Agentic AI & RAG Security Checks: Deep code inspections for prompt injections, fallback behavior, vector retrieval issues, and model-routing failures.
- Evidence-Based Quality Assurance: Guarantees that no false positives are logged by strictly requiring explicit lines of code or system configurations as verification evidence.
- Comprehensive Multi-Layer Test Blueprinting: Instantly constructs test plans spanning unit testing, end-to-end integration, performance profiling, monitoring boundaries, and resilience verification.
- Automated CI/CD Quality Gate Hooking: Designed to run seamlessly within Git pipelines, enabling automatic blocking of high-risk pull requests before compilation or deployment.
SKILL.md
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