Insert a trace-aware guardrail layer between agents and their tools so unsafe message patterns or tool-call sequences are blocked by explicit rules.
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Run score Not tested
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Insert a trace-aware guardrail layer between agents and their tools so unsafe message patterns or tool-call sequences are blocked by explicit rules.
Python environment or Invariant Gateway deployment, target LLM or MCP-enabled agent workflow, guardrail rules or policies, sample traces or live requests to evaluate
Requirements and caveats from upstream:
Basic usage or getting-started notes:
<a href="https://invariantlabs-ai.github.io/docs/mcp-scan/guardrails-reference/">Getting Started</a> |
Extracted from upstream docs: https://raw.githubusercontent.com/invariantlabs-ai/invariant/HEAD/README.md
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Use Caliper to run real agent tasks with and without a skill, MCP server, or rule change so reliability and token cost are measurable.
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Builds citation networks from Semantic Scholar API and CrossRef DOI metadata. Visualizes paper influence graphs using NetworkX, identifies seminal works, and tracks research lineage across fields.
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An ASE skill built around ACF Extended, the WordPress enhancement suite for Advanced Custom Fields that adds field types, admin improvements, front-end forms, options pages, and developer tooling. It is a practical fit for agents working inside complex WordPress content models and custom field workflows.
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