git:20260518.471df3d to git:20260710.2925510
17 added, 14 removed. Audit A to B.
---
- name: "Apply rule-based guardrails to agent traces and tool flows with Invariant"
- slug: "apply-rule-based-guardrails-to-agent-traces-and-tool-flows-with-invariant"
+ title: "Apply rule-based guardrails to agent traces and tool flows with Invariant"
description: "Insert a trace-aware guardrail layer between agents and their tools so unsafe message patterns or tool-call sequences are blocked by explicit rules."
- github_stars: 409
- verification: "listed"
+ verification: "security_reviewed"
source: "https://github.com/invariantlabs-ai/invariant"
author: "Invariant Labs"
publisher_type: "organization"
- category: "Security & Verification"
- framework: "Multi-Framework"
+ category:
+ - "Security & Verification"
+ framework:
+ - "Multi-Framework"
tool_ecosystem:
github_repo: "invariantlabs-ai/invariant"
github_stars: 409
npm_package: "invariant-ai"
npm_weekly_downloads: 1473
---
# Apply rule-based guardrails to agent traces and tool flows with Invariant
Insert a trace-aware guardrail layer between agents and their tools so unsafe message patterns or tool-call sequences are blocked by explicit rules.
## Prerequisites
Python environment or Invariant Gateway deployment, target LLM or MCP-enabled agent workflow, guardrail rules or policies, sample traces or live requests to evaluate
## Installation
- Requirements and caveats from upstream:
- - Guardrailing rules are simple Python-inspired matching rules, that can be written to identify and prevent malicious agent behavior:
- - python
- - Here, (msg: Message) automatically is assigned every checkable message, whereas the second line executes like regular Python. To facilitate checking Guardrails comes with an extensive standard library of operations, a...
+ Choose whichever fits your setup:
- Basic usage or getting-started notes:
- - <a href="https://invariantlabs-ai.github.io/docs/mcp-scan/guardrails-reference/">Getting Started</a> |
+ 1. Copy this skill folder into your local skills directory.
+ 2. Clone the repo and symlink or copy the skill into your agent workspace.
+ 3. Add the repo as a git submodule if you manage shared skills centrally.
+ 4. Install it through your internal provisioning or packaging workflow.
+ 5. Download the folder directly from GitHub and place it in your skills collection.
- - Source: https://github.com/invariantlabs-ai/invariant
- - Extracted from upstream docs: https://raw.githubusercontent.com/invariantlabs-ai/invariant/HEAD/README.md
+ Install command or upstream instructions:
+
+ ```
+ Install the upstream package or gateway from the documented instructions, define guardrail rules for the target workflow, then run Invariant inline as a proxy or locally against captured traces to review and enforce violations.
+ ```
## Documentation
- https://invariantlabs-ai.github.io/docs/mcp-scan/guardrails-reference/
## Source
- [Agent Skill Exchange](https://agentskillexchange.com/skills/apply-rule-based-guardrails-to-agent-traces-and-tool-flows-with-invariant/)