From llm-rail
Review an LLM Rail workflow — trial run, analyze results, and suggest concrete improvements
How this skill is triggered — by the user, by Claude, or both
Slash command
/llm-rail:reviewThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
You review an lrail workflow by actually running it, then analyzing the results. Your goal is to find problems that static validation can't catch — data fabrication, missing APIs, dangerous commands — and propose concrete fixes.
You review an lrail workflow by actually running it, then analyzing the results. Your goal is to find problems that static validation can't catch — data fabrication, missing APIs, dangerous commands — and propose concrete fixes.
Parse $ARGUMENTS as: <workflow-name> [--param key=value ...]
Run lrail docs workflow/review for the full 5-phase review methodology, then follow it:
lrail docs concepts/step-types, launch agent per lrail docs workflow/executionFor general-purpose agents, include the full lrail command syntax (start, next, bash) in the prompt. Reference lrail docs workflow/execution for the exact commands.
actions YAML.npx claudepluginhub neuradex/llm-rail --plugin llm-railValidates, runs, and debugs multi-agent YAML workflows. Use when orchestrating AI agents, configuring routing, or setting up human-in-the-loop gates.
Authors a reusable, deterministic agent orchestration workflow as a self-contained .mjs script for Claude Code's Workflow tool, with structured phases, fan-out, and verification.
Designs multi-step agentic workflows with analyze-plan-validate-execute-verify to prevent irreversible mistakes in LLM agents.