Orchestrate a seven-phase execution-gated pipeline for AI-generated artifacts.
npx skills add https://github.com/RyanDev1st/FORGER --skill forger
Please help me install this Agent Skill. Skill: forger Source: https://github.com/RyanDev1st/FORGER/tree/main Command: npx skills add https://github.com/RyanDev1st/FORGER --skill forger
This Skill solves the problem of AI hallucination in non-trivial tasks by forcing the agent to ground its work in live internet sources, survive adversarial cross-model review, and pass automated execution tests before shipping any artifact.
Run the forger pipeline on your current project by typing /forger followed by your task description.
High-intent search queries and answers about installing and using this skill.
You verify AI artifacts against live data using an execution-gated pipeline that forces agents to ground outputs in live sources and pass automated testing before delivery. This enforces structural invariants through adversarial cross-model review.
An execution-gated pipeline is a deterministic multi-phase workflow ensuring AI artifacts ship only after surviving adversarial review and passing automated execution tests. It enforces strict contracts from definition to final retention.
Prevent AI hallucination in system design by enforcing a seven-phase pipeline requiring agents to ground work in live data and pass automated execution tests. This validates outputs rigorously without manual verification.
Yes, the framework requires a configured workspace and integration with subagents to enforce structural invariants and audit gates. It orchestrates a strict seven-phase pipeline ensuring grounded, verified AI outputs.
Use an execution-gated pipeline for research synthesis when the cost of subtle errors is high and you need to trust agent output without manual verification. It ensures artifacts survive cross-model review and automated testing.
Yes, you can integrate automated testing within an AI agent pipeline to enforce execution gates. This guarantees generated artifacts pass acceptance tests and are grounded in live data before the workflow reaches final retention.
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