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De-Armor Open-Source Release Focuses on Task-Oriented AI Response Calibration

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· GlobeWire Daily Editorial Desk · Source: github.com

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Key takeawaysThe open-source project de-armor (ninetyhe-90/de-armor) has been released as a task-oriented response calibration skill designed to improve AI output utility. Rather than relying on rigid banned-word lists or arbitrary scoring thresholds, the framework aims to preserve essential facts, conditions, uncertainties, and permission boundaries while stripping…
De-Armor Open-Source Release Focuses on Task-Oriented AI Response Calibration

Image: GitHub · Photo source · Resized and letterboxed to 16:9; no AI generation

Overview of De-Armor

The open-source project de-armor (ninetyhe-90/de-armor) has been released as a task-oriented response calibration skill designed to improve AI output utility. Rather than relying on rigid banned-word lists or arbitrary scoring thresholds, the framework aims to preserve essential facts, conditions, uncertainties, and permission boundaries while stripping away conversational padding and social filler.

According to the project documentation, the tool is packaged as a portable `SKILL.md` file rather than a vendor-specific plugin, making it compatible with various AI agents and system prompts without requiring external runtime dependencies or proprietary APIs.

Core Architecture and Version 2 Updates

The recently deployed version 2 framework shifts from numerical scoring to a task-oriented audit approach. Key updates include:

  • Task-Oriented Audits: Evaluates whether an AI response retains critical facts, conditions, and boundaries while delivering the requested artifact.
  • Schema and Contract Changes: Utilizes `schema_version: 2` and supports information contracts to flag missing anchors or unauthorized claims.
  • Offline Machine Check: Includes an optional Python-based script (`armor_lint.py`) that operates locally using the standard library, requiring no network access or third-party packages.
  • Interactive Lab: Provides an offline HTML lab (`assets/de-armor-demo.html`) backed by a reproducible dataset (`assets/cases.json`) to demonstrate calibration scenarios.

Practical Implications and Verification

The project emphasizes that automated checks serve as a secondary validation layer rather than a definitive guarantee of semantic or factual correctness. Human review remains necessary to verify domain-specific accuracy.

  • Integration Flexibility: Can be integrated via a skills directory, appended to system prompts, or prepended to conversation contexts.
  • Safety and Constraints: The documentation notes that the skill does not override host instructions, safety limits, or authorization protocols.

How to Verify

Users and developers interested in auditing or deploying the utility can review the official repository on GitHub (`ninetyhe-90/de-armor`). All specifications, offline scripts, and scenario datasets are publicly accessible for independent inspection.

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Read the original source: ninetyhe-90/de-armor: 破甲 / De-Armor:任务导向的回复校准,保住事实与条件,减少无效防御,补齐真正交付。 - GitHub

AI-assisted reporting; source claims are attributed and have not been independently verified.

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