immune

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Hybrid adaptive memory: Cheatsheet (positive patterns pre-generation) and Immune (negative patterns post-generation) with Hot/Cold tiered auto-learning. Triggers on: "scan for errors", "immune scan", "check output quality", "antibody scan". NOT for PR review (use pr-review) or repo audits (use repo-sentinel).

AI & Automation 313 stars 46 forks Updated 4 days ago MIT

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# Immune System v3 — Hybrid Cheatsheet + Immune You operate a hybrid adaptive system with two complementary memories: - **Cheatsheet** (positive patterns): domain-specific strategies injected BEFORE generation to improve output quality - **Immune** (negative patterns): antibodies that detect known errors and discover new threats AFTER generation Both memories use Hot/Cold tiering to keep context lean. ## Input Parsing The user invokes with content to scan. Parse these parameters: - **input**: The text/code/content to scan (required — either inline or from context) - **domain**: One of: fitness, code, writing, research, strategy, webdesign, \_global (default: auto-detect) - **domains**: Array of domains (overrides single domain). Example: `domains=fitness,code` - **constraints**: Any specific requirements the output should satisfy (optional) - **mode**: `full` (cheatsheet + scan, default) | `scan-only` (skip cheatsheet) | `cheatsheet-only` (return cheatsheet, no scan) <examples> <example> /immune Check this function for common pitfalls → domains=["code"] (auto-detected), mode=full </example> <example> /immune domain=fitness Vérifie ce programme de musculation → domains=["fitness"] (explicit) </example> <example> /immune domains=fitness,code Check this workout generator API → domains=["fitness", "code"] (multi-domain) </example> <example> /immune → scans the most recent output in the conversation </example> </examples> If no inline text is provided, scan the last substa...

Details

Author
Mathews-Tom
Repository
Mathews-Tom/armory
Created
5 months ago
Last Updated
4 days ago
Language
Python
License
MIT

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