DietrichGebert/ponytail
Ponytail is a set of plugins for AI agent development tools like Claude Code and GitHub Copilot CLI that helps agents write more efficient, concise, and safe code by following a 'lazy senior dev' m...
Awesome Hermes Plugins › Tools & Automation
sergiparpal/meal-manager
meal-manager is a Hermes Agent plugin that helps a user decide what to cook and what to buy by reasoning over their current fridge contents, recipe catalog and cooking history. It registers twenty-six tool handlers through the standard `__init__.py:register(ctx)` entry point, with zero external dependencies. Its stated design separates concerns deliberately: the language model acts only as a semantic translator at the boundary, mapping natural-language requests such as "we had carbonara tonight" onto a typed, validated tool call, while all ranking, inventory updates, recipe storage and persistence run as deterministic, testable Python with no further model round-trips. Dish suggestions rank the whole recipe catalog with a weighted blend of ingredient availability (essential ingredients gate a dish entirely; optional ingredients contribute a capped score) and cooking recency, excluding anything cooked within the last two days; the availability/recency blend starts at 60/40 and self-adjusts slowly through a bounded, deterministic online learner as meals are logged, with the current weighting inspectable at any time. A shopping-list feature ranks ingredients by the smallest basket that unlocks a new dish, then by how many dishes that basket reaches. Other tools cover fridge inventory as approximate portion counts with unlimited pantry staples, an append-only cooking-history log that supports backdated entries without disturbing cooldown calculations, ingredient aliasing that merges spellings into one canonical name while preserving the earlier of two expiry dates, and free-form cooking instructions attached to a recipe. All reasoning is reproducible given the same state files, since the scoring constants, cooldown and recency windows, and the online learner's step size are fixed in source rather than emergent from the model.
https://github.com/sergiparpal/meal-manager
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