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Claude Skills by ErtasAI
github.com/ErtasAI9 skills0 installs0 views
- Costing A Model Vs An ApiDecide whether running your own model actually costs less than paying a hosted API, and at what volume the two cross over. Gathers the real inputs, token counts per request, today's provider pricing, hosting or hardware cost, and one-off training cost, then computes the breakeven point and reports it with the assumptions visible, so the number can be challenged. Use when someone asks whether self-hosting is cheaper, what a fine-tune would save, when owning a model pays for itself, or wants to...Votes: 0GitHub stars: 2
- Debugging A Bad Fine TuneDiagnose a fine-tuned model that behaves badly at inference: output that never stops or repeats forever, replies that ignore the training data, garbled or nonsense tokens, a model that answers as the wrong persona, an adapter that will not load against its base, or quality that collapsed after quantisation. Works backwards from the symptom to the cause, most often a chat template mismatch between training and inference, a missing end-of-sequence token, a tokenizer mismatch, or overfitting on ...Votes: 0GitHub stars: 2
- Evaluating A Tuned ModelLearn whether a fine-tuned model is actually better than what it started from, instead of guessing from a few prompts that felt good. Builds a probe set from the task definition, writes a rubric, runs the tuned model against its own base model and optionally against a hosted frontier model, scores the results, and produces a scorecard you can rerun after every training run. Use when someone asks whether a fine-tune worked, whether it is good enough to ship, how it compares to the base model o...Votes: 0GitHub stars: 2
- Inspecting A Model BundleWork out what is inside a model directory and what can actually run it. Reads GGUF headers, safetensors headers, adapter_config.json and config.json to identify whether it is GGUF, a LoRA adapter or a merged checkpoint, which base model it came from, which runtimes can load it directly versus which require conversion, and what defects the export left behind. Use when someone has a model folder or zip and does not know what is in it, asks what format a model is, asks whether a model runs with ...Votes: 0GitHub stars: 2
- Scoping A Custom ModelDecide whether a task actually needs a custom trained model, or whether a better prompt, retrieval, a smaller hosted model, or routing between models would do the job, and if a custom model is right, work out what it has to be good at and how you will know. Use when someone says they want to fine-tune, train their own model, run AI locally, offline, or with no network connection, cut their AI bill, own their model, get a model to answer the same way every time instead of drifting, wonders if ...Votes: 0GitHub stars: 2
- Shipping A Model In A Flutter AppPut a local language model inside a Flutter app and get it generating on device. Covers llamadart, which runs GGUF through llama.cpp and .litertlm through LiteRT-LM under one API on Android, iOS, macOS, Windows, Linux and Flutter web, plus LoRA adapter loading, flutter_gemma as the MediaPipe-lineage alternative, background isolate and threading behaviour, and whether to bundle the model or download it on first run. Use when the project is Flutter or Dart and someone wants on-device or offline...Votes: 0GitHub stars: 2
- Shipping A Model In A React Native AppPut a local language model inside a React Native or Expo app and get it generating on device. Covers react-native-executorch for .pte models and llama.rn for GGUF models, choosing between them, native build configuration and model loading, streaming answers into the UI as tokens arrive, and whether to bundle the model in the binary or download it on first run. Use when the project is React Native or Expo and someone wants on-device or offline AI, local inference, or a model running without an...Votes: 0GitHub stars: 2
- Shipping A Model In An Android AppPut a local language model inside a native Android app written in Kotlin or Java. Covers MediaPipe LLM Inference and LiteRT-LM for .task and .litertlm models, llama.cpp through the NDK for GGUF, ML Kit GenAI and Gemini Nano where a built-in model is enough, GPU and NNAPI delegation, Play asset packs, and whether to bundle the model or download it on first run. Use when the project is a native Android app and someone wants on-device or offline AI or local inference. Not for React Native or Exp...Votes: 0GitHub stars: 2
- Shipping A Model In An Ios AppPut a local language model inside a native iOS or macOS app written in Swift. Covers MLX on Apple silicon, Core ML conversion, llama.cpp via a Swift wrapper, choosing between them, app binary size and memory limits, background and memory-pressure behaviour, and whether to bundle the model or download it on first run. Also covers when Apple Foundation Models is the better answer than shipping your own. Use when the project is native Swift for iOS, iPadOS or macOS and someone wants on-device or...Votes: 0GitHub stars: 2