Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/mxslr/mlcraft/deployment-explainabilitynpx skills add mxslr/mlcraft --skill deployment-explainabilitygit clone --depth 1 https://github.com/mxslr/mlcraftWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00159 | $0.00828 |
| Opus 5 | $0.00079 | $0.00414 |
| Sonnet 5 | $0.00032 | $0.00166 |
| Haiku 4.5 | $0.00016 | $0.00083 |
Grade A, and why
deployment-explainability scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 41 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deployment and Explainability
Preprocessing PARITY (the bug that silently ruins deployments)
The raw input a user uploads MUST pass through exactly the same pipeline as training (resize, crop, enhancement like CLAHE, normalization). Package it as one shared function reused by both training and inference. A mismatch produces confident but wrong predictions.
Pick the explainability method that fits the model and task
Grad-CAM is only for convolutional image classifiers. It is not meaningful for detection, embeddings, tabular, or text. Choose by problem:
| Model or task | Explainability | Notes |
|---|---|---|
| CNN image classification | Grad-CAM, Grad-CAM++, Score-CAM | heatmap over the regions that drove the predicted class. Good for medical scans and single-object classification. |
| Vision Transformer classification | attention rollout, attention maps | more natural than Grad-CAM for pure ViTs. |
| Object detection (faces, vehicles, objects) | draw predicted boxes with class and confidence; optional D-RISE saliency | Grad-CAM is not standard for detectors. The boxes are the explanation. |
| Segmentation | overlay the predicted mask on the image, plus per-class confidence | |
| Face recognition or verification | nearest-neighbor example faces, embedding similarity score, t-SNE or UMAP of embeddings | Grad-CAM is not meaningful for metric-learning embeddings. |
| Tabular | SHAP (global summary and per-prediction), permutation importance | the standard for stakeholder trust. |
| Text or NLP | Integrated Gradients, token attributions, attention, SHAP for text | highlight the tokens that drove the decision. |
| LLM or RAG output | show the retrieved sources and the rationale; token logprobs for confidence | grounding is the explanation. |
| Time-series | feature and lag importance, temporal attention, SHAP on features | which past steps drove the forecast. |
Caption any saliency honestly: it shows where the model looked, not a ground-truth region.
Present the result well
- Show the calibrated probability or score and the operating threshold, not just a label.
- For an ensemble, serve the single best model for a coherent prediction and explanation; show the ensemble score as a second opinion.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 41 lines · 159 tokens per session scan A 6dbc0e0a16f7
deployment-explainability is a skill published in the GitHub repository mxslr/mlcraft (8 stars, last pushed 1mo ago), licensed MIT. It adds 159 tokens to every session and 828 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
albucore-benchmarks
Running Albucore micro-benchmarks under benchmarks/, synthetic router timings, and comparing PyPI releases with uv --no-project. Use when adding benchmarks, comparing performance across versions, or documenting benchmark workflow.
albucore-conventions
Albucore image processing conventions - shapes (H,W,C), dtypes (uint8/float32), benchmark-driven backend routing (OpenCV, NumPy, Torch CPU, LUT, NumKong), tests, and lockfile discipline. Use when implementing or modifying albucore modules, writing tests, or reviewing image-processing code.
performance-optimization
Systematic performance audit for Albucore runtime code. Use whenever implementing, reviewing, profiling, or optimizing atomic image operations, backend routing, reductions, label maps, LUTs, random generation, dtype conversions, allocation-heavy paths, batch or volume kernels, or in-place behavior.
torch-performance-optimization
Optimize or review eager CPU-only Albucore PyTorch runtime paths with benchmark-backed decisions. Use when adding or changing Torch CPU kernels, Tensor/NumPy bridges, Torch backend routing, tensor layouts, allocations, threading, profiling, memory-format candidates, or Torch performance benchmarks.
albucore-public-api
Albucore star-exported API (all), routers vs albucore.functions shims, and dependents such as Albumentations. Use when changing exports, documenting API, or deciding what belongs in package all.
ml-for-aec
Computer vision for buildings, image-to-floorplan, generative ML models, performance prediction, structural analysis ML, energy prediction, natural language to design, and point cloud ML for AEC computational design.