executing-plans
executing-plans
Use when executing an implementation plan in the current session as the implementer yourself — your human partner chose inline execution, or no subagent tool is available
model-and-backend-selectionSelect and troubleshoot DeepFace models, detector backends, optional dependencies, model weights, TensorFlow/Keras compatibility, normalization, and encryption.
Independent GitHub snapshot · Automated classification · Original documentation governs
Use this sub-skill when the user task needs Supported recognition, demography, spoofing, and detector names; DeepFace.build_model; base versus optional dependencies; TensorFlow/Keras/tf-keras; weight cache; CPU/GPU; normalization and encrypted embeddings.
Detailed task workflows route to sibling sub-skills; database clients and services go to ../datastore-search/SKILL.md; REST startup goes to ../api-service/SKILL.md.
from deepface import DeepFace works; otherwise use ../../references/troubleshooting.md.references/api-reference.md documents signatures, parameters, outputs, and API-specific behavior for this sub-skill.references/workflows.md or the focused workflow reference contains practical recipes and decision patterns.references/troubleshooting.md maps common errors to concrete recovery steps.scripts/inspect_deepface_models.py is the safe bundled helper for this sub-skill.Do not tell future agents to open or run original repository tests, examples, notebooks, scripts, or local checkout files. Use this self-contained sub-skill, its references, and its bundled helper scripts instead.