摄影照片评分Aesthetic Scorer
v1.4.9给你的照片打分、评价反馈、给出改进建议或美学分析 / Aesthetic photo scorer with detailed analysis
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by@kooui
MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
OpenClaw
Benign
high confidencePurpose & Capability
Name/description match the included code: two local models (Improved Aesthetic Predictor using CLIP+MLP and NIMA MobileNet) are used to compute a combined score. Declared dependencies (PyTorch, transformers, TensorFlow/Keras, Pillow, numpy) are appropriate for the stated models. Environment variables in config.json (model directory overrides) are proportional to the need to locate local model files.
Instruction Scope
SKILL.md instructs the agent to always generate a very large ('Level 10' ~4000-word) detailed evaluation in the background for every requested image and to 'save' it for immediate retrieval. The code files do not implement text-generation or a save/load mechanism for those evaluations—the large-text generation/saving would be done by the agent runtime per the SKILL.md instructions. This is not inherently malicious, but it is a scope/persistence concern: it implies extra compute, disk usage, and persistent storage of possibly sensitive images/analysis without specifying where or how long data is stored. Also there is a minor mismatch between README (which suggests automatic downloads on first use) and the scripts that force transformers to local_files_only and error if model files are missing; you must provision model files locally before use.
Install Mechanism
There is no formal install spec in the registry entry (instruction-only install). The package contains requirements.txt and README instructions to pip-install heavy ML packages and clone model repos. That is expected given the model-based functionality, but it means you must manually install large dependencies and place model weight files in the expected local paths (defaults use Windows-style F:\ paths). No remote, untrusted download URLs or obfuscated installers were found in the package.
Credentials
The skill does not request any credentials, tokens, or unexpected environment variables. The only env variables referenced are optional overrides for local model directories (AESTHETIC_SCORER_MODEL_DIR and AESTHETIC_SCORER_NIMA_DIR), which are reasonable and proportional.
Persistence & Privilege
always:false and autonomous invocation are normal. The SKILL.md's instruction to 'ALWAYS generate' and 'save' the detailed evaluation implies persistent storage of generated evaluations (and potentially intermediate outputs). The code itself does not modify other skills or global agent settings. You should confirm where the agent will store the saved detailed evaluations and for how long, since that affects privacy and disk usage.
Assessment
This skill appears to do what it says: offline, local aesthetic scoring with two local models. Before installing, consider these practical points: (1) You must install large ML dependencies (PyTorch, TensorFlow) and provide the model weight files locally—scripts expect local model files and will error if they're missing. (2) The defaults use Windows F:\ paths; set the environment variables (AESTHETIC_SCORER_MODEL_DIR, AESTHETIC_SCORER_NIMA_DIR) to where you store models on your machine. (3) SKILL.md requires the agent to always generate and 'save' very large detailed reports in the background—ask where the agent will store these files and for how long (privacy/disk-space consideration). (4) The skill does not request credentials or perform network exfiltration per the provided code, but installing/using it requires trust in the model weights you obtain (ensure you download from reputable sources). If you need the skill to be strictly non-persistent, request or implement an explicit save location and retention policy before enabling it.Like a lobster shell, security has layers — review code before you run it.
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License
MIT-0
Free to use, modify, and redistribute. No attribution required.