AKM Fashion
v1.0.2AKM implementation for wardrobe and outfit decision workflows. Models body context, scenes, wardrobe assets, and functional constraints before outputting sty...
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by@sirsws
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, and included files all focus on wardrobe profiling and decision workflows. There are no unrelated environment variables, binaries, or config paths requested.
Instruction Scope
Runtime instructions are limited to loading the included prompt/record files (ELICITATION_PROMPT.md, RECORD_TEMPLATE.md, EXECUTION_PROMPT.md, SAMPLE_RECORD.md) and producing structured outputs. The prompts explicitly forbid assuming wardrobe inventory and require surfacing MissingInputs. No instructions reference system files, secrets, or external endpoints beyond public GitHub/SSRN links in documentation.
Install Mechanism
The registry entry contains no formal install spec (it's instruction-only), but SKILL.md documents installing from a public GitHub repo via 'npx skills add https://github.com/sirsws/akm'. That is a normal publish/install workflow, but it means installing will fetch external code from GitHub — review the remote repo before running installs.
Credentials
The skill requests no environment variables, credentials, or config paths. The data it asks users to provide (body shape, wardrobe assets, preferences) is personal but expected for the stated function; no unexplained secrets are requested.
Persistence & Privilege
Flags are default (always:false, user-invocable:true, model invocation allowed). The skill does not request persistent system privileges or attempt to modify other skills or global config in the provided files.
Assessment
This skill appears internally consistent for wardrobe and outfit decision workflows. Before installing or providing personal data: 1) if you use the documented 'npx skills add https://github.com/sirsws/akm' command, understand that code will be fetched from the external GitHub repo — inspect that repo for unexpected scripts before running installs; 2) the skill will ask for detailed body and wardrobe information (sensitive personal data). Provide only what you are comfortable sharing and avoid including personally identifying text or images unless you trust the runtime environment; 3) because the registry package is instruction-only here, the runtime behavior is just prompt-driven logic — still confirm that the installed repository/version matches the listing and has no additional code that could run; 4) if you need lower risk, use the skill interactively (copy the prompts locally) rather than installing external code. Overall there are no red flags in the provided files, but follow normal caution when fetching external repositories and sharing personal details.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.