Recommend
v1.0.0Context-aware recommendations. Learns preferences, researches options, anticipates expectations.
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byIván@ivangdavila
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 align with the runtime instructions: gathering user context, extracting preferences, researching candidates, ranking, and storing outcomes. Required capabilities (reading memory and conversation history) are consistent with a personalization/recommendation skill.
Instruction Scope
SKILL.md instructs the agent to search memory/*.md, MEMORY.md, conversation history, and behavioral signals and then perform web-style research and shortlist options. This is coherent for recommendations but gives the skill broad read access to agent memory and conversation history and allows outbound research (unspecified endpoints). The instructions also tell the agent to 'store learnings in memory' so it will persist data.
Install Mechanism
Instruction-only skill with no install spec and no code files — minimal installation risk (nothing is written to disk by an installer).
Credentials
No environment variables, credentials, or external config paths are requested. The only resources used are agent-internal (memory, conversation history) which are appropriate for personalization.
Persistence & Privilege
The skill explicitly instructs the agent to store outcomes and update preference memory. It does not request always:true and is not force-included, but it will persist data in the agent's memory if invoked — consider this when sensitive information exists in memory.
Assessment
This skill appears coherent and does what it claims, but it will read your conversation history and memory files and write preference updates there. Before installing, review your memory (memory/*.md, MEMORY.md) for any sensitive items you don't want a recommendation skill to read or store (API keys, passwords, private notes). If you prefer, disable autonomous invocation for the agent or configure the agent's memory policies so the skill only stores non-sensitive preference signals. Finally, ask how the skill performs external research (which sites/APIs it uses) if you want tighter network/privacy controls.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.