Retention Drop Checker
v1.0.1Diagnose why short-video retention drops and suggest practical fixes. Use when views start but audience leaves early.
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byLeroyCreates@leooooooow
MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
OpenClaw
Benign
high confidencePurpose & Capability
The name and description (diagnosing short-video retention drops and suggesting fixes) match the runtime instructions: ask for retention curves/transcripts, perform qualitative diagnosis, and optionally emit a Python analysis script when structured CSV data is provided. There are no unrelated credential or binary requirements.
Instruction Scope
Instructions stay on-topic (clarify inputs, segment video, diagnose drops, recommend fixes, produce a reusable Python script if the user supplies structured data). One point to note: the skill expects users may upload retention CSVs—while the skill only says to generate analysis code (not to execute or transmit data), uploading analytics can include sensitive or proprietary data; the SKILL.md does not instruct where code should be run or how uploaded data is stored or transmitted.
Install Mechanism
No install spec and no code files beyond SKILL.md and a harmless output template; this is lowest-risk (instruction-only) from an installation perspective.
Credentials
The skill requests no environment variables, credentials, or config paths—there is no disproportionate secret or environment access requested.
Persistence & Privilege
The skill is not always-on, is user-invocable only, and does not request or describe modifying other skills or system-wide settings.
Assessment
This skill appears coherent and low-risk: it only needs the content you choose to provide (script/transcript, retention curve, or CSV) and will produce diagnostics and optional Python analysis code. Before uploading any retention exports or CSVs, confirm they contain no sensitive or proprietary identifiers (user IDs, PII, or private project data). If the skill returns a Python script, review the code before running it in your environment; run analysis locally or in a trusted environment rather than sending raw analytics to unknown endpoints. Note the license: CC BY-NC-SA for non-commercial use—contact the author for commercial licensing.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.