Clay
v1.0.0Run data quality checks on PMU (Phasor Measurement Unit) data. Use when the user asks to validate, check, or audit PMU measurements including frequency, volt...
⭐ 0· 95·0 current·0 all-time
byJiahui (Clay) Yang@clayutk
MIT-0
Download zip
LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
OpenClaw
Benign
high confidencePurpose & Capability
Name/description match the included files: SKILL.md, a sample CSV, a limits config, and a Python script that implements frequency, voltage, phasor angle, missing-data, and timestamp-gap checks. All requested actions (reading a CSV, applying configurable limits, producing reports) are appropriate for a PMU data quality tool.
Instruction Scope
Runtime instructions restrict the skill to asking for a PMU CSV, checking columns, running the provided Python script, and reporting local outputs. The SKILL.md does not instruct the agent to read unrelated system files, environment variables, or send data to external endpoints.
Install Mechanism
This is an instruction-only skill with an included Python script (no install spec). The script imports pandas and numpy but the skill does not declare these dependencies or require 'python' as a runtime binary; users should ensure a compatible Python environment with required packages is available before running.
Credentials
The skill requests no environment variables, credentials, or config paths. All file access is limited to user-supplied CSVs, the included templates, and locally produced reports, which is proportionate to the stated purpose.
Persistence & Privilege
always is false and the skill does not request persistent or elevated privileges. It does write output files (flagged CSV and optional HTML) alongside input files, which is expected for a reporting tool.
Assessment
This skill appears to be a straightforward local PMU CSV quality checker. Before installing/running: 1) Review the full script if you need to be certain it won't access other files or networks (the visible code imports pandas/numpy and writes local reports). 2) Run it in an environment with Python and the required libraries (pandas, numpy) installed or in a sandbox if the data is sensitive. 3) Be aware it reads whatever CSV path you provide and writes flagged output next to it — avoid pointing it at directories containing unrelated sensitive files. 4) If you plan to use it in production, confirm the limits_config.json matches your system and review any further code not shown to ensure no unexpected network or subprocess calls.Like a lobster shell, security has layers — review code before you run it.
latestvk97dfvkz34y8srcexnbkey0qdh83jyrv
License
MIT-0
Free to use, modify, and redistribute. No attribution required.