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Performance Analytics

Checked
groeimetai·vlatest·data·Apache-2.0

Helps with performance optimization, analytics as part of data analysis and analytics workflows workflows.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add serac-labs/serac --skill performance-analytics -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

groeimetai

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned groeimetai/snow-flow at af3ad2b3c0.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
af3ad2b3c0
Manifest
packages/skills/performance-analytics/SKILL.md
Scanned
1 file, 376 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

Performance Analytics

What it does

This skill enables AI agents to generate comprehensive performance analytics for Snowflake data environments. It allows agents to query system metrics, analyze resource utilization, and produce detailed reports on database efficiency.

When to use it

  • Resource Optimization: Identify underutilized or overburdened clusters to optimize cloud costs.
  • Bottleneck Detection: Analyze query execution times to find and resolve performance bottlenecks in data pipelines.
  • Capacity Planning: Review historical usage trends to forecast future storage and compute requirements.
  • Operational Auditing: Generate regular compliance reports detailing system health and security posture.

Key capabilities

  • Query Snowflake system tables for real-time metrics.
  • Analyze resource consumption patterns across warehouses.
  • Generate structured performance reports in various formats.
  • Monitor query execution statistics and wait events.

Example prompts

  • "Analyze the last 24 hours of warehouse usage and highlight any periods of excessive compute cost."
  • "Generate a report on slow-running queries that exceeded the 5-minute threshold this week."
  • "Summarize current storage utilization trends and predict when we will hit our capacity limit."

Tips & gotchas

Ensure your AI agent has appropriate SYSADMIN or MONITOR roles to access system tables required for deep analytics. Limit historical data ranges in prompts to prevent overwhelming the token context window with massive datasets.