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Tldr Stats

Checked
parcadei·vlatest·data·MIT

Tldr Stats summarizes key statistics from lengthy texts, quickly providing concise insights and saving valuable reading time.

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 parcadei/continuous-claude-v3 --skill tldr-stats -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

parcadei

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 parcadei/continuous-claude-v3 at d07ff4b06b.

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

Commit
d07ff4b06b
Manifest
.claude/skills/tldr-stats/SKILL.md
Scanned
1 file, 82 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

What it does

The tldr-stats skill provides concise statistical summaries of data. It can analyze text and extract key metrics, offering a simplified view of complex information. This allows users to quickly grasp trends and insights without needing to delve into raw datasets or lengthy reports.

When to use it

  • Summarize customer feedback from reviews or surveys to identify common themes and sentiment.
  • Analyze news articles related to a specific topic to understand the frequency of keywords or entities mentioned.
  • Get a quick overview of website traffic data, highlighting key metrics like page views and bounce rate.
  • Process research papers to extract important findings and statistical significance.

Key capabilities

  • Statistical analysis
  • Text summarization
  • Data extraction
  • Trend identification

Example prompts

  • "Summarize the sentiment in these customer reviews: [paste reviews here]"
  • "What are the key statistics from this article about climate change? [paste article text]"
  • “Give me a quick overview of website traffic for last month.”

Tips & gotchas

The quality of the statistical summary depends on the clarity and structure of the input data. Providing well-formatted text will yield more accurate and useful results.