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Transcript Analyzer

Listed
glebis·vlatest·writing·MIT

This tool swiftly analyzes transcripts to identify key topics, sentiment, and recurring themes for deeper insights and efficient research.

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 glebis/claude-skills --skill transcript-analyzer -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

glebis

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 failed 1 of 6 checks. A failure is not proof of bad intent; it means something in the code needs a human to look at it before you run it.

  • No undeclared network calls: failed

    Contacts 1 host that is neither well-known infrastructure nor declared in the skill's frontmatter: api.cerebras.ai.

    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.

    Hosts contacted

    api.cerebras.aiundeclared

    What it found

    • transcript-analyzer/scripts/src/lib/extract-service.ts:9contacts api.cerebras.ai
      baseURL: 'https://api.cerebras.ai/v1'

  • 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 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 glebis/claude-skills at 7524dff0c5.

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

Commit
7524dff0c5
Manifest
transcript-analyzer/SKILL.md
Scanned
12 files, 1,351 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

Transcript Analyzer

What it does

This tool processes audio or video transcripts to extract structured data, identify speakers, and summarize key topics. It converts unstructured conversation logs into organized insights suitable for analysis.

When to use it

  • Meeting Summaries: Quickly generate action items and decisions from recorded team stand-ups or client calls.
  • Interview Analysis: Extract candidate responses and sentiment trends from job interview recordings.
  • Podcast Research: Identify recurring themes, guest mentions, and time-coded segments for show notes.
  • Legal Discovery: Organize witness statements or deposition transcripts by speaker and topic.

Key capabilities

  • Speaker diarization to distinguish between different participants in a conversation.
  • Topic modeling to categorize discussion points automatically.
  • Text extraction from raw audio or video file metadata.
  • Summarization of long-form dialogue into concise bullet points.

Example prompts

  • "Analyze this meeting transcript and list all action items assigned to specific team members."
  • "Identify the three most discussed topics in this interview and provide a brief summary for each."
  • "Extract every mention of 'budget' from this podcast transcript along with the speaker's name and timestamp."

Tips & gotchas

  • Ensure your source transcripts are clean; heavy background noise or poor audio quality can reduce speaker identification accuracy.
  • For best results, provide context about the conversation type (e.g., "formal interview" vs. "casual chat") to help the analyzer tailor its summaries.