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Agent skill

Meeting Processor

by glebis in glebis/claude-skills

This skill should be used when processing meeting transcripts to auto-detect meeting type (leadgen, partnership, coaching, internal) and extract type-specific structured analysis.

MITAuto-check passed

Install Meeting Processor

skills CLI
$ npx skills add glebis/claude-skills --skill meeting-processor -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install glebis/claude-skills meeting-processor --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/glebis/claude-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/meeting-processor .claude/skills/meeting-processor && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
meeting-processor
GitHub stars
389
Token cost
~1.6k tokens
SKILL.md length
622 words
Files
13 (incl. scripts)
Skills in repo
91
Repo updated
First seen
Licence
MIT

At a glance

This skill should be used when processing meeting transcripts to auto-detect meeting type (leadgen, partnership, coaching, internal) and extract type-specific structured analysis.

  • Works in 7 steps: Script analyzes transcript and detects… → Extracts structured data via LLM → Identifies missing/ambiguous fields → …
  • Process meeting
  • SKILL.md covers When to Use, Prerequisites, Supported Meeting Types and Usage, plus 2 more sections
  • Runs Python and Shell scripts from its folder; calls python3 and pip; needs CEREBRAS_API_KEY

What it does

Meeting Processor is an agent skill from glebis/claude-skills. This skill should be used when processing meeting transcripts to auto-detect meeting type (leadgen, partnership, coaching, internal) and extract type-specific structured analysis. Triggers on "process meeting", "analyze meeting", "meeting summary", or after syncing new Fathom/Granola transcripts.

Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 15 other files, including scripts (for example `.claude-plugin/plugin.json`, `README.md` and `process_interactive.sh`).

The repository describes itself as: Collection of Claude Code skills for enhanced AI workflows. The licence is MIT.

When your agent uses it

  • Process meeting
  • Analyze meeting
  • Meeting summary
  • After syncing new Fathom/Granola transcripts

Example prompts

  • “process meeting”
  • “analyze meeting”
  • “meeting summary”
  • “/meeting-processor”

Requirements

  • Python 3
  • A Bash shell
  • A credential in CEREBRAS_API_KEY

Workflow steps

7 steps, taken from the first numbered list in SKILL.md.

  1. Script analyzes transcript and detects meeting type
  2. Extracts structured data via LLM
  3. Identifies missing/ambiguous fields
  4. Returns questions as JSON (exit code 2 signals interaction needed)
  5. Parse the JSON between INTERACTIVE_QUESTIONS markers
  6. Use AskUserQuestion to collect answers for each question
  7. Save answers to a temp JSON file and re-run with process_with_answers.py

What it can do on your machine

Read from SKILL.md and the folder at commit 7524dff. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 8 files in scripts/ (Python and Shell), which the agent can run.

    Shell commands in SKILL.md call:

    • python3
    • pip

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use pip, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • CEREBRAS_API_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Meeting Processor loads about 1.6k tokens when it runs. Until then it costs about 79 tokens; SKILL.md has 622 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~79
When it runs · the whole SKILL.md, loaded when a task matches
~1.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from glebis/claude-skills at commit 7524dff, republished under its MIT licence (© glebis). 622 words, ~1,556 tokens.

Download SKILL.mdSave it as .claude/skills/meeting-processor/SKILL.md (or your agent's skills folder). This skill also uses 12 other files; get the full folder from GitHub.
name
meeting-processor
description
This skill should be used when processing meeting transcripts to auto-detect meeting type (leadgen, partnership, coaching, internal) and extract type-specific structured analysis. Triggers on "process meeting", "analyze meeting", "meeting summary", or after syncing new Fathom/Granola transcripts.

Meeting Processor

Intelligent meeting transcript processor that auto-detects meeting type and applies type-specific extraction with optional interactive clarification.

When to Use

  • After syncing Fathom or Granola transcripts (/fathom --today, /granola export)
  • When asked to process, analyze, or summarize a meeting transcript
  • When a new meeting transcript appears in the vault root matching YYYYMMDD-*.md
  • For coaching sessions, delegate to coaching-session-summarizer skill instead

Prerequisites

bash
pip install openai pyyaml

Requires CEREBRAS_API_KEY environment variable (uses Cerebras API with llama-3.3-70b).

Supported Meeting Types

TypeDescriptionKey Extractions
leadgenSales/business development callsCommitments, pain points, budget, timeline, decision makers, deal stage, sentiment
partnershipCollaboration/partnership explorationOpportunity overview, value proposition, strategic alignment, technical needs, fit assessment
coachingCoaching/mentoring sessionsInsights, decisions, action items, themes, emotional arc, techniques, session quality
internalInternal team meetingsComing soon

Usage

Interactive Mode (default)

Run the processor, which auto-detects meeting type and asks clarifying questions:

bash
python3 ~/.claude/skills/meeting-processor/scripts/process.py <transcript-file> --mode interactive

Interactive flow:

  1. Script analyzes transcript and detects meeting type
  2. Extracts structured data via LLM
  3. Identifies missing/ambiguous fields
  4. Returns questions as JSON (exit code 2 signals interaction needed)
  5. Parse the JSON between __INTERACTIVE_QUESTIONS__ markers
  6. Use AskUserQuestion to collect answers for each question
  7. Save answers to a temp JSON file and re-run with process_with_answers.py

Handling interactive questions:

When the script exits with code 2, parse the output for questions JSON. Each question has:

  • question: The question text
  • header: Short label (used as answer key)
  • options: Array of {label, description} for AskUserQuestion

After collecting answers, create two temp files:

  • questions.json — the original questions context (includes partial_data, meeting_type, transcript_file)
  • answers.json — map of {header_lowercase: selected_label}

Then run:

bash
python3 ~/.claude/skills/meeting-processor/scripts/process_with_answers.py questions.json answers.json
Batch Mode

Extract only high-confidence information without user interaction:

bash
python3 ~/.claude/skills/meeting-processor/scripts/process.py <transcript-file> --mode batch
Force Meeting Type

Skip auto-detection:

bash
python3 ~/.claude/skills/meeting-processor/scripts/process.py <transcript-file> --type leadgen
python3 ~/.claude/skills/meeting-processor/scripts/process.py <transcript-file> --type partnership

Output

Analysis is appended to the transcript file as a ## Meeting Analysis section. Frontmatter is updated with meeting_type, processed_date, and processing_mode.

Leadgen Output Structure
  • Commitments & Actions — with deadlines and owners
  • Follow-up — next meeting date if scheduled
  • Client Context — pain points, budget, timeline, decision makers
  • Deal Assessment — stage (cold/warm/hot), probability (1-5), blocker, sentiment
Partnership Output Structure
  • Opportunity — description and value proposition for both sides
  • Commitments & Actions — with deadlines and owners
  • Follow-up — next meeting date if scheduled
  • Partnership Context — strategic alignment, technical needs, resources, challenges
  • Opportunity Assessment — fit (strong/medium/weak), readiness, success factors, sentiment
Show full SKILL.md (267 more words)Show less

After the meeting analysis is complete (Step 1), automatically link any matching meeting-prep notes to the session note. This replaces the need to manually run /meeting-prep link.

How It Works

  1. Derive the meetings directory from the processed session note's parent directory (do not hardcode paths).

  2. Extract session metadata from the processed note:

    • date from frontmatter (YYYYMMDD format)
    • participants from frontmatter (list of names)
    • If no participants field, extract names from the transcript header or attendee list

  3. Search for matching prep notes:

    bash
    find <MEETINGS_DIR> -name "YYYYMMDD-prep-*" -type f 2>/dev/null

    Where YYYYMMDD is the session date.

  4. Validate the match: For each candidate prep note, read its frontmatter and confirm:

    • The date field matches the session date
    • The participant field matches one of the session's participants (fuzzy: check both full name and first name, case-insensitive)
    • The session_note field is empty ("") — skip already-linked prep notes

  5. Update both files when a match is found:

    In the prep note:

    • Set session_note: "[[session-note-filename]]" (without .md extension)
    • Set status: done

    In the session note:

    • If a ## See also section exists, add - [[YYYYMMDD-prep-participant-slug]] to it
    • Otherwise, append a new section at the end:
      markdown
      ## Prep Note
- [[YYYYMMDD-prep-participant-slug]]
    • Never create duplicate links — check if the link already exists before adding

  6. Report in the processing output which prep notes were linked, skipped, or not found.

Rules
  • Derive MEETINGS_DIR from the session note path, not from hardcoded values
  • If the meeting-prep config.yaml is available, read prep_notes.prefix (default: prep) and prep_notes.type_tag (default: meeting-prep)
  • This step is non-blocking: if it fails or finds no prep notes, processing still succeeds

© glebis, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 12 other files (scripts) in meeting-processor of glebis/claude-skills.

  • SKILL.md
  • .claude-plugin/plugin.json
  • README.md
  • process_interactive.sh
  • scripts/detectors.py
  • scripts/extractors/__init__.py
  • scripts/extractors/coaching.py
  • scripts/extractors/leadgen.py
  • scripts/extractors/partnership.py
  • scripts/interactive.py
  • scripts/process.py
  • scripts/process_with_answers.py
  • skill.json

Open the folder on GitHubat commit 7524dff

Compare with similar skills

Meeting Processor next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Meeting Processor compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Meeting Processor this skillglebis/claude-skills389—~1.6kAutomated safety check: PassMIT
Meetingsalirezarezvani/claude-skills28k—~1.5kAutomated safety check: PassMIT
Meeting Distiller Prosickn33/agentic-awesome-skills47k1 repos~1kAutomated safety check: PassMIT
Meeting Ingestiongarrytan/gbrain31k—~7.7kAutomated safety check: PassMIT
Meeting Transcripthuytieu/COG-second-brain1.3k1 repos~2.3kAutomated safety check: PassMIT
Meeting Notes And Actionscomposio-community/awesome-codex-skills17k—~357Automated safety check: PassNone

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Questions about Meeting Processor

What does Meeting Processor do?

This skill should be used when processing meeting transcripts to auto-detect meeting type (leadgen, partnership, coaching, internal) and extract type-specific structured analysis. Meeting Processor is an agent skill from glebis/claude-skills. This skill should be used when processing meeting transcripts to auto-detect meeting type (leadgen, partnership, coaching, internal) and extract type-specific structured analysis.

When should I use Meeting Processor?

Meeting Processor fits situations like: process meeting; analyze meeting; meeting summary; after syncing new Fathom/Granola transcripts.

How do I install Meeting Processor in Claude Code?

Run `npx skills add glebis/claude-skills --skill meeting-processor -a claude-code`. Or copy the skill folder (meeting-processor in glebis/claude-skills) into .claude/skills/meeting-processor in your project. Claude Code loads it when a task matches its description.

How do I install Meeting Processor in Codex?

Run `npx skills add glebis/claude-skills --skill meeting-processor -a codex`. Or copy the skill folder (meeting-processor in glebis/claude-skills) into .agents/skills/meeting-processor in your project. Codex loads it when a task matches its description.

Can I use Meeting Processor in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add glebis/claude-skills --skill meeting-processor -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/meeting-processor, .gemini/skills/meeting-processor, .github/skills/meeting-processor and .opencode/skills/meeting-processor in your project.

What does Meeting Processor need to run?

Going by SKILL.md and its folder, Meeting Processor needs Python and a shell for the scripts in its folder, the command-line tools its instructions call (python3 and pip) and credentials named CEREBRAS_API_KEY. Our summary lists: Python 3; A Bash shell; A credential in CEREBRAS_API_KEY.

Does Meeting Processor access the network?

SKILL.md contains no URLs. Its commands use pip, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Meeting Processor safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Meeting Processor use?

Meeting Processor is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Meeting Processor use?

About 1.6k tokens (SKILL.md is roughly 6.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Meeting Processor?

Skills that share tags, products or a category with Meeting Processor: Meetings (alirezarezvani/claude-skills, 28k stars), Meeting Distiller Pro (sickn33/agentic-awesome-skills, 47k stars), Meeting Ingestion (garrytan/gbrain, 31k stars) and Meeting Transcript (huytieu/COG-second-brain, 1.3k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Meeting Processor?

glebis (a GitHub user) maintains it in glebis/claude-skills, which has 389 GitHub stars. The repository holds 91 skills in this directory. The repository was last updated on September 26, 2026.

Source: glebis/claude-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.