Meeting Prep logo

Meeting Prep

Organization
explorium-ai
meeting-prep

Meeting prep skill for Claude Code and Codex: generate a 30-minute pre-call brief for any target account with headline signals, attendee profiles, ranked talking points, discovery questions, and what NOT to do. Pulls real-time firmographics, hiring signals, technographics, funding, and business events for each prospect. Use for account research before sales calls, QBR preparation, and renewal meetings. Triggers on 'prep me for a meeting with', 'build a call brief for', 'I have a call with', 'pre-meeting research on'. Works in Claude Code, Codex, Hermes-Agent, and Claude Cowork.

Overview

Publisherexplorium-ai
Repositorygtm-skills
Skill namemeeting-prep
Stars
293
Forks
3
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by explorium-ai on GitHub. Read the source before you install it.

Installation

Install the Meeting Prep AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/explorium-ai/gtm-skills/tree/main/skills/meeting-prep
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/explorium-ai/gtm-skills.git /tmp/gtm-skills
mkdir -p .claude/skills
cp -r /tmp/gtm-skills/skills/meeting-prep .claude/skills/meeting-prep
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Meeting Prep in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Meeting Prep on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Meeting Prep is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Meeting Prep

Pull the company, signal, and attendee evidence the calling agent needs to write a tight, decision-ready brief for a ~30-minute meeting.

Input

  • Account identifier (required): an Explorium business_id (preferred), or a company name or domain.
  • Attendees (recommended): names and/or emails. Without attendees the brief is necessarily generic.
  • Meeting context (strongly recommended): free-form description of purpose, stakes, prior discussions, deal stage, walk-out goals, known tensions, competitive context, the offering in play, hypotheses to test, and pitfalls. Flat enums produce flat briefs.

If the identifier is ambiguous: a string with a dot or known TLD is a domain; otherwise treat it as a name.

Workflow

Parallelize aggressively. Company enrichment fans out from the resolved account; per attendee, resolve and enrich profile evidence in parallel.

  1. Anchor on purpose. Restate the meeting context in 1-2 sentences. If missing, ask once; if the user declines, default to general prep and state that assumption. Derive purpose (1 line), desired outcome, 3-5 priority topics, named risks or hypotheses. These drive insight focus, attendee framing, talking-point ranking, and the suggested agenda.
  2. Match the business. If a business_id was supplied, use it directly. Otherwise resolve by name or domain. If no confident match, surface the ambiguity rather than guessing. Domain-variant sanity check: if firmographics show a major brand but headcount is 1-50 and the entity looks like a registered-agent shell, retry with the alternate domain (.so vs .com) or the company-name string.
  3. Enrich the business broadly: firmographics and technographics, funding and acquisitions, strategic insights and stated challenges, hiring direction and workforce trends, competitive landscape and company hierarchies, recent LinkedIn posts, website changes. Gather broadly now; decide relevance during synthesis.
  4. Fetch business events on a 30-90 day window. Treat events as raw signal to triage in step 6. Event-attribution sanity check: tie every event to the matched business; do not blend across the parent / subsidiary tree silently.
  5. Match and enrich attendees in parallel. Resolve each by email when available, otherwise by full name plus the resolved company. If an attendee cannot be resolved, note it; do not fabricate. Enrich each resolved person for profile (seniority, department, tenure, prior roles). Pull contacts only when the user intends to send outreach: default email-only (cheaper), switch to email + phone only when phone is required (SDR dialer flows). Per-prospect post history is a current gap; pull the employer's recent posts for voice instead.
  6. Hand back raw evidence for synthesis. The calling agent writes the narrative brief. Organize so the calling agent can: triage events and posts (keep items tied to purpose, deal stage, attendees, priority topics; drop generic press); classify per-attendee posture from employment history and recent voice (Cold, Warm-but-dormant with significant past tenure at a relevant prior employer, Active, Hostile); rank 3-5 talking points by relevance to the desired outcome, each tied to a specific surfaced fact and ideally a named attendee; mark each named hypothesis confirmed, contradicted, or unresolved; tag claims by source category; flag any past-dated milestone for verification rather than dropping silently.
  7. Write the TL;DR last, framed by the meeting purpose and desired outcome.

Output Format

  • TL;DR: meeting purpose / desired outcome in one line (or "general meeting prep (no context supplied)" if defaulted); top 3 to know walking in, each tied to the outcome; hypothesis check one line each (confirmed / contradicted / unresolved); "Open with" a single recommended opening line, verbatim, calibrated to attendee posture.
  • Company Snapshot: industry, size bucket, revenue range, HQ, website, business model. One paragraph weaving in strategic priorities and current challenges.
  • Signals Worth Raising: grouped by category (funding and M&A, hiring direction, tech stack overlap or gaps, recent business events, voice on LinkedIn or site changes, competitive context, parent / subsidiary). For any past date inline: "Verify; date is in the past."
  • Attendees: per attendee, name and title, department (fallback "Unattributed" when null), canonical seniority display (c-suite, vice president, director, manager), email, phone, LinkedIn, time in role, posture (Cold / Warm-but-dormant / Active / Hostile). "Why they matter for THIS meeting": 2 lines max. Talking points for them: 1-2 specific. If profile data is thin: "Profile data restricted; verify externally."
  • Talking Points (3-5): ranked by impact, tagged for audience, each referencing a supporting data point with its source category tag. Prioritize items that show homework, connect to the offering, surface competitive angles, or hit a specific persona.
  • Discovery Questions (3-5): specific, anchored in concrete facts. Tag each for an attendee or the group.
  • Suggested Agenda (~30 min): (0-5) Open with the calibrated line; (5-15) Explore with the top talking point and 2 questions; (15-25) Develop with the secondary point, demo, or proposal; (25-30) Close on the specific desired next step.
  • What NOT to Do: 1-3 specific failure modes for THIS meeting. Concrete, not generic.

Limitations

  • Strategic insights and stated challenges are sourced from SEC 10-K filings: for private companies these will be all-null, and for public companies they can be 12-18 months stale. Use business events, funding, workforce trends, and recent posts for current-state signals.
  • Business event fetching supports a date floor but no upper bound; window the upper edge client-side.
  • Company size and revenue are bucketed. State buckets verbatim; do not interpolate exact figures.
  • No native CRM relationship history or deal-stage data. Posture must be inferred from profile history and post voice, plus user-supplied context.
  • No executive-move event enum. New-hire / departure signals must be inferred from workforce trends and profile changes, not fetched as a typed event.
  • Past-dated milestones must be verified externally; treat them as prompts to check, not ground truth.
  • Attendees who cannot be matched should be listed as unresolved. Do not synthesize their roles.
  • Department is null for many cross-functional senior roles (Chief X Officer, President, Founder). Group these under "Unattributed".

Frequently asked questions

What does the Meeting Prep AI skill do?

Meeting prep skill for Claude Code and Codex: generate a 30-minute pre-call brief for any target account with headline signals, attendee profiles, ranked talking points, discovery questions, and what NOT to do. Pulls real-time firmographics, hiring signals, technographics, funding, and business events for each prospect. Use for account research before sales calls, QBR preparation, and renewal meetings. Triggers on 'prep me for a meeting with', 'build a call brief for', 'I have a call with', 'pre-meeting research on'. Works in Claude Code, Codex, Hermes-Agent, and Claude Cowork.

Why use Meeting Prep on TypingMind?

Because you install it once and use it with any model. Meeting Prep is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Meeting Prep in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/explorium-ai/gtm-skills/tree/main/skills/meeting-prep. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Meeting Prep?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Meeting Prep?

As many as you like. As long as a model supports skills, you can use Meeting Prep with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Meeting Prep AI skill free?

Yes. It is published on GitHub by explorium-ai under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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