Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add commands/arise-ai-group/lead-management-plugin/prep-callgit clone --depth 1 https://github.com/Arise-AI-Group/lead-management-pluginWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/arise-ai-group/lead-management-plugin/prep-call)<a href="https://agentmods.dev/commands/arise-ai-group/lead-management-plugin/prep-call"><img src="https://agentmods.dev/badge/commands/arise-ai-group/lead-management-plugin/prep-call.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00000 | $0.00909 |
| Opus 5 | $0.00000 | $0.00454 |
| Sonnet 5 | $0.00000 | $0.00182 |
| Haiku 4.5 | $0.00000 | $0.00091 |
Grade A, and why
prep-call scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 125 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/prep-call
If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.
Prepare for an upcoming discovery or sales call with company research, attendee backgrounds, a suggested agenda, and discovery questions.
Usage
/prep-call
Then tell me who you're meeting with, or I'll check your calendar.
How It Works
┌─────────────────────────────────────────────────────────────────┐
│ PREP CALL │
├─────────────────────────────────────────────────────────────────┤
│ STANDALONE (always works) │
│ ✓ You tell me: company, meeting type, attendees │
│ ✓ Web research: company info, news, leadership │
│ ✓ Meeting brief with agenda and discovery questions │
│ ✓ Objection anticipation based on context │
│ ✓ Pre-meeting email sequence drafts │
├─────────────────────────────────────────────────────────────────┤
│ SUPERCHARGED (when you connect your tools) │
│ + ~~calendar: Auto-find meeting, pull attendees │
│ + ~~CRM: Lead history, prior interactions, score │
│ + ~~email: Send confirmation + questionnaire + reminder │
│ + ~~conversation intelligence: Pull prior call recordings │
└─────────────────────────────────────────────────────────────────┘
What I Need From You
Required:
- Company or contact name
- When the meeting is (or "tomorrow at 2pm")
Helpful but optional:
- Attendee names and titles
- What you know about them so far
- What you want to accomplish in the meeting
If calendar is connected: I'll find the meeting and pull attendees automatically.
Output
Meeting Brief
# Call Prep: [Company Name]
**Meeting:** [Type] — [Date] at [Time]
**Attendees:** [Names with titles]
**Your Goal:** [What you want to accomplish]
## Company Snapshot
| Field | Value |
|-------|-------|
| Company | [Name] |
| Industry | [Industry] |
| Size | [Employees] |
| What they do | [1-2 sentences] |
## Who You're Meeting
### [Name] — [Title]
- Background: [Career summary]
- Role in decision: [Decision maker / Influencer / Evaluator]
- Talking point: [Something to reference in conversation]
## What We Know
- Source: [How they found us]
- Score: [Hot / Warm / Cold]
- Key context: [Form message, prior interactions, referral info]
## Suggested Agenda
1. Open (5 min) — [Reference point]
2. Their world (10 min) — [What to explore]
3. Bottlenecks (10 min) — [Where to dig]
4. Quick wins (5 min) — [What to share]
5. Next steps (5 min) — [What to propose]
## Discovery Questions
1. [Tailored to their situation]
2. [Based on their industry]
3. [Focused on their likely pain points]
## Potential Objections
| Objection | Response |
|-----------|----------|
| [Likely concern] | [How to address it] |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago First seen · 125 lines · 0 tokens per session scan A c552eb690be7
prep-call is a command published in the GitHub repository Arise-AI-Group/lead-management-plugin (7 stars, last pushed 7mo ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 909 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other commands, from other repositories
git
Git operations with intelligent commit messages and workflow optimization.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.