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 skills add aAAaqwq/AGI-Super-Team --skill call-prepgit clone --depth 1 https://github.com/aAAaqwq/AGI-Super-TeamWrote 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/skills/aaaaqwq/agi-super-team/call-prep)<a href="https://agentmods.dev/skills/aaaaqwq/agi-super-team/call-prep"><img src="https://agentmods.dev/badge/skills/aaaaqwq/agi-super-team/call-prep/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/aaaaqwq/agi-super-team/call-prep"><img src="https://agentmods.dev/badge/skills/aaaaqwq/agi-super-team/call-prep.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 5.1 | $0.00000 | $0.01716 |
| Opus 5 | $0.00000 | $0.00858 |
| Sonnet 5 | $0.00000 | $0.00343 |
| Haiku 4.5 | $0.00000 | $0.00172 |
Grade A, and why
call-prep 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 3d 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 — 233 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Call Prep
Preparation for a call with a client/lead: research, CRM update, conversation plan, PDF
When to use
- "prepare for a call with X"
- "call prep for Y"
- "gather info before a meeting"
- "conversation plan with a client"
- There is a scheduled call in the calendar or task
Dependencies
- CRM data:
query-leads - Web search: WebSearch, WebFetch
- PDF:
weasyprint(Python)
Paths
| What | Path |
|---|---|
| CRM Companies | $CRM_PATH/contacts/companies.csv |
| CRM People | $CRM_PATH/contacts/people.csv |
| CRM Leads | $CRM_PATH/relationships/leads.csv |
| CRM Activities | $CRM_PATH/activities.csv |
| PM Tasks | $PM_PATH/pm_tasks_master.csv |
| Output PDF | $PROJECT_ROOT/docs/{slug}-call-prep.pdf |
How to execute
Step 1: Gather internal data
Read everything from CRM about this person/company:
1. companies.csv -- company record
2. people.csv -- person record + notes
3. leads.csv -- lead stage, priority, next_action, notes
4. activities.csv -- communication history (emails, calls, messages)
5. pm_tasks_master.csv -- related tasks
Important: gather ALL interaction history -- not just the latest entry.
Step 2: External research
Run in parallel:
1. WebSearch: "{name} {company}" -- general info
2. WebSearch: "{name} linkedin founder" -- career, track record
3. WebFetch: company website -- products, positioning, pricing
4. WebSearch: "{company} 2025 2026" -- latest news
5. WebFetch: LinkedIn profile (if URL exists in CRM)
What to look for:
- Who is this person (track record, previous companies, expertise)
- What the company does (product, business model, stage)
- Company size, funding, revenue
- Latest news, hiring, pivots
- Potential pain points (from job postings, posts, comments)
Step 3: Update CRM
Based on research, update:
companies.csv-- description, industry, sizepeople.csv-- role, notes from researchleads.csv-- notes
Use skill update-lead.
Step 3.5: Check client workspace
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.
- 3d ago First seen · 233 lines · 0 tokens per session scan A 1c47074319f4
call-prep is a skill published in the GitHub repository aAAaqwq/AGI-Super-Team (91 stars, last pushed yesterday), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,716 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 2026-09-05.
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