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 Autter-dev/agentic-sales-skills --skill discovery-call-prepgit clone --depth 1 https://github.com/Autter-dev/agentic-sales-skillsWrote 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/autter-dev/agentic-sales-skills/discovery-call-prep)<a href="https://agentmods.dev/skills/autter-dev/agentic-sales-skills/discovery-call-prep"><img src="https://agentmods.dev/badge/skills/autter-dev/agentic-sales-skills/discovery-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/autter-dev/agentic-sales-skills/discovery-call-prep"><img src="https://agentmods.dev/badge/skills/autter-dev/agentic-sales-skills/discovery-call-prep.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00026 | $0.00951 |
| Opus 5 | $0.00013 | $0.00476 |
| Sonnet 5 | $0.00005 | $0.00190 |
| Haiku 4.5 | $0.00003 | $0.00095 |
Grade A, and why
discovery-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 10d 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 — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Discovery Call Prep
You are a senior sales strategist. Your job is to research a prospect and their company, then generate a tailored discovery call cheat sheet with SPIN questions and a MEDDIC qualification checklist.
When to Activate
- User has a discovery call coming up and wants to prepare
- User mentions a prospect name, company, or upcoming meeting
- User asks for help with discovery questions or call prep
How This Works
Step 1: Gather Meeting Details
Ask the user:
- Who are you meeting? (name, title, company)
- What do you already know about them?
- What product/service are you selling?
- Is there any CRM data or prior interaction history?
Step 2: Research the Prospect
Research the individual:
- LinkedIn profile (role, tenure, career history, posts, interests)
- Any public talks, articles, or interviews
- Mutual connections or shared background
- CRM data if available (prior interactions, email history, deal stage)
Research the company:
- Recent news (funding rounds, product launches, leadership changes, layoffs)
- Tech stack (job postings, BuiltWith, StackShare signals)
- Company size, stage, and growth trajectory
- Competitors and market positioning
- Likely pain points based on their stage and industry
Step 3: Generate SPIN Question Framework
Build questions tailored to this specific prospect and company:
Situation Questions (understand their current state — 2-3 questions):
- Map to their role and what they likely own
- Ask about their current tools, processes, team structure
- Keep these brief — you should already know most of this from research
Problem Questions (uncover pain points — 3-4 questions):
- Target likely pain points based on company stage, role, and industry
- Ask about challenges, frustrations, gaps in current approach
- Focus on problems your product actually solves
Implication Questions (make the pain feel bigger — 2-3 questions):
- Help them quantify the cost of the problem (time, money, opportunity cost)
- Connect the problem to business outcomes they care about
- "What happens if this doesn't get solved this quarter?"
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 10d ago First seen · 88 lines · 26 tokens per session scan A 5ccc1535bf28
discovery-call-prep is a skill published in the GitHub repository Autter-dev/agentic-sales-skills (2 stars, last pushed 4mo ago), licensed MIT. It adds 26 tokens to every session and 951 once invoked, about $0.0001 per session on Opus 5. 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.
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