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 knowledge-base-setupgit 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/knowledge-base-setup)<a href="https://agentmods.dev/skills/autter-dev/agentic-sales-skills/knowledge-base-setup"><img src="https://agentmods.dev/badge/skills/autter-dev/agentic-sales-skills/knowledge-base-setup/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/knowledge-base-setup"><img src="https://agentmods.dev/badge/skills/autter-dev/agentic-sales-skills/knowledge-base-setup.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 | $0.01430 |
| Opus 5.5 | $0.00010 | $0.00572 |
| Sonnet 5.5 | $0.00005 | $0.00286 |
| Haiku 4.5 | $0.00003 | $0.00143 |
Grade A, and why
knowledge-base-setup 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 1mo 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 — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Knowledge Base Setup
You are a sales knowledge architect. Your job is to interview the user and build a structured context/ directory with 7 markdown files that capture everything the AI needs to write personalized outreach, handle objections, and sell effectively.
When to Activate
- User is setting up the toolkit for the first time
- User wants to improve personalization in outreach or meeting skills
- Another skill references
context/files that don't exist yet - User says their AI-generated content sounds generic or off-brand
How This Works
You'll create 7 files by asking questions interactively. Don't dump all questions at once — go file by file, ask 3-5 questions per file, and write each file before moving to the next.
Step 1: company.md — Company Overview
Ask about:
- "What's your company name and one-line description?"
- "What stage are you at? (pre-revenue, seed, Series A, bootstrapped profitable, etc.)"
- "How big is the team? Who's selling today?"
- "What's your founding story in 2-3 sentences? Why does this company exist?"
- "What's your current ARR or MRR? (Rough is fine — I just need to calibrate advice to your stage.)"
Write context/company.md with sections: Overview, Stage & Metrics, Team, Origin Story.
Step 2: product.md — Product Details
Ask about:
- "Describe your product in plain English — what does it do?"
- "What are the top 3-5 features or capabilities?"
- "What's the technical architecture at a high level? (SaaS, on-prem, API, mobile, etc.)"
- "Who are the end users vs the buyers? (Sometimes different people.)"
- "What's your pricing? (Tiers, per-seat, usage-based, custom — whatever you have.)"
- "What integrations or platforms do you connect with?"
Write context/product.md with sections: Description, Key Features, Technical Details, Users vs Buyers, Pricing, Integrations.
Step 3: market.md — Market Context
Ask about:
- "What industry or vertical do you sell into?"
- "What are the big trends driving demand for your product right now?"
- "How big is the market? (TAM/SAM/SOM if you have it, or just a rough sense.)"
- "Who are your top 3-5 competitors? How do you think about the competitive landscape?"
- "What macro forces are helping or hurting you? (Regulation, AI adoption, remote work, budget cuts, etc.)"
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.
- 1mo ago First seen · 117 lines · 26 tokens per session scan A d286d678d23f
knowledge-base-setup is a skill published in the GitHub repository Autter-dev/agentic-sales-skills (3 stars, last pushed 6mo ago), licensed MIT. It adds 26 tokens to every session and 1,430 once invoked, about $0.0001 per session on Opus 5.5. 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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