Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/Ken-Technology/cold-email-skillsnpx agentmods add skills/ken-technology/cold-email-skills/knowledge-setupWrote 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/ken-technology/cold-email-skills/knowledge-setup)<a href="https://agentmods.dev/skills/ken-technology/cold-email-skills/knowledge-setup"><img src="https://agentmods.dev/badge/skills/ken-technology/cold-email-skills/knowledge-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/ken-technology/cold-email-skills/knowledge-setup"><img src="https://agentmods.dev/badge/skills/ken-technology/cold-email-skills/knowledge-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 | $0.00073 | $0.02023 |
| Opus 5.5 | $0.00029 | $0.00809 |
| Sonnet 5.5 | $0.00015 | $0.00405 |
| Haiku 4.5 | $0.00007 | $0.00202 |
Grade A, and why
knowledge-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 9d 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 — 114 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Knowledge setup
The knowledge folder is what your agent knows about a company: what it sells, its offer and prices, who it sells to, its proof and competitors, and how its people write. It is plain markdown files on the user's machine, kept next to their campaign folders, so every campaign starts from the same facts: client-research reads it when it writes a campaign's research.md, and every campaign skill after it reads that.
This skill writes the folder and keeps it true. It writes files only: no account, no API keys, nothing sent anywhere.
The folder
Default ./knowledge/ under the current directory, next to ./cold-email/; use another path when the user names one.
./knowledge/
├── company-overview.md # what the company does, for whom, and why it wins
├── ideal-customer-profile.md # who buys, their problems, who is not a fit
├── pricing.md # plans and prices exactly as stated, with source and date
├── proof/ # one file per case study or result
│ └── {customer-slug}.md
├── competitors/ # one file per competitor
│ └── {competitor-slug}.md
├── voice.md # how the company's people write, each rule with its source
└── instructions.md # rules every email follows, each with its source
Related skills
- client-research researches a company for one campaign and writes
research.mdin the campaign workspace (./cold-email/{slug}/). It reads this folder first when it exists. - qualification writes who is in and who is out for a campaign (
qualification.md). - prompt-writer writes and tests the personalization prompts for a segment (
prompts.md). - create-campaign builds a whole campaign, from plan to ready-to-send copy.
Rules
- Extract, never invent. Every fact in a file comes from a source you can name: the website, a call, a document, or the person's words in this conversation. Never invent a price, a customer, a result or a number. A gap stays a gap: write it as an open question and ask.
- Cite what people want. A rule about how the company writes, or a claim about what a client wants, carries its source: who said it, where and when ("Dana, kickoff call, 2026-10-14"). With no source, ask; never fill the gap from a ticket's or an issue's claim.
- A yes before each write. Show what you will write, file by file, and save after the person agrees. A yes to the pricing file covers that file, not the folder.
- Read, then write. Read a file before you change it. When it changed since you read it (the person or someone else edited it), read it again, merge, show the person, save.
- Edit sections, keep files. Change the one section that changed and leave the rest. A restructure rewrites the same file, never a delete and a new file. When the folder sits in a git repo, its history keeps every version.
- Keep secrets out. Never write a password, an API key or a private link into these files: people share and commit them.
- This skill never sends, launches, or changes a campaign's schedule or volume. A person approves every send and launch.
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.
- 9d ago First seen · 114 lines · 73 tokens per session scan A c23b6438708a
knowledge-setup is a skill published in the GitHub repository Ken-Technology/cold-email-skills (4 stars, last pushed 6d ago), licensed MIT. It adds 73 tokens to every session and 2,023 once invoked, about $0.0003 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 2026-10-28.
knowledge-setup compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| knowledge-setupKen-Technology/cold-email-skills | Skill | 4 | 6d ago | MIT | A | 73 | Claude Code, Codex |
| cold-emailFrontal-so/outbound-skills | Skill | 9 | 1mo ago | MIT | A | 177 | Claude Code, Codex |
| list-buildingFrontal-so/outbound-skills | Skill | 9 | 1mo ago | MIT | A | 190 | Claude Code, Codex |
| claygentFrontal-so/outbound-skills | Skill | 9 | 1mo ago | MIT | A | 131 | Claude Code |
| conditional-logicFrontal-so/outbound-skills | Skill | 9 | 1mo ago | MIT | A | 128 | Claude Code |
| people-enrichmentFrontal-so/outbound-skills | Skill | 9 | 1mo ago | MIT | A | 110 | Claude Code |
| scoringFrontal-so/outbound-skills | Skill | 9 | 1mo ago | MIT | A | 113 | Claude Code |