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 Ken-Technology/cold-email-skills --skill prompt-writergit clone --depth 1 https://github.com/Ken-Technology/cold-email-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/ken-technology/cold-email-skills/prompt-writer)<a href="https://agentmods.dev/skills/ken-technology/cold-email-skills/prompt-writer"><img src="https://agentmods.dev/badge/skills/ken-technology/cold-email-skills/prompt-writer/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/prompt-writer"><img src="https://agentmods.dev/badge/skills/ken-technology/cold-email-skills/prompt-writer.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 | $0.00071 | $0.05599 |
| Opus 5 | $0.00036 | $0.02799 |
| Sonnet 5 | $0.00014 | $0.01120 |
| Haiku 4.5 | $0.00007 | $0.00560 |
Grade A, and why
prompt-writer 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 11d 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 — 512 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Writer Skill
Generate detailed AI personalization prompts based on campaign strategy AI variables. These prompts instruct your personalization tool or the LLM to create personalized content for each prospect.
Output Contract
prompts.md is the handoff artifact for personalization. Hard rules this skill must honor:
## User Prompts (To output: true)H2 heading - exact phrasing.- One H3 per variable using Title Case (
### First Line, not### first_line). H3 name is ≤30 chars. - Every H3 must contain a
**Prompt**:marker followed by the prompt text. - Prompt text ≤4096 chars. Runs until next
###,##, or---. Do NOT use---inside a prompt body - it cuts the prompt short. - Variable names must match exactly what
emails_v2.mduses in{{Title Case}}braces (e.g.{{First Line}},{{PS Line}},{{Subject Line}}). - Optional
## Rewriting InstructionsH2 at the bottom - a short steering paragraph for a rewriter pass, if you use one.
Plan Folder Resolution
This skill receives a {segment_folder} path (e.g., {workspace}/03-15 - plan 1/1 - early-stage/). Resolve {plan_folder} as the parent directory of {segment_folder} when you need plan-level files such as plan.md.
Execution Tasks
Execute these tasks in order. Do NOT skip any task unless noted.
Task 1: Load Context
- Read
{segment_folder}/strategy.md- Extract AI variables section - Read
{segment_folder}/emails_v2.md- Reviewed email copy (fall back toemails.mdif not found) - Read
{workspace}/research.md- ICP, value prop, messaging themes, product info, case studies ({workspace}= the client campaign workspace, default./cold-email/{slug}/under the current directory) - Read
{workspace}/notes.md(if exists) - Client-specific preferences
Task 2: Build User Prompts
Note: You write user prompts that generate personalized variables (
to_output: true). Company context, base rules, and the email sequence are supporting context for the LLM - pull them from research and the sequence file rather than inventing a separate backend. The default rewriting rules (core principles, style, spam/deliverability, flow) are tool-side if your stack rewrites outputs - you SHOULD still write a short optional steering paragraph for rewriting (see Task 2b). Theto_rewritefield on each user prompt controls whether that variable's output should go through a rewriter.
What ships with it
2 files 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.
- 11d ago First seen · 512 lines · 71 tokens per session scan A 2edbf4c4ebf9
prompt-writer is a skill published in the GitHub repository Ken-Technology/cold-email-skills (3 stars, last pushed 14d ago), licensed MIT. It adds 71 tokens to every session and 5,599 once invoked, about $0.0004 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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