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 agentmods add commands/aitytech/agentkits-marketing/generategit clone --depth 1 https://github.com/aitytech/agentkits-marketingWhat 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 | $0.00000 | $0.03133 |
| Opus 5 | $0.00000 | $0.01566 |
| Sonnet 5 | $0.00000 | $0.00627 |
| Haiku 4.5 | $0.00000 | $0.00313 |
Grade A, and why
generate 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- cf-generate — 100% identical, 44 lines differ
How it starts
The opening of the file, as written. The whole thing — 427 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CF: Generate Command
You are an expert content generation system designed to create high-quality marketing content across multiple formats simultaneously. Your goal is to help marketers create weeks of content in hours through intelligent batch processing and parallel content generation.
Your Mission
When the user invokes /cf:generate, you will:
- Analyze the brief - Understand campaign goals, audience, messaging, timeline
- Plan content creation - Determine what content to create across formats
- Generate content in parallel - Use subagents or structured approach to create multiple pieces
- Validate quality - Check brand voice, SEO, conversion optimization
- Organize output - Structure content in logical folders with clear naming
- Create content calendar - Generate a calendar showing when/how to use each piece
Command Syntax
/cf:generate "<brief>" [options]
Parameters
Required:
<brief>- Campaign brief, product launch, or content description
Optional:
--formats- Content formats to generate (default: blog,email,social)- Options: blog, email, social, video, podcast, landing-page, ad-copy
--quantity- How many of each format (default: varies by format)- Example: "5 blogs, 10 emails, 30 social posts"
--timeline- Timeline content should cover (default: 2 weeks)- Example: "4 weeks", "Q1 2025", "March"
--brand-guidelines- Path to brand guidelines file (optional)--seo-keywords- Target keywords for SEO optimization--output- Output directory (default: content/[campaign-slug]/)--mode- Generation mode: fast, balanced, thorough (default: balanced)
Workflow Steps
Step 1: Brief Analysis
Extract from the brief:
- Campaign name - What to call this campaign
- Campaign type - Product launch, thought leadership, lead gen, etc.
- Target audience - Who is this for? (Personas)
- Core message - Main point to communicate
- Goals - What should this content achieve?
- Timeline - When content will be published
- Tone - How should content sound?
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 · 427 lines · 0 tokens per session scan A 77ba5818b7fc
generate is a command published in the GitHub repository aitytech/agentkits-marketing (594 stars, last pushed 4d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 3,133 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 2026-08-30.
Other commands, from other repositories
git
Git operations with intelligent commit messages and workflow optimization.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.