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 rules/jondoescoding/jondoescoding-coding-rules/seo-long-form-article-blueprintgit clone --depth 1 https://github.com/jondoescoding/jondoescoding-coding-rulesWhat 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 | $0.01975 |
| Opus 5 | $0.00000 | $0.00988 |
| Sonnet 5 | $0.00000 | $0.00395 |
| Haiku 4.5 | $0.00000 | $0.00198 |
Grade A, and why
seo-long-form-article-blueprint 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 2d 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 — 301 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Article Creation Blueprint: The Systematic Method
Overview
This blueprint provides a systematic approach for creating high-converting, SEO-optimized articles that drive traffic and conversions. Use this framework for any article topic.
Core Principles
1. Always Start with the End Goal
- Primary Objective: Drive conversions to your primary offer
- Secondary Objectives: Build authority, provide genuine value, rank on Google
- Success Metric: Clear conversion tracking per article
2. Audience-First Approach
Define your specific avatar before writing:
- Who are they? (job, age, situation)
- What's their biggest pain point?
- What outcome do they desperately want?
- What's stopping them from achieving it?
Phase 1: Strategic Research (30-45 minutes)
Keyword Research Protocol
Research Framework:
-
Start with 5 keyword angles:
- [Main topic] + "tutorial"
- "How to [achieve outcome] with AI"
- "[Problem] solution for [audience]"
- "AI [tool/method] for beginners"
- "[Outcome] without [common obstacle]"
-
Evaluation Criteria:
- Search volume: 100-50,000 (sweet spot: 1,000-10,000)
- Competition: LOW to MEDIUM only
- Intent: Informational or Commercial
- Relevance: Direct connection to your offer
Statistical Research Protocol
Research Requirements:
- Find 3-5 credible statistics that support your argument
- Focus on: cost savings, time savings, success rates, industry trends
- ALWAYS provide source links in the article
- Prioritize data from 2024-2025
Example Search Patterns:
- "[Topic] ROI statistics [current year]"
- "[Industry] digital transformation data"
- "[Tool category] vs traditional methods study"
- "Small business [topic] challenges statistics"
Phase 2: Structured Interview Process
Interview Framework
Purpose: Extract authentic stories and specific details that create connection
Question Categories:
- Context Discovery
- What's the scope? (word count, depth, format)
- Who exactly is reading this?
- What action should they take after reading?
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.
- 2d ago First seen · 301 lines · 0 tokens per session scan A b0c3065533ff
seo-long-form-article-blueprint is a cursor rule published in the GitHub repository jondoescoding/jondoescoding-coding-rules (2 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,975 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 2026-08-31.
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