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.
git clone --depth 1 https://github.com/attaxr/attaxr-seo-pipelineWrote 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/rules/attaxr/attaxr-seo-pipeline/seo-pipeline)<a href="https://agentmods.dev/rules/attaxr/attaxr-seo-pipeline/seo-pipeline"><img src="https://agentmods.dev/badge/rules/attaxr/attaxr-seo-pipeline/seo-pipeline/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/rules/attaxr/attaxr-seo-pipeline/seo-pipeline"><img src="https://agentmods.dev/badge/rules/attaxr/attaxr-seo-pipeline/seo-pipeline.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 | $0.00000 | $0.00635 |
| Opus 5 | $0.00000 | $0.00318 |
| Sonnet 5 | $0.00000 | $0.00127 |
| Haiku 4.5 | $0.00000 | $0.00064 |
Grade A, and why
seo-pipeline 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 — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SEO Content Pipeline
Canonical source:
AGENTS.md(project root) — full pipeline details live there.
Pipeline (sequential, fail-stop)
flowchart LR
A["Keyword Research"] --> B["Content Collection"] --> C["SEO Analysis"] --> D["Content Creation"]
Stage 1 — Keyword Research
Search domain keywords via web search. Capture term, volume, difficulty, relevance (1-5), intent (commercial/informational/navigational), cluster. Prioritize long-tail commercial terms. Save to keywords.json.
Stage 2 — Content Collection
Credential Check (REQUIRED)
- Check
BROWSER_USE_API_KEYenv var → if unset, try.env→ if still unset, prompt user with redacted input (getpass.getpass()). - Never print the key in tool output, terminal, conversation, or logs.
- Do not proceed without a valid key.
Run
- Primary:
python -m seo_pipeline --from-json keywords.json(Browser Use Cloud SDK v3 — AI article extraction). - Fallback: DuckDuckGo HTML search (URLs only, no body content).
- Both fail? Stop. Do not stage 3.
Stage 3 — SEO Analysis
Analyze each article: keyword usage (title, H1, first 100 words, density), gaps, missing subtopics, readability, structure (H2/H3, lists, tables), 3-5 improvements.
Must find: the unique angle no competitor covers.
Output: pipeline_data/analysis/.
Stage 4 — Content Creation
Per cluster: 1,500+ words, original insight in first 200 words, H2/H3 hierarchy, lists, case study, CTA, 2-3 image suggestions.
Humanization
Read draft, strip AI-isms, adjust tone, vary rhythm, use active voice. Sound like an expert explaining to a colleague.
Output: pipeline_data/drafts/.
Quality Checklist
- All 4 stages done or explicitly skipped
- Each article ≥ 1,500 words
- Unique angle vs top 3 competitors
- SEO metadata complete
- ≥ 2 image suggestions per article
- Humanization pass applied
- Run report written
Configuration
| Variable | Default | Description |
|---|---|---|
SEO_PIPELINE_DIR |
./pipeline_data/ |
Artifact directory |
BROWSER_USE_API_KEY |
— | Browser Use Cloud API key |
AUTHOR_NAME |
Author Name |
Default author |
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 · 70 lines · 0 tokens per session scan A 67daa5f7d4d7
seo-pipeline is a cursor rule published in the GitHub repository attaxr/attaxr-seo-pipeline (2 stars, last pushed 2mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 635 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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