Borrowing it
Nothing to install: this file belongs to attaxr/attaxr-seo-pipeline. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/attaxr/attaxr-seo-pipeline/main/AGENTS.mdgit 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/instructions/attaxr/attaxr-seo-pipeline/agents-md)<a href="https://agentmods.dev/instructions/attaxr/attaxr-seo-pipeline/agents-md"><img src="https://agentmods.dev/badge/instructions/attaxr/attaxr-seo-pipeline/agents-md/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/instructions/attaxr/attaxr-seo-pipeline/agents-md"><img src="https://agentmods.dev/badge/instructions/attaxr/attaxr-seo-pipeline/agents-md.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 | $0.01766 | $0.01766 |
| Opus 5 | $0.00883 | $0.00883 |
| Sonnet 5 | $0.00353 | $0.00353 |
| Haiku 4.5 | $0.00177 | $0.00177 |
Grade A, and why
attaxr-seo-pipeline AGENTS.md 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 — 176 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SEO Content Pipeline Agent Instructions
Platform-agnostic workflow — works with Hermes Agent, Claude Code, Codex, OpenCode, or any LLM-based agent framework that supports tool calling (web search, file I/O, terminal, browser automation).
A 4-stage pipeline for systematic SEO content research, analysis, and creation. Outputs local files — no CMS integration.
How This Works
Runs as a stage-by-stage loop, one keyword cluster at a time. Each stage feeds the next. If a stage fails on a specific item, log the error and continue.
Agent Framework Requirements
| Capability | Used In | Purpose |
|---|---|---|
| Web search API | Stage 1-2 | Keyword research, SERP extraction |
| Browser automation / MCP | Stage 2 | Article content scraping |
| File system read/write | All stages | Data persistence between stages |
| LLM text generation | Stage 3-4 | SEO analysis, content creation |
| Python execution | Stage 2 | Scraping |
Stage 1 — Keyword Research
- Use web search to research keywords related to the business domain.
- For each keyword, capture: phrase, estimated search volume, relevance (1-5), commercial intent, cluster name.
- Prioritize long-tail keywords with clear commercial intent.
- Save to
keywords.json.
Schema:
[{"term":"keyword","search_volume":3200,"difficulty":72,"relevance":5,"intent":"commercial","cluster":"cluster-name"}]
Stage 2 — Content Collection
⚠️ Step 0 — Credential Check (REQUIRED)
Before attempting any scraping, the agent MUST verify that BROWSER_USE_API_KEY is configured:
- Check if the environment variable
BROWSER_USE_API_KEYis set. - If not set and a
.envfile exists, load it via:
Then re-check the variable.python -c "from pathlib import Path; import os; exec(open(Path('.env')).read().replace('export ',''))" - If still not set, the agent MUST prompt the user interactively:
"BROWSER_USE_API_KEY is not configured. This is required for article extraction. Enter your key (starts with 'bu_'): " - Security rules for the key input:
- The key must be captured via a redacted input method — never via plain
input()or echo-enabled prompts. - The
python scripts/setup.pyscript usesgetpass.getpass()which masks input; pass control to it when feasible. - The key value must never appear in logs, terminal scrollback, conversation history, or LLM output.
- If you write the key to
.envon the user's behalf, do so withset +o historyand sanitize any terminal output. - After capturing, write to
.envand source it:set -a; source .env; set +a - Do not proceed without a valid key.
- The key must be captured via a redacted input method — never via plain
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 · 176 lines · 1,766 tokens per session scan A ffd40d08d255
attaxr-seo-pipeline AGENTS.md is an instructions file published in the GitHub repository attaxr/attaxr-seo-pipeline (2 stars, last pushed 2mo ago), licensed MIT. It adds 1,766 tokens to every session, about $0.0088 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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