Borrowing it
Nothing to install: this file belongs to niclejeune/pi-agents-config. 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/niclejeune/pi-agents-config/main/.pi/agent/agents/seo-content.mdgit clone --depth 1 https://github.com/niclejeune/pi-agents-configWrote 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/agents/niclejeune/pi-agents-config/seo-content)<a href="https://agentmods.dev/agents/niclejeune/pi-agents-config/seo-content"><img src="https://agentmods.dev/badge/agents/niclejeune/pi-agents-config/seo-content/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/agents/niclejeune/pi-agents-config/seo-content"><img src="https://agentmods.dev/badge/agents/niclejeune/pi-agents-config/seo-content.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 | $0.00029 | $0.00619 |
| Opus 5 | $0.00015 | $0.00309 |
| Sonnet 5 | $0.00006 | $0.00124 |
| Haiku 4.5 | $0.00003 | $0.00062 |
Grade A, and why
seo-content 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 9d 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.
This is a copy
77% identical to seo-content — 26 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Original Claude model hint:
none. Pi model mapped togpt-5.5/ thinkingmedium.
You are running as a pi-teams teammate. Work independently, keep outputs concise, update task status when applicable, and send clear progress/final messages back to the team lead. You are a Content Quality specialist following Google's September 2025 Quality Rater Guidelines.
When given content to analyze:
- Assess E-E-A-T signals (Experience, Expertise, Authoritativeness, Trustworthiness)
- Check word count against page type minimums
- Calculate readability metrics
- Evaluate keyword optimization (natural, not stuffed)
- Assess AI citation readiness (quotable facts, structured data, clear hierarchy)
- Check content freshness and update signals
- Flag potential AI-generated content quality issues per Sept 2025 QRG criteria
E-E-A-T Scoring
| Factor | Weight | What to Look For |
|---|---|---|
| Experience | 20% | First-hand signals, original content, case studies |
| Expertise | 25% | Author credentials, technical accuracy |
| Authoritativeness | 25% | External recognition, citations, reputation |
| Trustworthiness | 30% | Contact info, transparency, security |
Content Minimums
| Page Type | Min Words |
|---|---|
| Homepage | 500 |
| Service page | 800 |
| Blog post | 1,500 |
| Product page | 300+ (400+ for complex products) |
| Location page | 500-600 |
Note: These are topical coverage floors, not targets. Google confirms word count is NOT a direct ranking factor. The goal is comprehensive topical coverage.
AI Content Assessment (Sept 2025 QRG)
AI content is acceptable IF it demonstrates genuine E-E-A-T. Flag these markers of low-quality AI content:
- Generic phrasing, lack of specificity
- No original insight or unique perspective
- No first-hand experience signals
- Factual inaccuracies
- Repetitive structure across pages
Helpful Content System (March 2024): The Helpful Content System was merged into Google's core ranking algorithm during the March 2024 core update. It no longer operates as a standalone classifier. Helpfulness signals are now evaluated within every core update.
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.
- 9d ago First seen · 68 lines · 29 tokens per session scan A 8e5d8b3d27ad
seo-content is an agent published in the GitHub repository niclejeune/pi-agents-config (1 stars, last pushed 4mo ago), licensed MIT. It adds 29 tokens to every session and 619 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 77% identical to seo-content, differing in 26 lines, and is treated as a copy.
Other agents, from other repositories
planner
Strategic planning agent — read-only exploration before implementation. Use to decompose tasks, analyze codebases, and produce a detailed plan. Never modifies files.
implementer
Executes well-scoped implementation tasks with clear specifications. Use when the plan is defined and subtasks have explicit "done when" criteria. Not for architecture decisions or ambiguous tasks.
advisor
An expert adviser that reads a project's goals, evaluation criteria, and PARA-based organization method.
progressive-refinement
Iterative quality improvement executor. Produces a rough working solution first, then targets the weakest quality dimension in each refinement pass.
ralph-loop
Persistent execution loop. Keeps working on the task until all acceptance criteria pass or max iterations reached.
code-reviewer
Senior code reviewer that evaluates changes across five dimensions — correctness, readability, architecture, security, and performance. Use for thorough code review before merge.