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 skills add AutomateLab-tech/seo-performance-mcp --skill weekly-auditgit clone --depth 1 https://github.com/AutomateLab-tech/seo-performance-mcpWrote 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/skills/automatelab-tech/seo-performance-mcp/weekly-audit)<a href="https://agentmods.dev/skills/automatelab-tech/seo-performance-mcp/weekly-audit"><img src="https://agentmods.dev/badge/skills/automatelab-tech/seo-performance-mcp/weekly-audit.svg" alt="Measured on agentmods" 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 | $0.00064 | $0.01230 |
| Opus 5 | $0.00032 | $0.00615 |
| Sonnet 5 | $0.00013 | $0.00246 |
| Haiku 4.5 | $0.00006 | $0.00123 |
Grade A, and why
weekly-audit 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 8d 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 — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.
weekly-audit
Companion skill for @automatelab/seo-performance-mcp. Where the base seo-performance skill routes a single question to the right tool, this skill runs the full weekly playbook end-to-end and returns one ranked action list.
The MCP server already exposes an audit_cohort prompt that runs a cohort report and emits briefs. This skill is one layer higher: it cross-references three independent signals (quick wins, cohort verdicts, citation losses), dedupes by URL, and ranks by how many signals agree.
When to use
The user wants the answer to "what should I edit this week" in one go - not a chat-driven exploration. For single-URL questions, defer to the base seo-performance skill.
What it does
Read-only. Proposes edits. Never applies them. Wiring the apply path is up to your CMS - keep audit and apply as separate skills.
Steps
1. Pull three lists in parallel
Issue these in a single message:
gsc_quick_winswithwindow=90, min_position=5, max_position=15, min_impressions=50, limit=20cohort_reportwithmin_age_days=90, window=30, limit=20posts_listwithlimit=50, min_age_days=30(input for the citation sweep)
2. Citation-loss sweep
For the top 15 URLs from posts_list (oldest first), call posts_cite_loss per URL. Keep only URLs with losses[].length > 0. If CITATION_INTELLIGENCE_URL is unset, this step returns empty - skip it.
3. Dedupe and rank
Merge by URL. A URL appearing in multiple lists gets a priority boost:
| Signal | Weight |
|---|---|
cohort_report verdict = refresh or merge |
3 |
gsc_quick_wins has any query at 0% CTR |
2 |
posts_cite_loss has losses |
2 |
cohort_report verdict = expand or double_down |
1 |
gsc_quick_wins low-CTR only |
1 |
Sort by total weight desc, then by cohort_report confidence desc. Take top 5.
4. Per top-5 URL, propose one concrete edit
Pick the single highest-lift edit for the dominant signal:
- Quick-win URL with 0% CTR query at position 5-15 -> rewrite
meta_titleusing the query verbatim, under 60 chars. refreshverdict withdecay_30d_over_30pct-> propose a new H2 + intro paragraph targeting the top GSC query.mergeverdict -> name the sibling URL and recommend a 301 target.- Citation loss -> propose H1 + lead-paragraph phrasing that mirrors the lost query verbatim (LLMs cite phrases, not paraphrases).
expandverdict -> name 3 FAQ questions to add (pull fromposts_snapshottop queries).
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.
- 8d ago First seen · 98 lines · 64 tokens per session scan A 0564d16e6353
weekly-audit is a skill published in the GitHub repository AutomateLab-tech/seo-performance-mcp (2 stars, last pushed 3mo ago), licensed MIT. It adds 64 tokens to every session and 1,230 once invoked, about $0.0003 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.
Other skills, from other repositories
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competitive-analysis
Analyze AI-search competitors for a query and recommend ranking strategy. Use when users ask competitor analysis, who ranks, or competitive landscape.
validation-doctor
Check Brave Search and Chrome DevTools MCP availability and provide exact setup snippets. Use when validation dependencies are missing or uncertain.
geo-content-research
Researches what prompts people ask AI engines (ChatGPT, Gemini, Perplexity, Claude) about a product category and produces a prompts.csv artifact — a prioritized, strictly-schema'd list of the queries where the brand should be cited. Feeds the monitor workflow.
create-geo-charts
Creates data visualizations (charts, graphs, tables) optimized for AI engine parsing and citation. Produces inline SVG/HTML with text summaries, data tables, and JSON-LD so AI engines can quote the data.