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 commands/mverab/egeoagents/geogit clone --depth 1 https://github.com/mverab/eGEOagentsWrote 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/commands/mverab/egeoagents/geo)<a href="https://agentmods.dev/commands/mverab/egeoagents/geo"><img src="https://agentmods.dev/badge/commands/mverab/egeoagents/geo.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 | $0.00021 | $0.00497 |
| Opus 5 | $0.00010 | $0.00249 |
| Sonnet 5 | $0.00004 | $0.00099 |
| Haiku 4.5 | $0.00002 | $0.00050 |
Grade A, and why
geo 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 5d 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.
What it actually says
/geo Command
Execute full GEO optimization pipeline.
Workflow
- Validate MCPs - Run
validation-doctor; if missing, provide setup snippets - Frontmatter Extraction - If the target is a local file containing frontmatter (YAML/TOML blocks), extract and preserve it completely unchanged. Only pass the remaining body content to the analyzer and rewriter.
- Analyze - Extract and score current content body (source of truth)
- Rank - Simulate baseline AI-engine ranking based on analyzer output
- Rewrite - Optimize content using analyzer findings. If the target was a local file with frontmatter, prepend the original unmodified frontmatter block to the optimized content body when saving.
- Index - Generate schema markup using analyzer findings
- Report - Compile results using analyzer output + validation status
Execution
Analyzing: $ARGUMENTS.target
Step 0/7: MCP Validation
→ Running validation-doctor...
Step 1/7: Frontmatter Extraction
→ Extracting and preserving frontmatter if present...
Step 2/7: Content Analysis
→ Delegating to geo-analyzer...
Step 3/7: Ranking Simulation
→ Delegating to geo-ranker...
Step 4/7: Content Optimization
→ Delegating to geo-rewriter...
Step 5/7: Schema Generation
→ Delegating to geo-indexer...
Step 6/7: Report Compilation
→ Generating final report...
Output
Save results to geo-output/ folder:
report.md- Executive summary with scoresanalysis.json- Raw analysis dataoptimized/[name].[ext]- Rewritten content (.mdor.htmldepending on format)schema/[name].json- JSON-LD markupchecklist.md- Implementation steps
Example Usage
/geo https://mysite.com/pricing
/geo ./content/landing-page.md
/geo https://competitor.com/product (analyze only, suggest how to beat)
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.
- 5d ago First seen · 67 lines · 21 tokens per session scan A 09285eae5091
geo is a command published in the GitHub repository mverab/eGEOagents (173 stars, last pushed 4d ago), licensed MIT. It adds 21 tokens to every session and 497 once invoked, about $0.0001 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 2026-08-30.
Other commands, from other repositories
auto
Natural-language front door to the marketing pack (narrative/TALE, SEO/GEO/SITE, social/ECHO, email/SEND, Paid Ads/ROAS, influencer/STAR, launch/RAMP). Use when a marketing goal is open-ended or spans disciplines, when it is unclear which skill fits, or for requests like 'help with our marketing', 'grow our traffic'…
seo-geo
SEO/GEO end-to-end along the SITE loop: survey demand and competitors, implement content, tune quality/tech/on-page, and evaluate authority/rankings/reports/memory (--phase survey|implement|tune|evaluate). Not sure? Use /aaron-marketing:auto.
narrative
Run a brand-narrative & messaging (TALE) workflow: trace the current message and positioning truth, architect the durable message house/voice/story canon, land it consistently across every surface, and evaluate resonance with tests and drift monitoring. Not sure? Use /aaron-marketing:auto.
social
Run an organic-social (ECHO) workflow: channel portfolio and voice dossiers, platform-native content and calendars, the social-quality gate with a pre-publish go/no-go, community/inbox/crisis operations, and the listening/SOV/dark-social measurement loop. Not sure? Use /aaron-marketing:auto.
ad
Run a paid-ads (ROAS) workflow: audience segments, account structure, ad creative, experiment design, pre-launch signal QA + the account-audit gate, measurement, and attribution. Not sure? Use /aaron-marketing:auto.
Run an email-marketing (SEND) workflow: deliverability/consent setup, segmentation, email creative, lifecycle flows, newsletter monetization, send-testing, and the email-quality audit gate. Not sure? Use /aaron-marketing:auto.