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/prashishh/seo-geo-report-engineWrote 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/prashishh/seo-geo-report-engine/discovery-audit)<a href="https://agentmods.dev/commands/prashishh/seo-geo-report-engine/discovery-audit"><img src="https://agentmods.dev/badge/commands/prashishh/seo-geo-report-engine/discovery-audit/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/commands/prashishh/seo-geo-report-engine/discovery-audit"><img src="https://agentmods.dev/badge/commands/prashishh/seo-geo-report-engine/discovery-audit.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 | $0.00026 | $0.01677 |
| Opus 5 | $0.00013 | $0.00839 |
| Sonnet 5 | $0.00005 | $0.00335 |
| Haiku 4.5 | $0.00003 | $0.00168 |
Grade A, and why
discovery-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 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.
How it starts
The opening of the file, as written. The whole thing — 99 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/discovery-audit
The one-time intake audit for $1 (defaults to the active project — resolve with
./bin/mkt config show). Runs the five diagnostic skills, then synthesizes everything into a single
research/DISCOVERY.md that becomes the evidence base for the growth proposal. This is the
"PERCEIVE + ANALYZE" pass before we propose anything — every later claim should trace back to here.
Steps
-
Load context.
./bin/mkt config show --project $1. Readclient.yml→domain,competitors[],target_keywords[],market,languages,ahrefs.project_id,ahrefs.brand_radar_report_id. Ifclient.ymlis mostly empty, stop and point the user at/new-client $1first. Note which Ahrefs ids are missing — some skills degrade gracefully without them (e.g. rank tracking needsproject_id; GEO needsbrand_radar_report_id). -
Run the diagnostic skills. Order matters where one feeds the next; the rest can run in parallel. Prefer the Ahrefs MCP throughout (
knowledge/ahrefs-mcp-map.md); calldocon a tool before first use; monetary values are USD cents (÷100). Each skill writes toprojects/$1/research/.-
First, the upstream "who they are + the market" pass (see § Upstream layers below): 0a.
market-opportunity— TAM/SAM/SOM sizing + a PMF qualifier. Frames whether the market is worth winning before we measure how they rank. 0b.positioning-messaging+customer-research— positioning/category claim and a voice-of-customer (VOC) pull. Establishes who they are, who they serve, and how buyers describe the problem in their own words. -
Then, sequentially (it seeds the SEO/GEO fan-out):
keyword-research— seed → expansion → intent clusters → winnable-volume scoring against our DR. Produces the keyword map + the opportunity size the proposal will quote. Seed it with the VOC language and category terms from 0b.
-
Then, in parallel (independent reads — delegate to subagents to run concurrently): 2.
technical-seo-audit— crawl/health, on-page, Core Web Vitals, indexability (Site Audit + GSC). The "Foundation" evidence. 3.competitor-analysis— keyword gap, content gap, backlink prospects, ad/promo notes vscompetitors[]. Reuses the keyword map from step 1. 4.geo-audit— AI-search visibility via Brand Radar: SoV, mentions, who AI cites, citability + crawler-access check. The "are we present in AI answers" read. 5.backlink-analysis— authority profile, ref-domain growth, anchor health, toxic/lost links, and reclaim/prospect targets.
-
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 · 99 lines · 26 tokens per session scan A 132898ad71b8
discovery-audit is a command published in the GitHub repository prashishh/seo-geo-report-engine (5 stars, last pushed 2mo ago), licensed MIT. It adds 26 tokens to every session and 1,677 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 2026-08-31.
Other commands, from other repositories
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.
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.
influencer
Run an influencer-marketing (STAR) workflow: audience & creator scouting, campaign targeting, briefs, outreach, amplification, and ROI reporting. Not sure? Use /aaron-marketing:auto.
launch
Run a product-launch (RAMP) workflow: positioning and launch tiering, window/early-access design, message house and asset kits, the launch-readiness gate with a T-1 go/no-go, launch-day execution, and the post-launch prove loop. 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.