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 prashishh/seo-geo-report-engine --skill opportunity-mapgit 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/skills/prashishh/seo-geo-report-engine/opportunity-map)<a href="https://agentmods.dev/skills/prashishh/seo-geo-report-engine/opportunity-map"><img src="https://agentmods.dev/badge/skills/prashishh/seo-geo-report-engine/opportunity-map/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/skills/prashishh/seo-geo-report-engine/opportunity-map"><img src="https://agentmods.dev/badge/skills/prashishh/seo-geo-report-engine/opportunity-map.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 | $0.00196 | $0.01249 |
| Opus 5 | $0.00098 | $0.00624 |
| Sonnet 5 | $0.00039 | $0.00250 |
| Haiku 4.5 | $0.00020 | $0.00125 |
Grade A, and why
opportunity-map 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 12d 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 — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.
opportunity-map
The engine that answers "where is the gap, how big is it, and exactly what do we build to capture it
and get on top of the competition." It exists because diagnosis (competitor-analysis, keyword-research,
geo-audit) tells you where you stand but not what to do; this turns that into a sized, sequenced,
falsifiable capture plan. Full methodology in playbooks/opportunity-capture.md (read it first).
When to reach for this
After (or alongside) the diagnostic skills, whenever the ask is "so what do we do to win." It is the
missing middle between competitor-analysis and the build skills (programmatic-seo,
comparison-pages, content-brief). If a project has a competitor read but no ranked, mechanism-driven
plan, run this.
Inputs (use what exists; pull what is missing)
research/diagnostics: competitor-analysis, keyword map, geo-audit / ai-citation-log, technical audit, backlinks, voice-of-customer. Read them all first.- Live data to fill gaps: Ahrefs (keyword gap, competitor organic keywords, KD, DR), the
web-researchskill for GEO open-field probing and competitor fact-checks, GA4/GSC for fit validation. client.ymlfor the goal (what "winning" converts to) and the competitor set.
Workflow (PERCEIVE -> ANALYZE -> VALIDATE -> ACT)
-
PERCEIVE (discover the four gaps). Per
playbooks/opportunity-capture.mdsection 1, find every gap: demand (competitor-ranks-we-do-not, low-KD real-volume terms, weak SERPs), citation (AI-answer open fields and beatable competitor citations, via the geo-audit / web-research probe), format (demand with no tool / comparison / dataset / interactive), and segment/journey (an ICP or journey stage the incumbents abandon). Record evidence + who owns it now. -
ANALYZE (size and rank). Score each cluster Demand x Winnability x Fit (section 2). Fit is the guardrail: high volume with the wrong audience is a Trap, not an opportunity (confirm against GA4 where possible). Label each Greenfield / Contested / Fortress / Trap. Rank descending.
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.
- 12d ago First seen · 79 lines · 196 tokens per session scan A 125bb04f2a21
opportunity-map is a skill published in the GitHub repository prashishh/seo-geo-report-engine (5 stars, last pushed 2mo ago), licensed MIT. It adds 196 tokens to every session and 1,249 once invoked, about $0.0010 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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