Borrowing it
Nothing to install: this file belongs to prashishh/seo-geo-report-engine. 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/prashishh/seo-geo-report-engine/main/.agents/skills/competitor-analysis/SKILL.mdgit 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/competitor-analysis)<a href="https://agentmods.dev/skills/prashishh/seo-geo-report-engine/competitor-analysis"><img src="https://agentmods.dev/badge/skills/prashishh/seo-geo-report-engine/competitor-analysis/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/competitor-analysis"><img src="https://agentmods.dev/badge/skills/prashishh/seo-geo-report-engine/competitor-analysis.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 | $0.00134 | $0.01724 |
| Opus 5 | $0.00067 | $0.00862 |
| Sonnet 5 | $0.00027 | $0.00345 |
| Haiku 4.5 | $0.00013 | $0.00172 |
Grade A, and why
competitor-analysis 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 — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.
competitor-analysis
Finds the gap between us and our true organic competitors and turns it into a prioritized,
falsifiable attack list. Ahrefs MCP is the engine (see knowledge/ahrefs-mcp-map.md); the full
methodology — competitor selection, the gap-scoring model, prioritization — lives in
playbooks/competitor-intel.md. Read it before scoring.
Methodology is PERCEIVE → ANALYZE → VALIDATE → ACT. Every recommendation must be falsifiable: state the observation, the dependency it rests on, and the leading indicator that would tell us within weeks that it failed.
Inputs
projects/<client>/client.yml— ourdomain,competitors[],target_keywords[],market,ahrefs.project_id. Resolve with./bin/mkt config show --project <client>.- Any prior research in
projects/<client>/research/and snapshots indata/.
Workflow
1. PERCEIVE — identify the true competitor set
- Pull organic competitors:
site-explorer-organic-competitorsfor ourdomain. - Union with the
competitors[]named inclient.yml(these are business rivals — they may or may not overlap organically). - Validate overlap on money keywords, not vanity keywords. A domain that overlaps only on
informational/long-tail terms we don't monetize is not a competitor. Confirm overlap on our
target_keywordsand commercial-intent head terms viasite-explorer-organic-keywords(filter to our money terms) andserp-overviewon 3–5 head terms. - Decide a working set (usually 3–5). Note which are organic rivals, business rivals, or both.
2. ANALYZE — pull the four gaps
Save every raw pull to projects/<client>/data/ (e.g. competitor-<domain>-keywords.json).
- Keyword gap. Pull
site-explorer-organic-keywordsfor us and each competitor. Find terms they rank top-10 for and we don't (or rank >20). Score each — see the model below. - Content gap.
site-explorer-top-pages/site-explorer-pages-by-trafficfor each competitor → cluster their best pages into themes/topics we lack. Note format (guide, tool, comparison, template) and the intent it serves. - Backlink gap.
site-explorer-referring-domainsfor us and each competitor → find domains linking to ≥2 competitors but not us (warm link prospects). Runsite-explorer-broken-backlinkson competitors → dead pages with live links = reclaim/recreate targets. Usesite-explorer-pages-by-backlinksto see which competitor pages earn the links. - SERP / authority.
rank-tracker-competitors-overview/-domains/-pagesandserp-overviewon head terms for live position context.site-explorer-domain-ratingand-domain-rating-historyfor us + each competitor to gauge feasibility and momentum.
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 · 107 lines · 134 tokens per session scan A f856806f9ad3
competitor-analysis is a skill published in the GitHub repository prashishh/seo-geo-report-engine (5 stars, last pushed 2mo ago), licensed MIT. It adds 134 tokens to every session and 1,724 once invoked, about $0.0007 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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