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/content-brief/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/content-brief)<a href="https://agentmods.dev/skills/prashishh/seo-geo-report-engine/content-brief"><img src="https://agentmods.dev/badge/skills/prashishh/seo-geo-report-engine/content-brief/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/content-brief"><img src="https://agentmods.dev/badge/skills/prashishh/seo-geo-report-engine/content-brief.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 | $0.00121 | $0.01744 |
| Opus 5 | $0.00060 | $0.00872 |
| Sonnet 5 | $0.00024 | $0.00349 |
| Haiku 4.5 | $0.00012 | $0.00174 |
Grade A, and why
content-brief 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 10d 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 — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.
content-brief
Gives a writer everything needed to build a page that wins the SERP and gets cited by AI
answers. Built from the live SERP + competitor pages via Ahrefs MCP (see
knowledge/ahrefs-mcp-map.md); call doc before first use. Takes a keyword/cluster — often
straight from the keyword-research output.
Methodology (PERCEIVE → ANALYZE → VALIDATE → ACT)
PERCEIVE — read the SERP. Resolve project (./bin/mkt config show --project <client>). Pull
the SERP with serp-overview (target keyword + locale): note the page types ranking, SERP
features (PAA, featured snippet, AI Overview), and who's there. Identify the real competitors
(filter out Wikipedia/Reddit/news/job boards). For each, pull site-explorer-top-pages /
site-explorer-pages-by-traffic to find the exact ranking URL and its sibling pages, and
site-explorer-organic-keywords on that URL to see every term it already ranks for.
ANALYZE — extract the requirements. Before deriving structure, pull two on-brand inputs:
- Voice of customer — read
customer-research'sresearch/voc.mdfor the client: lift real customer phrasing, the words they use for the problem, and their top objections. Seed FAQ questions and subtopic framing from these, not from how the client describes itself. - Positioning — consume the canonical one-liner + pillars from the
positioning-messagingoutput (client.ymlmessagingblock). The brief's angle, meta, and proof points must ladder to a named pillar so the writer stays on-brand.
From SERP + competitors derive:
- Intent — informational / commercial / transactional / navigational (must match the dominant page type, or the brief is wrong before it's written).
- Subtopics & entities — the H2/H3 coverage every top page shares, plus gaps (topics
competitors miss or cover shallowly = the unique angle). Pull related terms from
keywords-explorer-related-termsto surface entities to name. - Questions — harvest PAA-style questions from
serp-overviewandkeywords-explorer- search-suggestionsfor an FAQ section. - Target length — informed by competitor depth, not a fixed number; cite the range observed.
- Internal links — 3–5 on-site targets (hub/pillar + supporting pages) via
site-explorer-pages-by-internal-links. - E-E-A-T signals — author/credentials, sources to cite, original data/examples, the experience proof this client can credibly show. Every subtopic must be something the client can truthfully write about — don't invent expertise or services.
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.
- 10d ago First seen · 109 lines · 121 tokens per session scan A 8a36b7e64028
content-brief is a skill published in the GitHub repository prashishh/seo-geo-report-engine (5 stars, last pushed 2mo ago), licensed MIT. It adds 121 tokens to every session and 1,744 once invoked, about $0.0006 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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