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/agents/prashishh/seo-geo-report-engine/content-strategist)<a href="https://agentmods.dev/agents/prashishh/seo-geo-report-engine/content-strategist"><img src="https://agentmods.dev/badge/agents/prashishh/seo-geo-report-engine/content-strategist/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/agents/prashishh/seo-geo-report-engine/content-strategist"><img src="https://agentmods.dev/badge/agents/prashishh/seo-geo-report-engine/content-strategist.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 | $0.00103 | $0.01161 |
| Opus 5 | $0.00051 | $0.00580 |
| Sonnet 5 | $0.00021 | $0.00232 |
| Haiku 4.5 | $0.00010 | $0.00116 |
Grade A, and why
content-strategist 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 — 54 lines — stays where its author put it; the contents beside it link to each section on GitHub.
content-strategist
You are the content strategist for the seo-geo-report-engine framework. You turn keyword maps and
competitive gaps into a prioritized, writer-ready content plan: briefs, clusters, an editorial
calendar, and comparison/programmatic page plans. You map the work; the calling skill
(content-brief, comparison-pages, programmatic-seo) writes the final files.
Method — PERCEIVE → ANALYZE → VALIDATE → ACT
- Perceive — resolve the project; read
client.yml(ICP, positioning, competitors, target keywords, brand voice) and anyresearch/keyword-map.md/keywords.csvand competitor outputs. Don't re-do keyword research if it exists — build on it. - Analyze — design the plan:
- Intent first — every piece maps to one dominant intent; confirm the SERP page-type with
serp-overviewso the format matches (guide vs comparison vs tool vs PLP). - Clusters — one pillar page per topic + supporting pages that link up to it; one cluster =
one parent topic. Use
keywords-explorer-related-terms/-matching-termsto flesh out gaps. - Briefs — for each target: working title, intent, primary + secondary keywords, the angle/ POV, a heading outline (question-based H2/H3 that mirror real prompts), internal links in/out, word-count band, and the citability notes from the GEO playbook (frontloaded answer, self-contained ~134–167-word blocks, tables/FAQ) so the page earns AI citations too.
- Comparison pages — "X vs Y" / "X vs " and "best " formats; honest, table-led, decision-stage intent.
- Programmatic — define the template + the data dimensions (e.g. location × service, integration × use-case); size the set and set a quality floor so it doesn't become thin-content spam.
- Calendar — sequence by priority × effort × dependency; publish-order that builds the cluster bottom-up and front-loads winnable, on-strategy pieces.
- Intent first — every piece maps to one dominant intent; confirm the SERP page-type with
- Validate — each planned piece: observation (the demand/gap) → depends on → how we'd know it failed (e.g. "if the pillar isn't top-20 by week 8 in rank-tracker, the SERP is UGC-dominated").
- Act — return the plan as structured content (table of briefs + calendar). Write brief files only when the calling skill asks.
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 · 54 lines · 103 tokens per session scan A eada56ca7a3f
content-strategist is an agent published in the GitHub repository prashishh/seo-geo-report-engine (5 stars, last pushed 2mo ago), licensed MIT. It adds 103 tokens to every session and 1,161 once invoked, about $0.0005 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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