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 agentmods add commands/vuvax/press-rank/researchgit clone --depth 1 https://github.com/VUVAX/press-rankWrote 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/vuvax/press-rank/research)<a href="https://agentmods.dev/commands/vuvax/press-rank/research"><img src="https://agentmods.dev/badge/commands/vuvax/press-rank/research.svg" alt="Measured on agentmods" 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 | $0.00015 | $0.00426 |
| Opus 5 | $0.00008 | $0.00213 |
| Sonnet 5 | $0.00003 | $0.00085 |
| Haiku 4.5 | $0.00002 | $0.00043 |
Grade A, and why
research 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 5d 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.
What it actually says
/press:research
Produce a research brief that a writer can hand straight to /press:write.
Input
$ARGUMENTS— the topic or seed keyword.
Steps
- Seed expansion. Derive 15–30 keyword variations grouped by intent
(informational, commercial, transactional, navigational). If DataForSEO is
configured in
.env, pull volume + difficulty; otherwise estimate and label estimates clearly. - SERP intent read. For the primary keyword, characterize what currently ranks: page type (guide, listicle, product, tool), dominant angle, average word count, and whether an AI Overview / featured snippet is present.
- Competitor gap. List 3–5 top results and what they each cover. Flag subtopics they all miss (the gap to win) and entities they all mention (table stakes you must include).
- GEO angle. Identify 2–3 questions an AI assistant would answer from this page and the quotable facts it would cite.
- Recommended outline. H2/H3 skeleton with the target keyword mapped to each section and the search intent it serves.
Output
Save to research/<slug>.md with this shape:
# Research Brief: <topic>
- Primary keyword: <kw> (vol / difficulty / intent)
- Secondary keywords: [...]
- Search intent: <type>
- Recommended format: <guide | listicle | how-to | comparison>
- Target word count: <range>
## SERP snapshot
## Competitor gaps (win these)
## Table stakes (must include)
## GEO questions to answer
## Recommended outline (H2/H3 + keyword map)
Then suggest: Run /press:write <topic> to draft from this brief.
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.
- 5d ago First seen · 47 lines · 15 tokens per session scan A e611ba1374b7
research is a command published in the GitHub repository VUVAX/press-rank (2 stars, last pushed 3mo ago), licensed MIT. It adds 15 tokens to every session and 426 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
auto
Natural-language front door to the marketing pack (narrative/TALE, SEO/GEO/SITE, social/ECHO, email/SEND, Paid Ads/ROAS, influencer/STAR, launch/RAMP). Use when a marketing goal is open-ended or spans disciplines, when it is unclear which skill fits, or for requests like 'help with our marketing', 'grow our traffic'…
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.
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.
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.
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.
status
Unified status snapshot of the active brand: profile, engagements, recent insights, recent compliance violations, Python dependency mode.