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 naveedharri/benai-skills --skill marketing-os-researchgit clone --depth 1 https://github.com/naveedharri/benai-skillsWrote 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/naveedharri/benai-skills/marketing-os-research)<a href="https://agentmods.dev/skills/naveedharri/benai-skills/marketing-os-research"><img src="https://agentmods.dev/badge/skills/naveedharri/benai-skills/marketing-os-research/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/naveedharri/benai-skills/marketing-os-research"><img src="https://agentmods.dev/badge/skills/naveedharri/benai-skills/marketing-os-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00215 | $0.04116 |
| Opus 5 | $0.00108 | $0.02058 |
| Sonnet 5 | $0.00043 | $0.00823 |
| Haiku 4.5 | $0.00021 | $0.00412 |
Grade A, and why
marketing-os-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 7d 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 — 239 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Marketing OS Research
Research one question properly, then put the answer where the rest of the OS will read it.
The queue is already written. A well-kept OS records what it does not know: files marked as open questions, beliefs flagged as untested, ideas with no established pain, findings that name the exact measurement that would settle them. That backlog is this skill's input, and working it is more valuable than researching whatever comes to mind, because somebody already decided those questions mattered and wrote down what would answer them.
The output is not a report. It is a file the writers, the strategy and the next research run all read. The rendered page is a convenience for a human. The markdown is the artifact.
Run from the OS root. Stay inside that root. Start from zero on identity: who this is for and what they believe comes from the OS or from the user, never from your context.
First, check what you have
| State | Do |
|---|---|
No Context/config.md |
Not a Marketing OS. Point at marketing-os-setup and stop |
| A topic was named | Run on it. Still check the queue for an existing question it answers, because closing one is worth more than opening another |
| No topic named | Show the queue. Present the open questions with what each would settle, and let the operator pick |
| The queue is empty | Say so. It is a real and good state. Ask for a topic |
Step 1: build the queue
Read these and assemble one list. This is the step that makes the skill OS-native, and skipping it turns it back into a generic research tool.
| Source | What qualifies |
|---|---|
Intelligence/research/*.md with status: open-question |
A question the OS wrote down deliberately, often with the blocker named. Highest priority, because the file usually already states what would answer it |
Analytics/what-works.md, its open questions |
Claims with some evidence that do not yet support a rule. Each names the specific measurement that would settle it |
Analytics/what-works.md, its untested table |
Beliefs carried from conviction with no evidence at all, each with the measurement it needs |
Channels/{primary}/ideas/*.md with pain: not yet established |
An idea nobody has grounded yet |
Intelligence/market/ briefs, their translation-gap sections |
Something one audience understands and ours does not, which is a research question shaped like a content opportunity |
Intelligence/decisions/ |
Read for exclusions. A question a decision already settled is not open, and re-answering it wastes the run |
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 7d ago First seen · 239 lines · 215 tokens per session scan A 398869cee202
marketing-os-research is a skill published in the GitHub repository naveedharri/benai-skills (61 stars, last pushed yesterday), licensed MIT. It adds 215 tokens to every session and 4,116 once invoked, about $0.0011 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 2026-09-05.
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