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 MoizIbnYousaf/marketing-cli --skill audience-researchgit clone --depth 1 https://github.com/MoizIbnYousaf/marketing-cliWrote 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/moizibnyousaf/marketing-cli/audience-research)<a href="https://agentmods.dev/skills/moizibnyousaf/marketing-cli/audience-research"><img src="https://agentmods.dev/badge/skills/moizibnyousaf/marketing-cli/audience-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/moizibnyousaf/marketing-cli/audience-research"><img src="https://agentmods.dev/badge/skills/moizibnyousaf/marketing-cli/audience-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 335 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00142 | $0.03357 |
| Opus 5 | $0.00071 | $0.01679 |
| Sonnet 5 | $0.00028 | $0.00671 |
| Haiku 4.5 | $0.00014 | $0.00336 |
Grade A, and why
audience-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 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 — 409 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Note: Examples below use fictional brands (Acme, Lumi, Helm). Replace with your own brand context.
/audience-research -- Know Who You're Talking To
Every marketing skill gets better when you know your audience. Generic copy happens when you write for "everyone." Specific copy that converts happens when you write for a real person with real problems.
This skill builds that audience profile. It's the foundation - 8 of 11 content
skills read audience.md to shape their output. Running this first makes
everything downstream sharper.
No SaaS tools needed. Systematic research plus web search.
On Activation
- Check if
brand/directory exists in the project root. - If it does, read available files:
voice-profile.md,positioning.md,audience.md,competitors.md,creative-kit.md,stack.md,learnings.md. - Apply any loaded brand context to enhance output quality - skip questions the user has already answered.
- If
brand/does not exist, proceed without it - this skill works standalone.
Iteration Detection
Before starting, check whether ./brand/audience.md already exists.
If audience.md EXISTS --> Update Mode
Do not start from scratch. Instead:
-
Read the existing audience profile.
-
Present a summary of current personas:
EXISTING AUDIENCE PROFILE Last updated {date} by /audience-research Personas: ├── {Persona 1 name} {one-liner} ├── {Persona 2 name} {one-liner} └── Primary: {primary persona name} Watering holes: {N} mapped ────────────────────────────────────────────── What would you like to do? 1. Refine existing personas with fresh research 2. Add a new persona segment 3. Deep-dive into watering holes 4. Full rebuild from scratch -
Process the user's choice:
- Option 1 --> Re-run research for existing personas, update with new data
- Option 2 --> Identify the new segment, build persona, merge into existing file
- Option 3 --> Focus on community mining for existing personas
- Option 4 --> Full process from scratch
What ships with it
1 file 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.
- 9d ago First seen · 409 lines · 142 tokens per session scan A cfe795d8352b
audience-research is a skill published in the GitHub repository MoizIbnYousaf/marketing-cli (31 stars, last pushed 23d ago), licensed MIT. It adds 142 tokens to every session and 3,357 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 2026-08-30.
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