Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add indranilbanerjee/digital-marketing-pro/plugin install digital-marketing-proWrote 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/indranilbanerjee/digital-marketing-pro/audience-intelligence)<a href="https://agentmods.dev/skills/indranilbanerjee/digital-marketing-pro/audience-intelligence"><img src="https://agentmods.dev/badge/skills/indranilbanerjee/digital-marketing-pro/audience-intelligence/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/indranilbanerjee/digital-marketing-pro/audience-intelligence"><img src="https://agentmods.dev/badge/skills/indranilbanerjee/digital-marketing-pro/audience-intelligence.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 | $0.00162 | $0.03128 |
| Opus 5 | $0.00081 | $0.01564 |
| Sonnet 5 | $0.00032 | $0.00626 |
| Haiku 4.5 | $0.00016 | $0.00313 |
Grade A, and why
audience-intelligence 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 11d 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 — 177 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Audience Intelligence
When to Use This Skill
Activate this module when the user's request involves any of the following:
- Buyer Persona Creation: Building detailed profiles of ideal customers for marketing and product decisions
- Audience Research: Understanding who a brand's customers or prospects are at a demographic, psychographic, and behavioral level
- Segmentation Strategy: Dividing an audience into meaningful groups for targeted marketing
- Jobs-to-Be-Done (JTBD) Analysis: Identifying the functional, social, and emotional jobs customers hire a product to do
- Psychographic Profiling: Understanding audience values, attitudes, interests, lifestyles, and motivations
- Anti-Persona Definition: Defining who is NOT the target customer to prevent wasted spend
- Audience Sizing & TAM Estimation: Estimating the size of addressable audience segments
Trigger phrases: "buyer persona," "target audience," "who are our customers," "customer profile," "segmentation," "audience segments," "Jobs-to-Be-Done," "JTBD," "psychographic," "ideal customer profile," "ICP," "anti-persona," "lookalike audience," "audience research," "buying committee," "customer avatar"
Brand Context (Auto-Applied)
Before producing any marketing output from this module:
- Check session context — The active brand summary was output at session start. Use the brand name, industry, voice settings, channels, goals, compliance, and competitors shown there.
- If you need the full profile, read:
~/.claude-marketing/brands/{slug}/profile.json - Apply brand voice — Formality, energy, humor, authority levels must shape all content tone and word choices
- Check compliance — Auto-apply rules for brand's target_markets and industry using
skills/context-engine/compliance-rules.md - Reference industry benchmarks — Consult
skills/context-engine/industry-profiles.mdfor the brand's industry - Use platform specs — Reference
skills/context-engine/platform-specs.mdfor character limits and format requirements - Check campaign history — Run
python "${CLAUDE_PLUGIN_ROOT}/scripts/campaign-tracker.py" --brand {slug} --action list-campaignsbefore planning new work - If no brand exists, say: "No brand profile found. Use /digital-marketing-pro:brand-setup to create one, or I can proceed with general best practices."
- Check brand guidelines — If
~/.claude-marketing/brands/{slug}/guidelines/_manifest.jsonexists, load and enforce:restrictions.mdfor banned words, restricted claims, and mandatory disclaimers;channel-styles.mdfor channel-specific tone overrides (may differ from base voice);messaging.mdfor approved key messages, taglines, and positioning language;voice-and-tone.mdfor detailed voice rules beyond the 4 numeric scores. If producing content for a specific channel, channel style rules take precedence over base voice settings.
What ships with it
5 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.
- 11d ago First seen · 177 lines · 162 tokens per session scan A 311b85a7e693
audience-intelligence is a skill published in the GitHub repository indranilbanerjee/digital-marketing-pro (806 stars, last pushed 4d ago), licensed MIT. It adds 162 tokens to every session and 3,128 once invoked, about $0.0008 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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