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Audience Intel

Community
davekilleen
audience-intel

Use when synthesizing dated customer conversations, feedback, or behavior evidence into audience or persona insight, especially when sources are numerous, repeated, time-bounded, or disagree.

Overview

Publisherdavekilleen
RepositoryDex
Skill nameaudience-intel
Stars
481
Forks
130
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by davekilleen on GitHub. Read the source before you install it.

Installation

Install the Audience Intel AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/davekilleen/Dex.git /tmp/Dex
mkdir -p .claude/skills
cp -r /tmp/Dex/packages/dex-agent-plugin/skills/_available/marketing/audience-intel .claude/skills/audience-intel
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Audience Intel in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Audience Intel on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Audience Intel is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Audience intelligence

When to use

Use this skill for a defined audience question and a bounded set of customer evidence: conversations, feedback, support notes, research, or behavior records. It can produce an evidence-backed view of persona patterns and messaging implications even when the evidence is incomplete.

Do not use it to invent personas, choose a market, or make a segmentation decision on a human's behalf. Not for outbound targeting, CRM tagging, customer contact, or changing source records without a separately confirmed action.

Inputs and source discipline

  • State the question, audience scope, time-box, timezone, and as-of date/time before searching. A missing date is unknown; never substitute the current date.
  • Keep an evidence ledger with source path or ID, source date, retrieval/as-of date, audience context, exact quote or direct observation, and evidence state. Quote customer language exactly and attach the source and date to every quote.
  • Record the sources searched, excluded, inaccessible, and their date coverage. Treat a source's own timestamp separately from the date the source was retrieved.
  • Time-box the search. When the window or source budget ends, report what was not searched rather than implying complete coverage.
  • Deduplicate only repeated references to the same supported interaction or evidence item. Keep every supporting source reference and do not merge distinct customers or events merely because their wording is similar.

Method

  1. Define the audience question, inclusion rules, time-box, timezone, and as-of boundary.
  2. Gather the permitted sources read-only and build the quote/source/date ledger before clustering anything.
  3. Mark direct statements and behavior as observed. Keep each quote attributable to its source without adding intent, emotion, or demographic detail that was not stated.
  4. Deduplicate the ledger using available source or interaction identifiers, then count unique supported items. Show the deduplication rule and any ambiguous duplicates.
  5. Cluster observed pain points, goals, language, roles, and decision criteria by the persona or segment actually evidenced. Label a persona pattern inferred when it is a synthesis rather than a direct description, and explain which observations support it.
  6. Compare segments and sources. Surface contradictory quotes or behaviors side by side; do not resolve a contradiction by averaging it away.
  7. Separate findings from recommendations. Offer content or research recommendations as proposals, not human decisions, and preview any requested save before asking for confirmation.

Truth and uncertainty rules

  • Observed: directly present in a dated source; cite the source and quote or describe the observation without embellishment.
  • Inferred: a pattern derived from observed items; name the reasoning and confidence.
  • Unknown: not evidenced, not readable, or outside the time-box; do not fill the gap.
  • Stale: evidence is outside the requested period or is superseded by a newer source; retain it for context but do not present it as current.
  • Contradictory: credible sources disagree; show the conflict, dates, and possible scope difference without inventing a cause.

Never invent dates, metrics, percentages, owners, intent, money, status, causes, or evidence. Frequency is a count of documented unique items only; do not turn a small or unknown sample into a population claim.

Output contract

Return a report containing:

  • question, scope, time-box, timezone, as-of date/time, source coverage, and deduplication rule;
  • a quote/source/date ledger or traceable excerpt list;
  • persona patterns with observed and inferred components kept separate, supporting sources, and confidence for each finding;
  • unknowns, stale evidence, contradictory evidence, and coverage limits;
  • recommendations clearly marked as recommendations rather than decisions.

Do not present an unsupported persona label, count, or recommendation as fact. If no reliable evidence supports a requested conclusion, say so explicitly.

Safety and write boundaries

Default to read-only. Do not edit customer notes, profiles, CRM fields, tags, audiences, or outbound systems. A recommendation is not a human decision. For any requested file or system action, show a preview of the exact destination and content/change, obtain explicit confirm from the human authority, then perform only that approved action. Never broaden the source scope or action scope during a write.

Verification and recovery

Before delivery, read back every material finding against its ledger entry and reconcile quoted evidence, unique-item counts, dates, and segment labels. If a source cannot be read, mark the affected item unknown; if a source changes during review, mark the finding stale and re-check it. After an authorized write, read back the destination and reconcile it with the confirmed preview. If a read or write fails, stop, preserve the draft and error details, report the partial state, and ask the human authority whether to retry or recover; never claim success or retry blindly.

Frequently asked questions

What does the Audience Intel AI skill do?

Use when synthesizing dated customer conversations, feedback, or behavior evidence into audience or persona insight, especially when sources are numerous, repeated, time-bounded, or disagree.

Why use Audience Intel on TypingMind?

Because you install it once and use it with any model. Audience Intel is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Audience Intel in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/davekilleen/Dex/tree/main/packages/dex-agent-plugin/skills/_available/marketing/audience-intel. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Audience Intel?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Audience Intel?

As many as you like. As long as a model supports skills, you can use Audience Intel with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Audience Intel AI skill free?

It is published on GitHub by davekilleen. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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