audience-analyze-search

An audience analysis workflow that starts with a plain-English description of a market, such as customers for a type of service in a location. It discovers relevant signals, builds the audience, and summarizes what the resulting group looks like.

In plain words
What is it for?
Use it to discover and analyze a market from a written brief, including its defining traits and aggregate audience profile.
Why use it?
It helps when you know the audience you want to understand but do not already have the data signals needed to define it.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/wattdata/plugin/audience-analyze-search
Any agent
npx skills add wattdata/plugin --skill audience-analyze-search
Clone the repo
git clone --depth 1 https://github.com/wattdata/plugin

Made for: Claude Code, Codex.

Per session 105 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,085 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00105 $0.02085
Opus 5 $0.00053 $0.01043
Sonnet 5 $0.00021 $0.00417
Haiku 4.5 $0.00011 $0.00209

Measured 2d ago against content hash 3120c3cad03f, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

audience-analyze-search 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 2d 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.

skills/audience-analyze-search/SKILL.md · 81 lines

How it starts

The opening of the file, as written. The whole thing — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Analyze a market from a brief

Purpose

audience-analyze-search is the way into the read for a user who arrives with a brief, not signals — "who's in the market for roofing near Nashville", "profile this audience". It discovers the signals behind the brief, organizes them into three pools, lets the operator steer which signals land in which pool, materializes the audience, and hands it to the shared read. Size is an output, never a target — there's no band and no strategy procedure; the headcount is whatever the composed signals land at.

This is a delta over audience-analyze: the unique work here is getting from a brief to a built signal stack; once that stack exists, the read and the shareable report are the parent's shared procedure (audience-analyzeThe read & report), composed with verbatim — not restated.

Works with

  • Called by: the audience-analyze router, when the user brought a brief and no signals.
  • Runs inline (the same discovery and scoring explore uses, here feeding a built-and-read audience):
    • signal discovery — one concept per beat: a pool's concept (in the user's phrasing, tagged with its role) → validated candidate signals with evidence (context/discovery.md).
    • signal scoring — the gathered signals scored against the model (relevance · freshness · rarity/specificity · breadth/size · coverage), grounded on the brief, so the operator can see how each stacks up and curate — traits-only, never a set of people (context/scoring.md).
    • adjacencies (optional, at a pivot checkpoint) — adjacent concepts / unprobed domains worth adding to a pool (context/adjacencies.md).
  • Runs the read inline — the profiling procedure (context/profiling.md) in mode A (the built stack) → the two-section read; the parent's shared read, run inline on the main thread.

Language

Inherits the parent's table (signals / must-haves / exclusions; lift explained once; sample named). Internally the three pools map to the boolean shape — defining → OR (core), must-have → AND (must_have), exclusion → AND_NOT (exclusion) — but the operator only ever sees defining signals, must-haves, exclusions. No AND/OR/NOT, no boolean-"pools" jargon at the surface (signal pool, the kept-signals carrier, is the user's word and fine).

Read the full file on GitHub · 81 lines

Changes

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.

  1. 2d ago First seen · 81 lines · 105 tokens per session scan A 3120c3cad03f

Subscribe to this mod's changes

audience-analyze-search is a skill published in the GitHub repository wattdata/plugin (5 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 105 tokens to every session and 2,085 once invoked, about $0.0005 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.

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