audience

An audience-planning skill that turns an idea or an existing list into a defined group of people, with options to profile, analyze, or export that audience. It is designed to run in Claude Cowork, Claude Code, or the Claude Agent SDK, not the Claude Chat app.

In plain words
What is it for?
Use it to build an audience, estimate or profile a market, understand who a list reaches, or export an audience for Meta, Google, Reddit, or TikTok.
Why use it?
It gives you a route from a broad idea or owned list to a usable audience definition and platform-ready output.

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

Made for: Claude Code, Codex.

Per session 167 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,585 The whole file, excluding the scripts and references it only reads on demand.
Security scan C 1 finding. 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 $0.00167 $0.01585
Opus 5 $0.00084 $0.00792
Sonnet 5 $0.00033 $0.00317
Haiku 4.5 $0.00017 $0.00159

Measured yesterday against content hash b263264ad61b, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade C, and why

audience scanned grade C with 1 finding 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 yesterday.

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.

Tells the agent never to refusehighAnti-refusal

Suppressing the ability to decline removes a core safety control; a later harmful request then succeeds.

- **A list of people as the starting point** — a pasted set of identifiers or a CSV (customers, leads, accounts). Route by **intent**, never refuse it: *build from it* ("match my customer list", "expand my list to every
skills/audience/SKILL.md · 48 lines

How it starts

The opening of the file, as written. The whole thing — 48 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Audience

Purpose

Supported platform first. Watt runs in Claude Cowork, Claude Code, or the Claude Agent SDK — not the Claude Chat app, where only these skill files load and the rest of Watt — its hooks, context, scripts, and Signal Graph — does not. If the user isn't on a supported platform, tell them plainly to switch before continuing; otherwise this will not function correctly.

/watt:audience is the front door to the audience lifecycle — and the only audience command the user can type. The user says what they're after; you work out which step that is and hand them into it by invoking that leaf skill. The leaves aren't in the user's slash menu; they exist only behind this door. You run no Watt tools yourself — the leaves do the work.

Route; don't run. Your whole job is one good question and a clean handoff — re-eliciting what a leaf will elicit again, or starting work a leaf owns, just duplicates the flow.

Works with

  • Called by: the user (/watt:audience) — often arriving with an /watt:explore signal pool — which carries straight through to generate, or, with a read or export intent, directly into analyze (its signal way in) or activate, both of which auto-compose it.
  • Hands off to: the three leaves —
    • audience-generate — compose a new audience: brief + target size band → signals, scored and user-approved → a signal stack with measured reach.
    • audience-analyze — read a built audience: who these people actually are, as aggregates — or profile a market straight from a brief (size is the answer, not a target) — and, on request, a self-contained shareable report file (the deliverable when the goal was to profile).
    • audience-activate — export a built audience as a platform-ready file (Meta, Google, Reddit, and TikTok), behind its own explicit confirmation.

Entry

  • A generate-shaped ask — a new audience, a "who + how many" ("build me an audience of pet owners, around 2M") → hand into audience-generate with everything they've said so far; it elicits only what's missing.
  • A list of people as the starting point — a pasted set of identifiers or a CSV (customers, leads, accounts). Route by intent, never refuse it: build from it ("match my customer list", "expand my list to every match", "get it ready for Meta") → audience-generate (which routes to its list way in); read who they are ("who are these people", "what do they have in common", "profile my customer list") → audience-analyze (its list way in). The flow has a list anchor on both the build and read sides.
  • A profile-shaped ask — "who's in my market", "how many roofers near Nashville", "an audience profile for my client" — understanding a market, not sizing to a budget → audience-analyze (its -search flavor profiles from a brief and writes the shareable report). There's no target to compose toward, so this is a read, not a build.
  • An analyze-shaped ask — "who's actually in it", "what do these people look like" → audience-analyze. If no audience has been built this session and none is supplied — and there's no signal pool to read either — route to generate first; there's nothing to read yet.
  • An activate-shaped ask — "export it", "push it to Meta", "push it to Google", "get me the file" → audience-activate. Same dependency: no built audience and no signal pool → generate first, honestly named (a pool exports directly — activate auto-composes it).
  • A refresh-shaped ask — "refresh my audience", "re-run this", "is this still ~2M?" — usually with a pasted audience record. The record is the recipe and refresh means freeze the expression: the same signals re-run verbatim against today's Signal Graph (it recalculates daily), returning refreshed membership and an updated record — never silently re-picking signals. Route by what they want from the refreshed audience: the updated count and read → audience-analyze (its signal way in); a fresh export → audience-activate. Wanting different signals isn't a refresh — that's running audience-generate again with the brief; name the difference if it's ambiguous.
  • A built audience already in session — offer the next step instead of re-eliciting: "You've got the 2.4M-reach hiker audience — analyze who's in it, or export it for Meta, Google, Reddit, or TikTok?"
  • Bare /watt:audience. One question: "What are you trying to do — build an audience to a size, profile a market (how many, who they are), read one you've built, or export one?"
  • Explore-shaped curiosity — "what's out there for X", no intent to build → /watt:explore, named as the lighter step. Its signal pool carries straight into generate later.

Read the full file on GitHub · 48 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. yesterday First seen · 48 lines · 167 tokens per session scan C b263264ad61b

Subscribe to this mod's changes

audience is a skill published in the GitHub repository wattdata/plugin (5 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 167 tokens to every session and 1,585 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it C with 1 finding (tells the agent never to refuse). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Related

Other skills, from other repositories

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

next-cache-components-adoption

Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…

vercel/next.js · 95 tokens

babysit-pr

Babysit a GitHub pull request after creation by continuously polling review comments, CI checks/workflow runs, and mergeability state until the PR is merged/closed or user help is required. Diagnose failures, retry likely flaky failures up to 3 times, auto-fix/push branch-related issues when appropriate, and keep…

openai/codex · 114 tokens

imagegen

Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output…

openai/codex · 113 tokens

cpu-profile-analysis

Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…

microsoft/vscode · 71 tokens

next-cache-components-optimizer

Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…

vercel/next.js · 170 tokens