audience-analyze-signal

An audience analysis tool for when you already have audience signals, such as traits, interests, or other defining data. It builds the audience from those supplied signals and produces a summary of who it reaches.

In plain words
What is it for?
Use it to analyze a signal set from an audience-building step, an exploration pool, a saved audience record, or signals you name yourself.
Why use it?
It avoids repeating audience discovery when the relevant signals are already available.

Skill for Claude CodeCodex

Part of the watt plugin — 12 skills, 2 hooks, 1 MCP server shipped together

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

Made for: Claude Code, Codex.

Or install watt, the plugin that ships this one along with the rest of its 12 skills, 2 hooks, 1 MCP server.

Per session 132 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,567 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 $0.00132 $0.01567
Opus 5 $0.00066 $0.00783
Sonnet 5 $0.00026 $0.00313
Haiku 4.5 $0.00013 $0.00157

Measured 3d ago against content hash 5348d0755610, 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-signal 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 3d 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-signal/SKILL.md · 61 lines

How it starts

The opening of the file, as written. The whole thing — 61 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Analyze an audience from its signals

Purpose

audience-analyze-signal is the way into the read when the signals already exist — a signal stack fresh from audience-generate, an /watt:explore signal pool, a pasted audience record from a past session, or signals the user names. There's no discovery and no pivot loop: take the signals as given, materialize them, and hand the built stack to the shared read.

This is a delta over audience-analyze: the unique work here is just resolving the supplied signals into a built signal stack; 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 a built audience is in session or the user supplied its signals.
  • Runs inline:
    • name resolution (only when the user supplied bare names) — resolve each named signal to its verified trait inline, following the discovery procedure (context/discovery.md), driving from meaning. Never used to discover more signals — the user's set is the set.
  • Runs the read inline — the profiling procedure (context/profiling.md) in mode A (the 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). The role column in an audience record — defining (any-of) / must-have (all-of) / exclusion (none-of) — carries the boolean shape exactly; the operator never sees AND/OR/NOT.

The flow

1 — Take the signals as given

  • A built audience in session (fresh from generate, or composed in audience-analyze-search) — use it directly. Confirm which one in a word if there's any doubt.
  • A re-supplied audience record — read from the saved record file in the working directory, pasted in, or compacted in context — the role column (defining / must-have / exclusion) carries the expression exactly as built, and names ride beside hashes; take it as the stack. A past session's reach/headcount is "measured then", not re-measured silently. On a refresh-shaped ask ("refresh this", "is it still ~2M?"), the read's fresh materialization is the re-measure: after the read, re-write the audience record per the record contract (context/record.md) with today's measured reach against the header's original target (reach 2.1M (band 1M–5M) · refreshed), location and roles unchanged.
  • A signal pool (an /watt:explore session's kept signals, or a lookalike pool) — auto-compose it to the default stack: signals the pool marks must-have go all-of, its exclusions none-of, everything else any-of (one OR union). If the pool carries no role markers at all, ask once"any must-haves or must-have-nots in here, or read them all as one group?" — then build. This is a deterministic reading of the user's picks, the same way a record's role column reconstructs an expression — never a strategy compose; refining the pool into a tuned stack is audience-generate's lane, offered if the read shows it's wanted.
  • Signals the user names, no hashes — the leaf cannot fabricate a hash. Resolve each name to its verified trait inline, following the discovery procedure (context/discovery.md) — driving from meaning — surface the matches for a one-touch confirm, and only then build the stack. Never pass a name off as a hash, never guess one.

Read the full file on GitHub · 61 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. 3d ago First seen · 61 lines · 132 tokens per session scan A 5348d0755610

Subscribe to this mod's changes

audience-analyze-signal is a skill published in the GitHub repository wattdata/plugin (5 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 132 tokens to every session and 1,567 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 2026-08-31.

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