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.
npx agentmods add skills/wattdata/plugin/audience-analyze-signalnpx skills add wattdata/plugin --skill audience-analyze-signalgit clone --depth 1 https://github.com/wattdata/pluginWhat 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 | $0.00132 | $0.01567 |
| Opus 5 | $0.00066 | $0.00783 |
| Sonnet 5 | $0.00026 | $0.00313 |
| Haiku 4.5 | $0.00013 | $0.00157 |
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.
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-analyze → The read & report), composed with verbatim — not restated.
Works with
- Called by: the
audience-analyzerouter, 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.
- name resolution (only when the user supplied bare names) — resolve each named signal to its verified trait inline, following the discovery procedure (
- 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
rolecolumn (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:exploresession'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'srolecolumn reconstructs an expression — never a strategy compose; refining the pool into a tuned stack isaudience-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.
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.
- 3d ago First seen · 61 lines · 132 tokens per session scan A 5348d0755610
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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