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audience-analyze-list

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UpdatedJuly 6, 2026 at 13:12

Read who a supplied list of people is — take a list of people as identifiers (emails, phones, names, addresses, inline or as a CSV) to resolve, or as already-resolved entity IDs (a roster from grouping, or a pasted entity-ID set), and render the discovered half of the read (the traits that define them against the world by lift, plus segmentation). No specified-signals section — the user gave people, not signals. The list way into audience-analyze, behind /watt:audience. Aggregates only — never individual records, never an export. Not a user command. Use when the user brings a list of people and asks who they are — "profile my customer list", "what do these people have in common", "read this roster's groups".

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SKILL.md

readonly

name	audience-analyze-list
description	Read who a supplied list of people is — take a list of people as identifiers (emails, phones, names, addresses, inline or as a CSV) to resolve, or as already-resolved entity IDs (a roster from grouping, or a pasted entity-ID set), and render the discovered half of the read (the traits that define them against the world by lift, plus segmentation). No specified-signals section — the user gave people, not signals. The list way into audience-analyze, behind /watt:audience. Aggregates only — never individual records, never an export. Not a user command. Use when the user brings a list of people and asks who they are — "profile my customer list", "what do these people have in common", "read this roster's groups".
user-invocable	false
compatibility	Designed for Claude Cowork, Claude Code, or Agent SDK. Requires Signal Graph.

Analyze a supplied list of people

Purpose

audience-analyze-list is the way into the read when the user brings a list of people, not signals. The list arrives in one of two forms, and they differ only in whether resolution is needed:

identifiers — a customer CSV, a pasted set of emails/phones/names/addresses → resolve them to Watt entities first.
entity IDs — a roster from the grouping objective, or any pre-resolved entity-ID set the user holds → already entities; skip resolution and read them directly.

Either way, because no signals were specified, there is no your-signals half — only the discovered read: the traits that define these people against the world by lift, plus per-domain segmentation.

This is a delta over audience-analyze: the unique work here is getting the list to a chainable entity set — resolving identifiers, or taking an entity-ID set as-is; the discovered read and the shareable report are the parent's shared procedure (audience-analyze → The read & report), composed with verbatim — discovered-only.

Works with

Called by: the audience-analyze router, when the user supplied a list of people — a CSV/identifiers to resolve, or a pre-resolved entity-ID set (a roster from grouping).
Runs inline:
- the resolution procedure (context/resolution.md) (identifiers only) — the supplied identifiers → a workflow:// entity-IDs URI plus the counts (identifiers submitted, entities resolved). The resolve runs inline but stays IDs-only — the leaf handles only the URI and counts, never a contact record. Skipped entirely when the list is already entity IDs — there is nothing to resolve.
- the profiling procedure — mode B (the entity-IDs URI, no signals) → the discovered-only read. The parent's shared read, run inline (context/profiling.md).

Language

Inherits the parent's table (lift explained once; sample named). The headcount here is a count of the people in hand, not a market total: "N people resolved from your list" for identifiers, or "N people in the set" / "N in this group" for an entity-ID set or roster.

The flow

1 — Get the list to an entity-ID set

Two input shapes; the only difference is whether resolution is needed.

Identifiers (a pasted set, or a CSV — uploaded or to upload). Name the identifier types present (email, phone, name, address); addresses need explicit handling, so call them out. Run the resolution procedure inline (context/resolution.md) with the identifiers (inline) or the csv_resource_uri, the entity_type, and a workflow_id. It matches them, dedupes, and returns the entity_ids_uri plus the counts — input_identifier_count and resolved_count (and below_floor_count). Narrate them plainly ("5,000 identifiers in, 4,200 people resolved — reading those"). The resolve runs inline but stays IDs-only — the leaf handles only the URI and counts, never a contact record.
Entity IDs already (a roster's roster_uri from grouping, or a pasted entity-ID set). These are the resolved entities — skip the resolution procedure entirely; take the URI as-is and the count of IDs in it. For a roster, the user can read the whole set (the roster_uri) or a single group (its entity_ids_uri); the group_label classification is what lets a per-group read be a separate inline run.

2 — Run the shared read (discovered-only)

Run the parent's shared read & report (audience-analyze → The read & report): run the profiling procedure inline (context/profiling.md) in mode B with the entity_ids_uri (the resolved set, or the roster/group URI taken as-is), the headcount, and the workflow_id — no signals, so the read is discovered-only. Render the dashboard with the discovered half (defining traits by lift, segmentation, the intent panel by reach); there is no your-signals section. Offer the shareable report — built with --no-specified so it drops Section 1 — and the next step (for a roster, a per-group read is the natural deeper cut, one mode-B inline run per group).

How to behave

The list is people, not signals — so the read is discovered-only, and the dashboard says so plainly rather than showing an empty your-signals section.
Aggregates only, and IDs never become records here. The resolution procedure returns entity IDs; the profiling procedure reads aggregates over them. No name, email, address, or contact data is ever pulled, shown, or written — turning IDs back into people is audience-activate's lane, behind its own confirmation.
Honest about the match. For identifiers, both counts are on screen — identifiers submitted vs. people resolved — and the read covers who matched, not everything uploaded; say so. For an entity-ID set or roster there's no match step (they're already entities) — the count is the set size, and for a roster the pruned/coverage from the build still applies.
Narrate the inline run and report the return in one plain line each — counts, never identifier values or records.
US-only, adults-only, person audiences only.

Refuse cleanly

"Enrich my list / give me their emails and phones." That's enrichment/export — audience-activate's lane, behind its confirmation. This leaf reads the list as aggregates only.
"Show me which of my people matched." Aggregates only — the read is over the matched set as a whole, not a per-row report; the counts say how many matched.
A brief instead of a list ("people interested in X") → that's audience-analyze-search (discover signals) — route there.

Failure modes

The resolution procedure matches few or none (bad identifiers, wrong column mapping) — surface the counts (how few of the submitted identifiers resolved) and the likely cause; never read an empty or near-empty set as if it were the list.
The profiling procedure errors mid-read. Surface where it stopped and what completed; never fill a panel with plausible numbers.

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name	audience-analyze-list
description	Read who a supplied list of people is — take a list of people as identifiers (emails, phones, names, addresses, inline or as a CSV) to resolve, or as already-resolved entity IDs (a roster from grouping, or a pasted entity-ID set), and render the discovered half of the read (the traits that define them against the world by lift, plus segmentation). No specified-signals section — the user gave people, not signals. The list way into audience-analyze, behind /watt:audience. Aggregates only — never individual records, never an export. Not a user command. Use when the user brings a list of people and asks who they are — "profile my customer list", "what do these people have in common", "read this roster's groups".
user-invocable	false
compatibility	Designed for Claude Cowork, Claude Code, or Agent SDK. Requires Signal Graph.

Analyze a supplied list of people

Purpose

audience-analyze-list is the way into the read when the user brings a list of people, not signals. The list arrives in one of two forms, and they differ only in whether resolution is needed:

identifiers — a customer CSV, a pasted set of emails/phones/names/addresses → resolve them to Watt entities first.
entity IDs — a roster from the grouping objective, or any pre-resolved entity-ID set the user holds → already entities; skip resolution and read them directly.

Works with

Called by: the audience-analyze router, when the user supplied a list of people — a CSV/identifiers to resolve, or a pre-resolved entity-ID set (a roster from grouping).
Runs inline:
- the resolution procedure (context/resolution.md) (identifiers only) — the supplied identifiers → a workflow:// entity-IDs URI plus the counts (identifiers submitted, entities resolved). The resolve runs inline but stays IDs-only — the leaf handles only the URI and counts, never a contact record. Skipped entirely when the list is already entity IDs — there is nothing to resolve.
- the profiling procedure — mode B (the entity-IDs URI, no signals) → the discovered-only read. The parent's shared read, run inline (context/profiling.md).

Language

The flow

1 — Get the list to an entity-ID set

Two input shapes; the only difference is whether resolution is needed.

Identifiers (a pasted set, or a CSV — uploaded or to upload). Name the identifier types present (email, phone, name, address); addresses need explicit handling, so call them out. Run the resolution procedure inline (context/resolution.md) with the identifiers (inline) or the csv_resource_uri, the entity_type, and a workflow_id. It matches them, dedupes, and returns the entity_ids_uri plus the counts — input_identifier_count and resolved_count (and below_floor_count). Narrate them plainly ("5,000 identifiers in, 4,200 people resolved — reading those"). The resolve runs inline but stays IDs-only — the leaf handles only the URI and counts, never a contact record.
Entity IDs already (a roster's roster_uri from grouping, or a pasted entity-ID set). These are the resolved entities — skip the resolution procedure entirely; take the URI as-is and the count of IDs in it. For a roster, the user can read the whole set (the roster_uri) or a single group (its entity_ids_uri); the group_label classification is what lets a per-group read be a separate inline run.

2 — Run the shared read (discovered-only)

How to behave

The list is people, not signals — so the read is discovered-only, and the dashboard says so plainly rather than showing an empty your-signals section.
Aggregates only, and IDs never become records here. The resolution procedure returns entity IDs; the profiling procedure reads aggregates over them. No name, email, address, or contact data is ever pulled, shown, or written — turning IDs back into people is audience-activate's lane, behind its own confirmation.
Honest about the match. For identifiers, both counts are on screen — identifiers submitted vs. people resolved — and the read covers who matched, not everything uploaded; say so. For an entity-ID set or roster there's no match step (they're already entities) — the count is the set size, and for a roster the pruned/coverage from the build still applies.
Narrate the inline run and report the return in one plain line each — counts, never identifier values or records.
US-only, adults-only, person audiences only.

Refuse cleanly

"Enrich my list / give me their emails and phones." That's enrichment/export — audience-activate's lane, behind its confirmation. This leaf reads the list as aggregates only.
"Show me which of my people matched." Aggregates only — the read is over the matched set as a whole, not a per-row report; the counts say how many matched.
A brief instead of a list ("people interested in X") → that's audience-analyze-search (discover signals) — route there.

Failure modes

The resolution procedure matches few or none (bad identifiers, wrong column mapping) — surface the counts (how few of the submitted identifiers resolved) and the likely cause; never read an empty or near-empty set as if it were the list.
The profiling procedure errors mid-read. Surface where it stopped and what completed; never fill a panel with plausible numbers.