Audiences

Clay Audiences

Audiences is Clay's CRM-shaped store of people and companies (plus deal records). An audience — also called a saved segment — is a named filter over one entity type. Nothing is copied into it: it selects records live, so its membership changes as records change.

Audiences is the default home for the workspace's own people and companies. When a user mentions people, companies, contacts, leads, accounts, customers, deals, or opportunities without naming a surface, they mean these records — start here, not in the tables entry-point skill (a separate surface, right only when the user names a table) and not in search (net-new prospects that are not in the workspace yet).

Read this before any audiences work. Supporting references:

• answering-data-questions.md — read this first for any request to count, find, or filter existing records. Before filtering a categorical field, call fields list-values as described there. Also covers field coverage and when to enrich.
• queries.md — read before writing a --query. Audiences DSL grammar, relationship semantics, data or date filters, and server-side sorting for top-N questions.
• filters.md — writing the filter AST that defines an audience. Read it before you author or edit a filter.
• custom_objects.md — deals / opportunities. Read it before anything that touches them, including vague rankings such as "largest opportunities", "biggest things in our pipeline", or "recent wins", and GTM phrasings: closed-won, closed-lost, open pipeline, deal stage, deal size / ACV / ARR, close date, forecast, win rate, renewal, expansion, churn, "our customers". Deals are read-only. Use the DSL opportunities root for deal counts and IDs; use people/company roots when the requested results are contacts/accounts associated with those deals.
• The workflows skill's audiences.md — writing values onto records (the upsert-audiences-record action) and triggering a workflow off an audience.

Read with the CLI, write records with the action. clay audiences covers every Audiences primitive — segments, fields, and reading records — but it has no command that writes a field value onto a record. That is the upsert-audiences-record action's job.

Net-new people or companies — not in the workspace yet. When search and routines are available CLI commands, start in the searches skill, then persist via a routine wrapping an upsert workflow (the workflows skill's audiences.md, then the routines skill).

Act on this audience — pull matching records (records search-ids / get). When routines is an available CLI command, run a routine over them (see the routines skill). Ongoing automation (fire when membership changes) is the workflows skill's audiences.md (audience_segment trigger).

One entity type, three spellings

The same entity types are named differently depending on where you are. Get this mapping right up front — it is the most common source of wasted round trips.

Supported --entity-type values by command (all under clay audiences):

Use these exact plural values: person, company, and deal are invalid. Commands not listed above do not take --entity-type; segment-scoped commands use the saved audience's type. CLI output can also carry entityType: "deals". For filtered deal searches, omit --entity-type and use --query with the DSL opportunities root; read custom_objects.md first.

Workflow triggers use the middle spelling: an audience_segment trigger's segmentId is the audience id from clay audiences list, and its entityType is CONTACT / ACCOUNT, not people / companies.

Audiences (saved segments)

Prefer dynamic segments: saved audience filters whose membership updates as records start or stop matching the criteria. Use the user’s request and conversation context to determine the filter. Use a fixed cohort with hard coded matching values only when explicitly requested. Read filters.md before building the filter.

clay audiences list --entity-type people          # id, name, entityType (no filter); 50/page, pass back .cursorclay audiences get <audienceId>                   # same, plus the full filter ASTclay audiences create --entity-type people --name "Missing emails" --filter ./filter.jsonclay audiences update <audienceId> --name "…" --description "…" --filter ./filter.jsonclay audiences archive <audienceId>               # soft delete, idempotent; records untouched

• create and update take --filter as inline JSON, a file path, or - for stdin. Use a file or stdin for anything non-trivial — inline JSON in a shell triggers an approval prompt and invites quoting mistakes.
• update replaces the whole filter; there is no partial merge, and entity type is immutable after creation. Omitted flags are left alone.
• get returns an id-free filter, so clay audiences get <id> | jq .filter pipes straight back into create --filter - to clone an audience.

Fields

Field ids are what record payloads and filter ASTs key on, and they are scoped to an entity type — every subcommand takes --entity-type.

clay audiences fields list --entity-type people                       # every field: id, name, dataType, fieldType, hiddenclay audiences fields list --entity-type people --filter id=email     # narrow by id (repeatable, comma-separated)clay audiences fields list --entity-type people --include-system      # system fields, hidden by defaultclay audiences fields create --entity-type people --name "Lead score" --data-type numberclay audiences fields update <fieldId> --entity-type people --hidden trueclay audiences fields delete <fieldId> --entity-type peopleclay audiences fields segments <fieldId> --entity-type people         # audiences whose filter references the fieldclay audiences fields list --entity-type deals                        # deal field definitionsclay audiences fields list-values title --entity-type people          # distinct values and counts

• Run fields list once and save it (> /tmp/people-fields.json), then slice it with jq. Re-running it to grep, head, and parse the same output three different ways is pure latency.
• fields list returns the workspace record-field catalog (system fields excluded) — use those ids for upsert-audiences-record.
• Names are not ids. Account "company name" is org_name. Never guess an id from a display name.
• Discover categorical values before filtering. Use fields list-values instead of guessing labels. See answering-data-questions.md for value discovery and high-cardinality guidance.
• create silently uniquifies a taken name ("Tier (2)") — read the returned name and id rather than assuming the one you passed.
• Before update --data-type or delete, run fields segments <fieldId>: a delete rewrites saved filters with those clauses removed, changing what those audiences match. Empty data means nothing is affected.
• Default and system fields reject --name and --data-type changes and cannot be deleted; --hidden, --order, and --description still work.

Default field ids

Present in every workspace, so you can write a filter against these without listing fields first:

• people — name, first_name, last_name, email, linkedin_url, phone, title, signal_summary
• companies — org_name, domain, headquarters_location, linkedin_url, sfdc_owner_id, signal_summary, technographics
• deals — see custom_objects.md

signal_summary is derived from the signal events a signal has written onto the record, not something you set — to see which signals feed it, use the signals skill.

Anything else is workspace-defined — get its id from fields list.

Records

clay audiences records search-count --entity-type people --audience-id <id>   # count a scope server-sideclay audiences records search-ids   --entity-type people --audience-id <id>   # matching ids, --limit per page + .cursorclay audiences records get --entity-type people --ids 1,2,3                   # field values, max 100 ids

Scope for both search commands (--query, --audience-id, and --filter are mutually exclusive):

• no scope flag → every record of the entity type
• --query <dsl> → preferred for ad-hoc searches; omit --entity-type (combining them is an error). Use count from ... for search-count and select from ... for search-ids. Read queries.md before constructing the query
• --audience-id <id> → a saved audience's records
• --filter <json|file|-> → AST fallback for unsupported DSL constructs, matching exactly what an audience built from that filter would hold

Add --archived to either to search archived records instead of live ones.

Use search-count for "how many". It counts server-side without fetching every ID. A field's is_not_null query (or NotEmpty AST filter) checks coverage when relying on unfamiliar data or proposing an enrichment. See answering-data-questions.md.

Use search-ids --query 'select from ... order by <field> desc' --limit N for top-N people, companies, or deals. This is the preferred way to answer "Top N" questions: rank server-side and fetch only the N returned IDs for details, without paginating through the full record set or sorting it locally. Before ranking, check the field's dataType with fields list: numeric questions require a number or currency field, not numeric-looking text. If the type is wrong, recommend fixing the Audiences field type before ranking; do not change it without approval. Sorting supports one same-entity text, number (including currency), or date field. Missing values come last. Sorted results have no cursor, even if more records match; they are not a full export. To count the same scope, use count from ... where ... without order by or limit. Read queries.md for grammar, field discovery, limits, and examples.

For filtered deal counts and IDs, use --query with the opportunities root. --entity-type deals without a query still covers the whole population; combining it with --audience-id or a non-empty --filter is a validation error. The activities root supports counts; activity IDs are strings and cannot use search-ids' numeric pagination. See queries.md and custom_objects.md.

search-ids returns ids only — feed them to records get --ids in batches of 100 for field values, keyed by field id (unset fields may be omitted). Records not found are omitted rather than erroring. To size a scope, use search-count, not a paging loop over search-ids.

Budget the walk before you start it

search-count first, then decide whether a full walk fits. Both stages spend the same per-command budgets, and the second one dominates:

• ids — unsorted search-ids pages at --limit ids per call (default 50, max 10,000). Size it from the count: aim for about 10 calls.
• field values — records get takes 100 ids per call and is charged per id against its hourly budget, so the detail pass costs count/100 calls that no batching shrinks. You can only make up to 60 calls per minute.

When that does not fit your data size, narrow the scope instead of grinding through it: tighten the filter — a shorter date window is usually the biggest win, then a single stage, owner, or segment — or read one page and label the answer a sample of that scope. Never start an unbounded paging loop and hope it lands: it spends the workspace's budget and the user gets a stalled turn instead of an answer.

Segment activities

Use clay audiences activities when the user asks what happened inside a saved segment, such as recent email or call activity, campaign touches, source mix, or activity volume over a period.

clay audiences activities get --segment-id audseg_abc --since 2026-08-01 --until 2026-08-20 --activity-types callclay audiences activities summary --segment-id audseg_abc --since 2026-08-01 --until 2026-08-20 --activity-types call --sources CLAY_SEQUENCER

• Use activities get for the raw activity feed. It returns cursor-paginated events for records in the segment.
• Use activities summary for grouped counts by activity type and source when the user asks for totals, trends, or a quick breakdown instead of individual events.
• Bound the first request with --since and, when possible, --until, --activity-types, or --sources. These queries can take a few seconds on large segments, so prefer one targeted request over repeated exploratory calls.
• It is okay to page through activities get with each returned cursor when the user needs the full bounded result set. Continuation requests pass only --cursor and optional --limit; do not repeat time, activity-type, or source filters with a cursor. Do not loop for freshness or repeatedly rerun broad activity queries without narrowing the request.
• Common prompts: "show recent calls for this segment", "summarize email activity since last week", "which sources drove activity for this audience?", "sample the latest campaign activity before I run a workflow".

Signals

Signals write activities onto records.

A signal on an audience — a watch for job changes, new hires, funding news, job postings — stores each captured event as an activity attached to the person or company entity, not as a row anywhere.

Decide whether "signals" means triggers or captured events

Users often call both the watch and each occurrence it captures a "signal". Do not decide from the noun or signal type alone. Use the session context to form a likely interpretation, but do not silently guess. If the user's wording does not explicitly distinguish the two, ascertain their intent with a short, plain-language question before calling either surface: "Do you mean how many JobPost watches are configured, or how many JobPost events were captured?" You may say which reading seems more likely from the conversation, but contextual likelihood is not confirmation. An explicit earlier statement about triggers or events does count as confirmation.

• Use clay signals for trigger definitions when the conversation is at the inventory or configuration level: listing, creating, updating, pausing, resuming, scheduling, checking run status, or choosing a destination. In that context, "how many JobPost signals do I have?" means count JobPost trigger definitions.
• Use clay audiences signals for captured events when the conversation is about results or history: what happened, detections over a period, affected records, an audience/segment, or a trigger's output. In particular, once the session is focused on one specific trigger, the same question — "how many JobPost signals do I have?" — usually means how many events that trigger has captured, not how many trigger definitions exist. Resolve its underlying signal.id with clay signals get when needed, then narrow clay audiences signals summary with --signal-ids.
• After the user confirms captured events, reuse the record or audience/segment and time scope already established in the session. If any required scope is still missing, ask for it rather than inventing one.

Once the intended meaning is clear:

• Signal questions should use signal-specific surfaces. Do not use clay audiences activities commands to answer signal-event questions; those commands read activity rows and can miss signal-specific event detail.
• Read captured events by record or segment. clay audiences signals get returns the full event history for one person or company with --entity-id, or full payloads across a saved segment with --segment-id. The signals summary command groups a segment's counts by signal id and type.
• Filters can select on them. "Companies with a job posting in the last 30 days" is a signal_events predicate — see filters.md, "Filter by signal activity".
• For which signals exist, what one watches, and why one is not producing events, see the signals skill — that is where to start for "is this audience's signal firing?"

clay audiences signals get --entity-id 123 --entity-type people --days-lookback 30clay audiences signals get --segment-id audseg_abc --since 2026-08-01 --until 2026-08-20 --signal-types JobPost,Newsclay audiences signals summary --segment-id audseg_abc --since 2026-08-01 --signal-types JobPost --signal-ids sig_abc

• Use signals get --entity-id when the user names one record. Pass the numeric id from clay audiences records search-ids, its --entity-type, and an explicit --days-lookback. It returns that record's events newest first; continuation calls repeat all three scope flags and pass the returned --cursor.
• Use signals get --segment-id when the event payloads across a saved segment matter. It returns one cursor page with data, timestamps, signal id and type, and matched-entity count.
• Use signals summary for totals and first/last activity times. A summary can cover multiple --signal-types and optionally narrow to underlying sig_… ids with --signal-ids.
• Segment-scoped first-page reads require --since and --signal-types; bound them with --until where possible. Their continuation calls repeat --segment-id but pass only --cursor and optional --limit, without repeating filters.
• --since (inclusive) and --until (exclusive) filter on activityTime — when the underlying event happened — not emittedAt, when the signal event was emitted.
• --signal-ids takes the underlying signal.id shown by clay signals get, not that command's td_… trigger definition id.

Error codes

Every command prints JSON on stdout and a typed error envelope on stderr. Exit codes: 0 ok, 2 validation, 3 auth, 4 rate-limit, 5 network, 6 not-found. 3 on an audiences command usually means Audiences is not enabled for the workspace — that is a workspace-config answer for the user, not something to retry.

4 (rate_limited) means the workspace spent its request budget for that one command. The budget is per command and shared by everyone in the workspace, so a fields list loop cannot starve search-count. It defaults to 60 calls/minute and a workspace can be raised above that, so read details.limit off the error rather than assuming the default.

Sleep details.retryAfter seconds and carry on. A request-rate rejection costs no budget and writes nothing, so repeating the identical call — including a create or update — once the wait is over is safe.

records get carries a second budget: an hourly one charged per record id it returns, which batching does not reduce. Read its 429s more carefully than the rest:

• details.limit is records/hour (default 100,000), not calls/minute, and the message says "Hourly records limit" rather than "Too many requests".
• The rejected call did spend a request token — that one is not refunded — so the wait it asks for can end in a second 429, this time from the per-minute rate. Back off again rather than reading it as a failure.

That budget exists to prevent data exfiltration, so be conservative: before pulling a large set, check whether a sample of records answers the request.