Workflows Audience Enrichment logo

Workflows Audience Enrichment

Organization
clay-run
workflows-audience-enrichment

Clay workflows — build enrichment steps and map their results back to Audiences in an existing audience enrichment workflow. Use while editing an audience enrichment workflow or migrating an Audience-backed bulk enrichment table into a workflow.

Overview

Publisherclay-run
Repositoryagent-plugins
Skill nameworkflows-audience-enrichment
Stars
145
Forks
24
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by clay-run on GitHub. Read the source before you install it.

Installation

Install the Workflows Audience Enrichment AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/clay-run/agent-plugins/tree/main/clay/skills/workflows-audience-enrichment
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/clay-run/agent-plugins.git /tmp/agent-plugins
mkdir -p .claude/skills
cp -r /tmp/agent-plugins/clay/skills/workflows-audience-enrichment .claude/skills/workflows-audience-enrichment
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Workflows Audience Enrichment in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Workflows Audience Enrichment on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Workflows Audience Enrichment is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Build an audience enrichment workflow

Use this skill for an existing audience_enrichment workflow or an explicitly requested Audience-backed bulk enrichment migration. It owns the workflow-specific sequence: build enrichment steps, ensure the shared final Audiences writeback, and map enrichment results onto audience fields.

Read the complete workflows and audiences skills first. Read /workflows-discover-actions when choosing provider actions, and read the workflows skill's audiences.md before mapping the writeback.

Verify the workflow type

Start every task with:

bash
clay workflows get <workflowId>

Continue when .type is exactly audience_enrichment. For a regular workflow (null or absent type), convert only as part of an authorized Audience bulk enrichment migration, using the procedure below. For other tasks or workflow types, return to /workflows. Never convert or reclassify a workflow implicitly.

Plan the enrichment

Read the full graph and identify the audience trigger, existing enrichment steps, and current writeback node. Resolve the following from the workflow and user instructions, asking only where a choice remains:

  • which audience fields should be populated
  • whether the workflow should enrich people or companies
  • whether the requested fields are actually missing or sparse
  • the action or function to use when the workspace catalog offers consequential alternatives
  • the audience size before testing or publishing

Use the audiences skill to inspect field fill rates and count matching records. Prefer direct enrichment actions for provider data, Clay functions for reusable workspace logic, agents for unstructured research or classification, conditionals for eligibility and fallbacks, and code for deterministic transformations.

Map unambiguous existing fields without asking whether to use them or create new ones. A request to update a field does not need another confirmation because it may already have values; do not add a fill-blanks-only restriction unless requested. For ambiguous mappings, offer plausible fields and a new-field option. Create needed fields without asking permission, following the workflows skill's audiences.md. Reuse field choices already made.

Present a short plan and get approval before editing the graph unless the user already authorized the edits. Get separate approval before publishing. Testing must be within the authorized scope; follow the shared policy in workflows-discover-actions/cost-and-budget.md for cost disclosure and significant-spend confirmation.

Build the enrichment path

Migrating an Audience bulk enrichment table

If the destination is a regular workflow, convert it with clay workflows update <workflowId> --type audience_enrichment once the user has authorized the migration. Then create draft Audience triggers matching the table's source segments and entity type. Conversion alone does not create triggers or run enrichment.

Read the table's full column settings and the destination trigger's output schema before creating nodes. Preserve the table's enrichment behavior while using the existing audience trigger as the source of the original record ID and fields:

  • Replace a lookup that only reloads the triggering Audience record, and columns that only extract its fields, with direct trigger bindings. Keep formulas that transform those fields.
  • Only carry over a related-company lookup when a downstream enrichment or writeback uses its output and that value is not already available from the trigger. Preserve user lookups that retrieve different records or implement additional filtering.
  • Pin each required value on the consuming code or tool node using an inputSchema property with the trigger's sourceNodeId and the exact sourcePath from its output schema. For example, a trigger exposing fields.Name supplies sourcePath: "$.fields.Name"; this may differ from the Audience field ID name. A nested expression like {{fields.id}} does not replace an explicit upstream binding. Read /workflows's data-passing.md for the writable shape.
  • In code, read the pinned input by its property name with context.get_input. On the final writeback, pin the original record ID from the trigger and the transformed value from the enrichment node, then reference those input names in inputMappingConfig. Read each node back and confirm its source bindings survived before running.

Use the shared writeback procedure below for the table's final Audience update. Do not copy the table's source-record lookup or legacy writeback action into a separate update path.

Build from the existing audience trigger. Preserve the trigger and any existing upsert-audiences-record node.

Use clay workflows actions list and clay workflows actions schema to choose and configure each enrichment. Never guess action input names, output paths, credentials, or provider availability. Put cheap eligibility checks before paid actions. When an enrichment can miss, use the user's chosen fallback or conditional path rather than silently writing a blank value.

Use the general workflows skill for node creation, insertion, data passing, and branch wiring. Do not manually create or reconnect the final audience writeback while building intermediate steps.

Ensure and configure the final writeback

After the enrichment graph is structurally complete, run:

bash
clay workflows ensure-audience-writeback <workflowId>

This command calls the same server-side helper as the audience enrichment editor. It creates or reuses exactly one upsert-audiences-record node, validates its entity type, and reconnects every eligible terminal route. Do not reproduce that topology change with manual node or edge edits.

Read the returned node before updating it. Preserve its tool identity and static entityType, then configure its inputMappingConfig with clay workflows nodes update using the writable shape from clay workflows nodes get.

Discover destination fields with:

bash
clay audiences fields list --entity-type people
clay audiences fields list --entity-type companies

Follow the workflows skill's audiences.md for the complete mapping shape. In particular:

  • set lookupFields|selectedLookupFields to the static array ["id"]
  • bind lookupFields|id to the audience trigger record id: use $.fields.id when the trigger output schema contains a fields object, otherwise use $.id; inspect the trigger output schema and never guess the path
  • do not replace the record-id lookup with email, LinkedIn URL, phone, or domain; those aliases are for generic or net-new audience upserts where an existing audience record id is unavailable
  • include every destination id in recordFields|selectedRecordFields and provide its matching recordFields|<id> binding
  • set recordFields|removeNullValues to true unless the user explicitly wants blanks to clear existing values
  • bind enrichment outputs from their declared $.result.<outputPath> paths; never invent paths

Read the writeback node again after updating it and confirm the mappings persisted.

Validate, test, and publish

Finish an audience enrichment workflow in this order:

  1. Run clay workflows ensure-audience-writeback <workflowId>, read the returned node, and confirm its mappings persisted.

  2. Validate the graph with the available workflow tools and show the resulting diagram.

  3. Resolve the segment id from the workflow's existing audience_segment trigger. Do not search for audience entities or records to decide what to publish or run. If the workflow has no audience_segment trigger, stop this completion sequence and use the general workflows testing guidance; do not alter its triggers.

  4. Explain the likely credit exposure and get approval for a credit-consuming test.

  5. Test 10 segment records from the current draft by default:

    bash
    clay workflows runs test <workflowId> --audience-segment <segmentId> --limit 10

    Use a different limit only when the user requests or approves it.

  6. Wait for the run to finish. Inspect both the enrichment output and the final Audiences update. If mappings need correction, correct and validate them, then return to step 4 and get renewed approval before another credit-consuming test.

  7. Get separate approval, then publish the tested draft with clay workflows publish <workflowId>.

When the user asks to publish and run an audience_segment workflow, publish first, then test 10 records from the version just published with --audience-segment <segmentId> --limit 10 --live. Do not substitute an entity lookup, a record search, or a plain manual run for the requested segment run. Later edits remain draft-only until the workflow is published again.

Frequently asked questions

What does the Workflows Audience Enrichment AI skill do?

Clay workflows — build enrichment steps and map their results back to Audiences in an existing audience enrichment workflow. Use while editing an audience enrichment workflow or migrating an Audience-backed bulk enrichment table into a workflow.

Why use Workflows Audience Enrichment on TypingMind?

Because you install it once and use it with any model. Workflows Audience Enrichment is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Workflows Audience Enrichment in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/clay-run/agent-plugins/tree/main/clay/skills/workflows-audience-enrichment. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Workflows Audience Enrichment?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Workflows Audience Enrichment?

As many as you like. As long as a model supports skills, you can use Workflows Audience Enrichment with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Workflows Audience Enrichment AI skill free?

It is published on GitHub by clay-run. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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