Searches logo

Searches

Organization
clay-run
searches

Clay search — find people or companies in Clay's GTM database with advanced queries and page through the matches. Use when the user wants to search Clay for prospects/accounts, not query an existing table.

Overview

Publisherclay-run
Repositoryagent-plugins
Skill namesearches
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 Searches 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/searches
  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/searches .claude/skills/searches
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Searches 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 Searches 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 Searches 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.

Clay search

Search Clay's GTM database with advanced queries and return matching records — people or companies.

Audiences is the workspace's own people and companies — read and segment what they already have. Search is Clay's GTM database for net-new lists. This is not the tables entry-point skill (querying data already in a table) and not the workflows entry-point skill (automations). Reach for Search when the user wants to find prospects or accounts not in the workspace yet.

How it works

A search is a three-step, forward-only iterator:

  1. Discover the query grammar and queryable fields.
  2. Create the search and receive a searchId.
  3. Run it to pull the next page of records. Repeat while hasMore is true.

There is no cursor: the iterator's position lives server-side and can't be replayed, so each run call returns the records after the previous one.

Before authoring a query, save the reference to a file in your working directory and read it from there — it is about 2,000 lines, far larger than one tool result can hold, so never print it to stdout or read it end to end:

bash
clay searches query-mode reference | jq -r '.reference' > ./clay-search-reference.md
grep -n '^##' ./clay-search-reference.md

The grep lists every section with its line number. Read these sections by line offset, once per conversation:

  1. Grammar, Operators, Where Semantics, and Query mode policy.
  2. Common query guardrails plus the People or Companies query guardrails for the entity you are searching.
  3. Your entity's field catalog in full — People fields and Experience fields for people searches, Companies fields for companies searches. Every field name and every allowed enum value is there; a value that is not listed fails validation.

Read a topic section (Location filtering, Dates/tenure/recency, Company identification, Products and services, Company size and revenue) only when the request needs it, and grep Examples for a field or phrase to see a worked query. Do not re-read sections already in your context. Queries return people or companies only; job-posting criteria are nested filters on those (jobs.exists(...), company.jobs.any(...)). Workspace audience references are covered in the Audiences section below.

Run clay searches --help (and clay searches <cmd> --help) for flags and output shapes. If clay isn't on PATH or clay whoami fails on auth, run the setup skill.

Audiences

The query language supports @audience_segment("segmentId") references; they resolve against the workspace when the search runs. clay searches query-mode reference also serves the Clay UI, so wherever it says to route an audience to a picker ("Resource selection required … in the Clay Search panel"), that applies only to the UI surface. When authoring a query here, resolve the audience yourself:

  • To exclude everything the workspace already has — "exclude my existing contacts", "net-new accounts only", "not already in Clay", "exclude all my audiences" — use the built-in sentinel @audience_segment("ALL"), the whole Audiences dataset for the query's entity: clay.exclude_people_identifiers(@audience_segment("ALL")) or clay.exclude_company_identifiers(@audience_segment("ALL")). No lookup, no segment to create. Only resolve a specific segment when the user names a particular audience.
  • clay audiences list --entity-type people|companies returns segment ids. Contact segments match people queries; Account segments match companies queries. Follow each response's cursor with --cursor until it is absent before matching a name or checking for duplicates. Write the id into @audience_segment("<segmentId>"). The reference's "Workspace resource references" section has the function signatures — e.g. clay.exclude_people_identifiers(@audience_segment("SEGMENT_ID")) to exclude a Contact audience, or clay.filter_to_companies(@audience_segment("SEGMENT_ID")) to target current employers from an Account audience.
  • Never invent a segment id. If the user names an audience you cannot resolve, or several segments match, follow "Choosing a segment" in the audiences skill: list segments, then ask with them as choices.

When a criterion isn't supported

Check the reference before deciding whether it can express the criteria. Do not invent a field or operator that is not in the reference.

If the query can't express a criterion, split the request into what search can do and what a routine does:

  1. Search on the closest available built-in criteria to get a candidate set (e.g. industry, size, or title criteria that approximate the intent).
  2. Feed those results into a saved routine that enriches or scores each record for the attribute the user actually asked about, then filter or act on that routine's output.

Tell the user the field isn't a native search filter and offer this search → routine path rather than returning nothing. See the routines skill and "Next: enrich or persist the results" below for the handoff.

Warn before large generic searches

Before create, if the ask is generic and large — few criteria beyond something like industry + location (e.g. "all tech companies in NYC") — stop and suggest refining first. Offer 2–3 concrete narrowing options search can express (company size, title/seniority, open roles, tech stack, products and services, named companies/domains, or a small result limit). Do not create until they confirm or narrow; continue with the broad query only if they insist. Skip when the ask is already clearly bounded. After they insist, still apply Warn before near-exhaustion below when relevant.

Start a search

bash
clay searches query-mode reference | jq -r '.reference' > ./clay-search-reference.md
clay searches query-mode create --query '<query>'

create returns { "searchId": "search_..." }.

Paging

run returns { "data": [ ... ], "hasMore": <boolean>, "periodQuota"?: { "limit", "used", "remaining", "resetsAt" } }. --limit is the page size. periodQuota appears on successful run responses only — not on create; do not invent values. Reuse the same searchId; each call returns the next page. Continue while hasMore is true, but after every run that returns periodQuota, apply Warn before near-exhaustion below before the next page. Stop when hasMore is false, or when the quota is near exhaustion — unless the user explicitly asks to continue.

bash
clay searches query-mode run <searchId> [--limit <n>]

Warn before near-exhaustion

Keep routine quota checks internal under the shared cost policy in workflows-discover-actions/cost-and-budget.md. Do not announce a quota or seek approval merely because a search consumes results. The verified near-exhaustion condition below is an exception.

When periodQuota is present, before a create or run that will consume N results (the volume you plan to pull, not the full match set), check remaining − N. If that would leave under 15% of limit, stop and ask first:

This search will return {{N}} results and leave {{remaining − N}} of your period quota.

Continue only if they confirm. Otherwise offer a smaller pull that keeps at least 15% remaining, or stop. Skip when periodQuota is absent.

Example: limit 10,000, remaining 2,000, N 1,500 → 500 left (5% of cap) → warn.

Quotas

If a create or run fails with quota_exceeded (exit 1, HTTP 402), the workspace has hit a plan result cap (per-request, per-search, or period) or a credit/usage limit. Short backoff will not help. Read the error message and choose one of:

  1. Per-request size — message names a "per request" limit. This is the only exception: reissue once with --limit ≤ that cap (e.g. free plans often allow 50 per request).
  2. Per-search, period, or credits — anything else. Stop spending allowance: no further run at any --limit (including 1), and no new create to work around a per-search cap. The report is the finished deliverable. Give the short cap account from the error (upgrade, named period reset, or contact support). Always include the plan selector — resolve <workspaceId> with clay whoami | jq -r '.workspace.id' when the error has no URL: https://app.clay.com/workspaces/<workspaceId>/billing/plan-selector (bare URL; prefer a plan-selector URL from the error when present). If the error includes an upgrade URL or upgrade copy, make that selector extremely prominent: near the top of the reply and again as the last line (Upgrade your plan now at: <url>). If the error says to contact support, lead with that path and still add one plan-selector line so they can self-serve if they prefer. Also share the public search docs for follow-up questions: https://developers.clay.com/searches#result-limits. If the user asks to upgrade their plan, open the plan selector link for them. Pull a remainder only if they ask for it in a later message after that report; the original request for N results is not that ask.

validation_error (exit 2) means malformed input (bad flags/filters/query), not a quota. server_error (exit 1) on query-mode create/run can be a transient upstream fault (on run, e.g. the semantic-query embedding service timed out). On create, retry once after a short wait. On run, retry once only when no data came back — a retry re-serves the current page and can charge and count it again if the failure happened after billing. rate_limited (exit 4) is a short HTTP 429 backoff and may be retried after details.retryAfter.

Next: enrich or persist the results

Search only finds records. After paging results, offer one of two plugin paths — both via clay routines runs start. See the routines skill for sizing runs and fetching results.

Enrich without persisting

Prefer Clay-managed routines for standard enrichment. Before reaching for the raw action catalog or building a workflow, list the full, paginated routines set and check source: managed first. Clay ships managed routines that cover most enrichment — e.g. Work Email, Company Domain, Enrich Person, Enrich Person and Find Contact Details, Company Job Openings. Match on each routine's input schema (clay routines get <id>), not its name. Only fall through to the action catalog or a new workflow when no managed or custom routine fits.

bash
clay routines list
bash
clay routines get function:t_abc123

After Search has results, use the routines skill to start the run: list or get the routine schema, then clay routines runs start.

Persist into Audiences

When the user wants Search hits kept in the workspace, find or create a routine whose underlying workflow upserts with upsert-audiences-record. If none exists, build the workflow using the workflows skill's audiences.md, then to run in bulk see the routines skill.

Frequently asked questions

What does the Searches AI skill do?

Clay search — find people or companies in Clay's GTM database with advanced queries and page through the matches. Use when the user wants to search Clay for prospects/accounts, not query an existing table.

Why use Searches on TypingMind?

Because you install it once and use it with any model. Searches 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 Searches in TypingMind?

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

Which AI models can use Searches?

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 Searches?

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

Is the Searches 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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