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---
name: account-contacts
description: "Find and rank the right people at one target company: a contact shortlist for outbound, or a buying-committee map showing economic buyers, technical evaluators, influencers and coverage gaps. Use for 'get the HR leadership at miro.com', 'who are the decision-makers at Acme', 'map the buying committee at Globex', 'contacts at Stripe for a security pitch', 'who owns this at the account'. Not for finding people across many companies (use list-builder) or for looking up a named person (use enrich-contact)."
---

# Account Contacts

Pull the right people at one company, check how reachable they are, and say who to engage first.

## Connector rules

Use only the AgentSource tools in your tool list. Each tool's live description and input schema are authoritative; if this skill disagrees with a tool, follow the tool.

- **`tool_reasoning`** is required on every call: the user's original request in their exact words (up to 2000 characters), reused unchanged across chained calls for the same task. Never replace it with a per-step rationale or add personal data the user did not write.
- **Sessions:** reuse `session_id` from the previous response while continuing the same request; omit it to start a new request. Never invent one.
- **Chaining by ID:** pass `business_id` and `prospect_id` values from `match-business`, `match-prospects` or `fetch-entities` results. Do not re-match entities that a fetch already returned.
- **`autocomplete` first**, one concept per call, for `linkedin_category`, `naics_category`, `company_tech_stack_tech`, `job_title`, `interests`, `skills`, `business_intent_topics`, and US `city_region`. Use the returned values as-is. Use ISO codes directly for countries (`US`) and regions (`US-CA`).
- **What you see:** every tool returns full results inline. `fetch-entities` returns up to `page_size` rows per call (at most 500) and a `next_cursor` for the next page; `fetch-entities-statistics` returns aggregates over every match.
- **Credits:** every data call uses credits on the user's AgentSource account. Request only the rows and enrichments the task needs, count with `fetch-entities-statistics` before a large pull, and confirm with the user before fetching more than 100 rows or enriching contact details for more than 50 people.
- **Limits per call:** `match-business` 50 companies, `match-prospects` 40 people, `enrich-business` and `enrich-prospects` 50 IDs, `fetch-businesses-events` 20 companies. Name several enrichment types in one `enrichments` list rather than one call each.
- **Presenting:** show returned records in a table with the fields that matter for the task. Never mention session IDs or internal table names.
- **Event details:** if a fetch used the `events` filter and the user did not already ask for event details, ask before calling `fetch-businesses-events` or `fetch-prospects-events`.
- **Lists:** work from lists pasted in chat or text from attached files, in batches that respect the limits above.

## Input

- **Company** (required): a domain (preferred) or a name.
- **Use case:** prospecting (default), deal acceleration, or renewal and expansion. For a buying-committee request, add the purpose: deal stage, who is already engaged, who is missing, and any hypothesis ("find the economic buyer").
- **Focus (optional):** a function (HR, security, finance, RevOps) or seniority (C-level, VP and above, Director and above), and how many people (default 25, up to 100).

If only a bare company is given, ask once what the contacts are for; if the user declines, build a general prospecting shortlist and say so.

## Workflow

1. **Match the company** with `match-business`. A big brand that resolves to 1-50 employees under "Corporate Managing Offices" probably hit a shell entity, so retry with the other domain. If several companies match, show the top 2-3 (industry, size, HQ) and ask.
2. **Turn the use case into a persona blueprint** before fetching:
   - *Prospecting:* broad cov

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---
name: account-research
description: "Build a purpose-driven intelligence brief on one target company: firmographics, funding, hiring and workforce trends, tech stack, recent business events, and peers. Use for pre-call research, renewal or QBR prep, competitive evaluation, and outbound targeting, for example 'research stripe.com for outbound to finance leaders', 'account brief on Snowflake', 'profile this company before my call'. Not for a quick lookup of one data point (use enrich-company), a comparison of several rivals (use competitor-research), or a meeting agenda with attendee profiles (use meeting-prep)."
---

# Account Research

A brief on one company, framed around why the user needs it.

## Connector rules

Use only the AgentSource tools in your tool list. Each tool's live description and input schema are authoritative; if this skill disagrees with a tool, follow the tool.

- **`tool_reasoning`** is required on every call: the user's original request in their exact words (up to 2000 characters), reused unchanged across chained calls for the same task. Never replace it with a per-step rationale or add personal data the user did not write.
- **Sessions:** reuse `session_id` from the previous response while continuing the same request; omit it to start a new request. Never invent one.
- **Chaining by ID:** pass `business_id` and `prospect_id` values from `match-business`, `match-prospects` or `fetch-entities` results. Do not re-match entities that a fetch already returned.
- **`autocomplete` first**, one concept per call, for `linkedin_category`, `naics_category`, `company_tech_stack_tech`, `job_title`, `interests`, `skills`, `business_intent_topics`, and US `city_region`. Use the returned values as-is. Use ISO codes directly for countries (`US`) and regions (`US-CA`).
- **What you see:** every tool returns full results inline. `fetch-entities` returns up to `page_size` rows per call (at most 500) and a `next_cursor` for the next page; `fetch-entities-statistics` returns aggregates over every match.
- **Credits:** every data call uses credits on the user's AgentSource account. Request only the rows and enrichments the task needs, count with `fetch-entities-statistics` before a large pull, and confirm with the user before fetching more than 100 rows or enriching contact details for more than 50 people.
- **Limits per call:** `match-business` 50 companies, `match-prospects` 40 people, `enrich-business` and `enrich-prospects` 50 IDs, `fetch-businesses-events` 20 companies. Name several enrichment types in one `enrichments` list rather than one call each.
- **Presenting:** show returned records in a table with the fields that matter for the task. Never mention session IDs or internal table names.
- **Event details:** if a fetch used the `events` filter and the user did not already ask for event details, ask before calling `fetch-businesses-events` or `fetch-prospects-events`.
- **Lists:** work from lists pasted in chat or text from attached files, in batches that respect the limits above.

## Input

- **Company** (required): a name or domain.
- **Purpose** (strongly recommended): one sentence on why the brief is needed and what decision it supports (renewal risk, competitive evaluation against Acme, outbound to finance leaders). If it is missing, ask once; if the user declines, produce general account intelligence and say so.

## Workflow

1. **Anchor on purpose.** Restate it in one sentence and pick 2-4 themes (for renewal risk: workforce changes, executive moves, competing vendors, expansion signals). Themes decide which enrichments to add and how to filter peers.
2. **Match the company** with `match-business` using name and domain. A major brand that resolves to 1-50 employees in "Corporate Managing Offices" is likely a shell entity; retry with the other domain or the plain name. If there is no confident match, ask the user.
3. **Enrich in tiers** with one `enrich-business` call listing the chosen types in `enrichment

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---
name: clean-data
description: "Tidy a pasted list of companies or contacts before matching or enrichment: normalize company names, domains, countries and emails, flag invalid rows with a reason, match each row to AgentSource records, and remove duplicates that resolve to the same company or person. Use for 'clean this list', 'match these companies to domains', 'dedupe these contacts', 'which of these emails are valid', 'prep this list for enrichment'. Works on lists pasted in chat, up to roughly 100 rows. Not for building a new list (use list-builder)."
---

# Clean Data

Standardize and check a pasted list, then match it so duplicates collapse onto real company or person records. Nothing is deleted: every row stays, with a status and reason.

## Connector rules

Use only the AgentSource tools in your tool list. Each tool's live description and input schema are authoritative; if this skill disagrees with a tool, follow the tool.

- **`tool_reasoning`** is required on every call: the user's original request in their exact words (up to 2000 characters), reused unchanged across chained calls for the same task. Never replace it with a per-step rationale or add personal data the user did not write.
- **Sessions:** reuse `session_id` from the previous response while continuing the same request; omit it to start a new request. Never invent one.
- **Chaining by ID:** pass `business_id` and `prospect_id` values from `match-business`, `match-prospects` or `fetch-entities` results. Do not re-match entities that a fetch already returned.
- **`autocomplete` first**, one concept per call, for `linkedin_category`, `naics_category`, `company_tech_stack_tech`, `job_title`, `interests`, `skills`, `business_intent_topics`, and US `city_region`. Use the returned values as-is. Use ISO codes directly for countries (`US`) and regions (`US-CA`).
- **What you see:** every tool returns full results inline. `fetch-entities` returns up to `page_size` rows per call (at most 500) and a `next_cursor` for the next page; `fetch-entities-statistics` returns aggregates over every match.
- **Credits:** every data call uses credits on the user's AgentSource account. Request only the rows and enrichments the task needs, count with `fetch-entities-statistics` before a large pull, and confirm with the user before fetching more than 100 rows or enriching contact details for more than 50 people.
- **Limits per call:** `match-business` 50 companies, `match-prospects` 40 people, `enrich-business` and `enrich-prospects` 50 IDs, `fetch-businesses-events` 20 companies. Name several enrichment types in one `enrichments` list rather than one call each.
- **Presenting:** show returned records in a table with the fields that matter for the task. Never mention session IDs or internal table names.
- **Event details:** if a fetch used the `events` filter and the user did not already ask for event details, ask before calling `fetch-businesses-events` or `fetch-prospects-events`.
- **Lists:** work from lists pasted in chat or text from attached files, in batches that respect the limits above.

## Input

A list of companies or contacts pasted in chat, or the text of an attached file. Roughly 100 rows is the practical limit in one conversation; for more, work in batches. Note which column is the company name, domain, email, LinkedIn URL, phone or country; ask only if it is unclear. For a very large file, suggest the user clean it in a spreadsheet first.

## Workflow

1. **Profile the input.** Report row count, fill rate for each field, and obvious problems: placeholder text ("N/A", "none", "-"), pasted addresses in the name field, many spellings of one country, free-email providers, and role addresses.
2. **Standardize,** keeping the raw value beside the normalized one:
   - *Company name:* trim, collapse spaces, remove stray punctuation, and drop a trailing legal suffix (Inc, LLC, Ltd, GmbH, S.A.) for matching only.
   - *Domain:* strip the protocol and `www`, keep the registrab

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---
name: competitor-research
description: "Compare two to four rival companies side by side: size, funding, hiring trends, tech stack, recent strategic moves, and overlap with the user's target customers, with a short battlecard per competitor. Use for 'compare Gong and Chorus', 'size up our competitors', 'competitive landscape for X', 'battlecard for renewal'. Not for a deep brief on one company (use account-research) or for finding lookalike companies (use lookalike-accounts)."
---

# Competitor Research

A dated, fact-led comparison of a few named rivals.

## Connector rules

Use only the AgentSource tools in your tool list. Each tool's live description and input schema are authoritative; if this skill disagrees with a tool, follow the tool.

- **`tool_reasoning`** is required on every call: the user's original request in their exact words (up to 2000 characters), reused unchanged across chained calls for the same task. Never replace it with a per-step rationale or add personal data the user did not write.
- **Sessions:** reuse `session_id` from the previous response while continuing the same request; omit it to start a new request. Never invent one.
- **Chaining by ID:** pass `business_id` and `prospect_id` values from `match-business`, `match-prospects` or `fetch-entities` results. Do not re-match entities that a fetch already returned.
- **`autocomplete` first**, one concept per call, for `linkedin_category`, `naics_category`, `company_tech_stack_tech`, `job_title`, `interests`, `skills`, `business_intent_topics`, and US `city_region`. Use the returned values as-is. Use ISO codes directly for countries (`US`) and regions (`US-CA`).
- **What you see:** every tool returns full results inline. `fetch-entities` returns up to `page_size` rows per call (at most 500) and a `next_cursor` for the next page; `fetch-entities-statistics` returns aggregates over every match.
- **Credits:** every data call uses credits on the user's AgentSource account. Request only the rows and enrichments the task needs, count with `fetch-entities-statistics` before a large pull, and confirm with the user before fetching more than 100 rows or enriching contact details for more than 50 people.
- **Limits per call:** `match-business` 50 companies, `match-prospects` 40 people, `enrich-business` and `enrich-prospects` 50 IDs, `fetch-businesses-events` 20 companies. Name several enrichment types in one `enrichments` list rather than one call each.
- **Presenting:** show returned records in a table with the fields that matter for the task. Never mention session IDs or internal table names.
- **Event details:** if a fetch used the `events` filter and the user did not already ask for event details, ask before calling `fetch-businesses-events` or `fetch-prospects-events`.
- **Lists:** work from lists pasted in chat or text from attached files, in batches that respect the limits above.

## Input

- **Competitors** (required): 2-4 names or domains. Beyond four, the side-by-side stops being readable; offer a second pass.
- **Purpose** (ask once if missing): battlecard, board update, win-loss prep, renewal.
- Optional: priority angles (pricing pressure, AI roadmap, a region), the user's target customers (to judge overlap), and hypotheses to confirm or contradict.

## Workflow

1. **Anchor.** Restate the purpose, angles and hypotheses in 1-2 sentences.
2. **Match all competitors** in one `match-business` call, with name and domain where available. Check each for the shell-entity pattern (a big brand showing 1-50 employees under "Corporate Managing Offices") and retry with the alternate domain. Drop and flag any that do not resolve confidently; do not proceed with the wrong company.
3. **Enrich** all matched competitors in one `enrich-business` call, listing the types in `enrichments`: firmographics, funding-and-acquisitions, workforce-trends, technographics, competitive-landscape, strategic-insights, challenges, company-ratings, linkedin-posts. Prioritize by the

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---
name: enrich-company
description: "Look up one company, or a pasted list of up to 50 names or domains, and add firmographics, tech stack, funding and acquisitions, workforce and hiring trends, ratings, company hierarchy, website changes, or LinkedIn posts. Use for 'enrich this company', 'what does acme.com use', 'how big is Stripe', 'funding history for Asana', 'add industry and headcount to this list of domains'. Not for finding new companies (use list-builder) or a full multi-section brief (use account-research)."
---

# Enrich Company

Resolve named companies to AgentSource records, then add the data the user asked for.

## Connector rules

Use only the AgentSource tools in your tool list. Each tool's live description and input schema are authoritative; if this skill disagrees with a tool, follow the tool.

- **`tool_reasoning`** is required on every call: the user's original request in their exact words (up to 2000 characters), reused unchanged across chained calls for the same task. Never replace it with a per-step rationale or add personal data the user did not write.
- **Sessions:** reuse `session_id` from the previous response while continuing the same request; omit it to start a new request. Never invent one.
- **Chaining by ID:** pass `business_id` and `prospect_id` values from `match-business`, `match-prospects` or `fetch-entities` results. Do not re-match entities that a fetch already returned.
- **`autocomplete` first**, one concept per call, for `linkedin_category`, `naics_category`, `company_tech_stack_tech`, `job_title`, `interests`, `skills`, `business_intent_topics`, and US `city_region`. Use the returned values as-is. Use ISO codes directly for countries (`US`) and regions (`US-CA`).
- **What you see:** every tool returns full results inline. `fetch-entities` returns up to `page_size` rows per call (at most 500) and a `next_cursor` for the next page; `fetch-entities-statistics` returns aggregates over every match.
- **Credits:** every data call uses credits on the user's AgentSource account. Request only the rows and enrichments the task needs, count with `fetch-entities-statistics` before a large pull, and confirm with the user before fetching more than 100 rows or enriching contact details for more than 50 people.
- **Limits per call:** `match-business` 50 companies, `match-prospects` 40 people, `enrich-business` and `enrich-prospects` 50 IDs, `fetch-businesses-events` 20 companies. Name several enrichment types in one `enrichments` list rather than one call each.
- **Presenting:** show returned records in a table with the fields that matter for the task. Never mention session IDs or internal table names.
- **Event details:** if a fetch used the `events` filter and the user did not already ask for event details, ask before calling `fetch-businesses-events` or `fetch-prospects-events`.
- **Lists:** work from lists pasted in chat or text from attached files, in batches that respect the limits above.

## Input

One or more company names or domains, typed or pasted (up to 50 per match call). A name plus a domain matches best. Optional: which kinds of data the user wants.

## Workflow

1. **Match.** Call `match-business` with each company's `name` and/or `domain`. The result is complete, so read it directly.
2. **Check the match.** A well-known brand that resolves to 1-50 employees in a "Corporate Managing Offices" category probably landed on a registered-agent shell. Retry with the alternate domain or the plain company name. The match has no confidence score, so when the candidates look ambiguous, ask the user instead of guessing. Report unmatched rows and do not substitute other companies.
3. **Choose enrichments** from what the user asked for. With no preference, use firmographics plus technographics.

   | Need | Enrichment |
   |---|---|
   | Name, description, size, revenue, location, industry | `firmographics` |
   | Tech stack | `technographics` |
   | Web infrastructure | `webstack` |
   | Rounds, 

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---
name: enrich-contact
description: "Look up one person or a small pasted list of people and return their professional profile and contact details: work email, phone, LinkedIn, title, seniority, department, work history, and recent job changes. Identify people by email, LinkedIn URL, phone number, or full name plus company. Use for 'find the email for Jane Smith at Acme', 'enrich this contact', 'who is this person', 'background on these attendees'. Not for finding new people by role (use list-builder or account-contacts) or for drafting outreach."
---

# Enrich Contact

Resolve people to AgentSource records, then add the profile and contact data the user asked for.

## Connector rules

Use only the AgentSource tools in your tool list. Each tool's live description and input schema are authoritative; if this skill disagrees with a tool, follow the tool.

- **`tool_reasoning`** is required on every call: the user's original request in their exact words (up to 2000 characters), reused unchanged across chained calls for the same task. Never replace it with a per-step rationale or add personal data the user did not write.
- **Sessions:** reuse `session_id` from the previous response while continuing the same request; omit it to start a new request. Never invent one.
- **Chaining by ID:** pass `business_id` and `prospect_id` values from `match-business`, `match-prospects` or `fetch-entities` results. Do not re-match entities that a fetch already returned.
- **`autocomplete` first**, one concept per call, for `linkedin_category`, `naics_category`, `company_tech_stack_tech`, `job_title`, `interests`, `skills`, `business_intent_topics`, and US `city_region`. Use the returned values as-is. Use ISO codes directly for countries (`US`) and regions (`US-CA`).
- **What you see:** every tool returns full results inline. `fetch-entities` returns up to `page_size` rows per call (at most 500) and a `next_cursor` for the next page; `fetch-entities-statistics` returns aggregates over every match.
- **Credits:** every data call uses credits on the user's AgentSource account. Request only the rows and enrichments the task needs, count with `fetch-entities-statistics` before a large pull, and confirm with the user before fetching more than 100 rows or enriching contact details for more than 50 people.
- **Limits per call:** `match-business` 50 companies, `match-prospects` 40 people, `enrich-business` and `enrich-prospects` 50 IDs, `fetch-businesses-events` 20 companies. Name several enrichment types in one `enrichments` list rather than one call each.
- **Presenting:** show returned records in a table with the fields that matter for the task. Never mention session IDs or internal table names.
- **Event details:** if a fetch used the `events` filter and the user did not already ask for event details, ask before calling `fetch-businesses-events` or `fetch-prospects-events`.
- **Lists:** work from lists pasted in chat or text from attached files, in batches that respect the limits above.

## Input

For each person, one of: an email, a LinkedIn URL, a phone number, or a full name with a company name or domain. A name without a company cannot be matched. Up to 40 people per `match-prospects` call. Optional: whether the user wants contact details, a profile, or career changes.

## Workflow

1. **Pick the strongest identifier** for each person: email, then LinkedIn URL, then phone, then name plus company. Do not fan out several strategies for one person. If the user gave a role instead of a name ("the CFO at Asana"), do not match on the title; use `match-business` to get the company, then `fetch-entities` for prospects filtered by title and seniority, and let the user choose.
2. **Match.** Call `match-prospects`. For a name plus company, add the company's `business_id` from `match-business` when you have it. Results are complete: a person is either matched or null. A typo in an email is a miss; do not retry it as a name search, since that could return a di

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---
name: list-builder
description: "Build a targeted list of companies or people from a plain-language description of the audience: job titles, seniority, department, industry, company size, location, tech stack, buying-intent topics, or recent company events such as funding or a new office. Use for requests like 'find 50 CISOs in Texas', 'companies using Snowflake in the UK', 'build a target account list', 'companies that opened a new office in California', or 'heads of demand gen at fintechs'. Not for a single named company or person (use enrich-company or enrich-contact), for counting a market without pulling rows (use market-sizing), or for sending outreach."
---

# List Builder

Turn a plain-language audience description into a list of companies or people. Results come back in full, one page at a time.

## Connector rules

Use only the AgentSource tools in your tool list. Each tool's live description and input schema are authoritative; if this skill disagrees with a tool, follow the tool.

- **`tool_reasoning`** is required on every call: the user's original request in their exact words (up to 2000 characters), reused unchanged across chained calls for the same task. Never replace it with a per-step rationale or add personal data the user did not write.
- **Sessions:** reuse `session_id` from the previous response while continuing the same request; omit it to start a new request. Never invent one.
- **Chaining by ID:** pass `business_id` and `prospect_id` values from `match-business`, `match-prospects` or `fetch-entities` results. Do not re-match entities that a fetch already returned.
- **`autocomplete` first**, one concept per call, for `linkedin_category`, `naics_category`, `company_tech_stack_tech`, `job_title`, `interests`, `skills`, `business_intent_topics`, and US `city_region`. Use the returned values as-is. Use ISO codes directly for countries (`US`) and regions (`US-CA`).
- **What you see:** every tool returns full results inline. `fetch-entities` returns up to `page_size` rows per call (at most 500) and a `next_cursor` for the next page; `fetch-entities-statistics` returns aggregates over every match.
- **Credits:** every data call uses credits on the user's AgentSource account. Request only the rows and enrichments the task needs, count with `fetch-entities-statistics` before a large pull, and confirm with the user before fetching more than 100 rows or enriching contact details for more than 50 people.
- **Limits per call:** `match-business` 50 companies, `match-prospects` 40 people, `enrich-business` and `enrich-prospects` 50 IDs, `fetch-businesses-events` 20 companies. Name several enrichment types in one `enrichments` list rather than one call each.
- **Presenting:** show returned records in a table with the fields that matter for the task. Never mention session IDs or internal table names.
- **Event details:** if a fetch used the `events` filter and the user did not already ask for event details, ask before calling `fetch-businesses-events` or `fetch-prospects-events`.
- **Lists:** work from lists pasted in chat or text from attached files, in batches that respect the limits above.

## Parse the request

Pull these out of the user's message:

- **Entity type:** `prospects` if any person attribute is named (title, seniority, department, skills); `businesses` only when the request is about companies with no people.
- **Roles:** titles, seniority, department.
- **Company traits:** industry, size, revenue, company age, public or private.
- **Geography:** country, state or region, US city. Note whether it describes the person or the company.
- **Signals:** tech stack, buying-intent topics, recent events (funding, new office, hiring, M&A) with a 30-90 day window.
- **Count:** use the exact number requested; default 30. Set `page_size` to the count, up to 500; for more, page with `next_cursor`. Confirm before pulling more than 100 rows, because each row uses credits.
- **Extras:** contact details (emails, phones

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---
name: lookalike-accounts
description: "Find companies that resemble a seed company by rebuilding its industry, size, region and tech-stack profile and searching for others that share it. Use for 'find companies like Stripe', 'lookalike accounts for Acme', 'similar companies to our best customer', 'expand my territory', 'extend this account list'. Not for similar people (use list-builder) or for comparing named rivals (use competitor-research). Results are an approximation, not a similarity score."
---

# Lookalike Accounts

Rebuild the profile of one seed company, then search for companies that share it.

## Connector rules

Use only the AgentSource tools in your tool list. Each tool's live description and input schema are authoritative; if this skill disagrees with a tool, follow the tool.

- **`tool_reasoning`** is required on every call: the user's original request in their exact words (up to 2000 characters), reused unchanged across chained calls for the same task. Never replace it with a per-step rationale or add personal data the user did not write.
- **Sessions:** reuse `session_id` from the previous response while continuing the same request; omit it to start a new request. Never invent one.
- **Chaining by ID:** pass `business_id` and `prospect_id` values from `match-business`, `match-prospects` or `fetch-entities` results. Do not re-match entities that a fetch already returned.
- **`autocomplete` first**, one concept per call, for `linkedin_category`, `naics_category`, `company_tech_stack_tech`, `job_title`, `interests`, `skills`, `business_intent_topics`, and US `city_region`. Use the returned values as-is. Use ISO codes directly for countries (`US`) and regions (`US-CA`).
- **What you see:** every tool returns full results inline. `fetch-entities` returns up to `page_size` rows per call (at most 500) and a `next_cursor` for the next page; `fetch-entities-statistics` returns aggregates over every match.
- **Credits:** every data call uses credits on the user's AgentSource account. Request only the rows and enrichments the task needs, count with `fetch-entities-statistics` before a large pull, and confirm with the user before fetching more than 100 rows or enriching contact details for more than 50 people.
- **Limits per call:** `match-business` 50 companies, `match-prospects` 40 people, `enrich-business` and `enrich-prospects` 50 IDs, `fetch-businesses-events` 20 companies. Name several enrichment types in one `enrichments` list rather than one call each.
- **Presenting:** show returned records in a table with the fields that matter for the task. Never mention session IDs or internal table names.
- **Event details:** if a fetch used the `events` filter and the user did not already ask for event details, ask before calling `fetch-businesses-events` or `fetch-prospects-events`.
- **Lists:** work from lists pasted in chat or text from attached files, in batches that respect the limits above.

## Input

- **Seed company** (required): a name or domain. If the user gives a person, or asks for similar contacts, use list-builder instead; this skill returns companies.
- **Count:** default 25, up to 100.
- **Overrides (optional):** country or region focus, size or revenue bucket, public-only, and extra tech-stack requirements.

## Workflow

1. **Match the seed** with `match-business`, using name and domain. If several candidates return, pick the one whose domain matches and say which you chose. If none resolves, ask for a domain.
2. **Check the seed.** A big brand that resolves to 1-50 employees under "Corporate Managing Offices" is a shell entity. Retry with the other domain, since a wrong seed produces unrelated results.
3. **Read the seed's profile** with `enrich-business` and `firmographics` plus `technographics`. Take: industry (LinkedIn or NAICS, whichever is populated), headcount and revenue bucket, country (or the region the user asked for), and the top 3-5 technologies, favoring category-defining tools (CRM, mark

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---
name: market-sizing
description: "Size a market: count how many companies match an ideal customer profile, refine the filters with the user until the number makes sense, and return the final filter set. Use for 'how many companies match', 'size the market for X', 'TAM for fintech in the UK', 'is my ICP too broad', 'territory sizing', or a rough SAM estimate. Counts and breakdowns only, with no list of companies unless asked. Not for building the list itself (use list-builder)."
---

# Market Sizing

Refine a company-level ICP and report how many companies match, using statistics instead of pulling rows.

## Connector rules

Use only the AgentSource tools in your tool list. Each tool's live description and input schema are authoritative; if this skill disagrees with a tool, follow the tool.

- **`tool_reasoning`** is required on every call: the user's original request in their exact words (up to 2000 characters), reused unchanged across chained calls for the same task. Never replace it with a per-step rationale or add personal data the user did not write.
- **Sessions:** reuse `session_id` from the previous response while continuing the same request; omit it to start a new request. Never invent one.
- **Chaining by ID:** pass `business_id` and `prospect_id` values from `match-business`, `match-prospects` or `fetch-entities` results. Do not re-match entities that a fetch already returned.
- **`autocomplete` first**, one concept per call, for `linkedin_category`, `naics_category`, `company_tech_stack_tech`, `job_title`, `interests`, `skills`, `business_intent_topics`, and US `city_region`. Use the returned values as-is. Use ISO codes directly for countries (`US`) and regions (`US-CA`).
- **What you see:** every tool returns full results inline. `fetch-entities` returns up to `page_size` rows per call (at most 500) and a `next_cursor` for the next page; `fetch-entities-statistics` returns aggregates over every match.
- **Credits:** every data call uses credits on the user's AgentSource account. Request only the rows and enrichments the task needs, count with `fetch-entities-statistics` before a large pull, and confirm with the user before fetching more than 100 rows or enriching contact details for more than 50 people.
- **Limits per call:** `match-business` 50 companies, `match-prospects` 40 people, `enrich-business` and `enrich-prospects` 50 IDs, `fetch-businesses-events` 20 companies. Name several enrichment types in one `enrichments` list rather than one call each.
- **Presenting:** show returned records in a table with the fields that matter for the task. Never mention session IDs or internal table names.
- **Event details:** if a fetch used the `events` filter and the user did not already ask for event details, ask before calling `fetch-businesses-events` or `fetch-prospects-events`.
- **Lists:** work from lists pasted in chat or text from attached files, in batches that respect the limits above.

## Input

- **ICP** in plain language, or "my ICP" with details to ask for.
- **Use case (optional):** territory design, investor sizing, or ICP sharpening (default).
- **SAM inputs (optional):** an addressable fraction (0-1) and average revenue per account in USD.

## Workflow

1. **Parse the ICP** into filter dimensions and note which the user stated. Persona details ("CTOs", "VP Sales") are recorded but do not change a company count.
2. **Disambiguate regions first.** "EU" can mean EU member states or all of Europe; "Americas" can mean North America or the US and Canada. Ask one question, then count.
3. **Resolve free-text values** with `autocomplete`, then sort each into one of three outcomes before counting:
   - *Clean match:* an unambiguous canonical value. Use it.
   - *Parent or brittle match:* only a parent or sibling exists (for example "fintech" maps only to "Financial Services"). Stop and show the nearest entries, then ask whether to approximate with the parent, use a technology or intent proxy, or drop it. Do not 

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---
name: meeting-prep
description: "Prepare a 30-minute call brief for a target account: headline signals, attendee profiles, ranked talking points, discovery questions, a suggested agenda, and what to avoid. Use for 'prep me for a call with Acme', 'I have a meeting with Jane at Globex tomorrow', 'pre-meeting research', renewal or QBR meetings. Not for general research with no meeting (use account-research) or for a standalone person lookup (use enrich-contact)."
---

# Meeting Prep

Gather the company, signal and attendee evidence, then write a tight brief the user can read in five minutes.

## Connector rules

Use only the AgentSource tools in your tool list. Each tool's live description and input schema are authoritative; if this skill disagrees with a tool, follow the tool.

- **`tool_reasoning`** is required on every call: the user's original request in their exact words (up to 2000 characters), reused unchanged across chained calls for the same task. Never replace it with a per-step rationale or add personal data the user did not write.
- **Sessions:** reuse `session_id` from the previous response while continuing the same request; omit it to start a new request. Never invent one.
- **Chaining by ID:** pass `business_id` and `prospect_id` values from `match-business`, `match-prospects` or `fetch-entities` results. Do not re-match entities that a fetch already returned.
- **`autocomplete` first**, one concept per call, for `linkedin_category`, `naics_category`, `company_tech_stack_tech`, `job_title`, `interests`, `skills`, `business_intent_topics`, and US `city_region`. Use the returned values as-is. Use ISO codes directly for countries (`US`) and regions (`US-CA`).
- **What you see:** every tool returns full results inline. `fetch-entities` returns up to `page_size` rows per call (at most 500) and a `next_cursor` for the next page; `fetch-entities-statistics` returns aggregates over every match.
- **Credits:** every data call uses credits on the user's AgentSource account. Request only the rows and enrichments the task needs, count with `fetch-entities-statistics` before a large pull, and confirm with the user before fetching more than 100 rows or enriching contact details for more than 50 people.
- **Limits per call:** `match-business` 50 companies, `match-prospects` 40 people, `enrich-business` and `enrich-prospects` 50 IDs, `fetch-businesses-events` 20 companies. Name several enrichment types in one `enrichments` list rather than one call each.
- **Presenting:** show returned records in a table with the fields that matter for the task. Never mention session IDs or internal table names.
- **Event details:** if a fetch used the `events` filter and the user did not already ask for event details, ask before calling `fetch-businesses-events` or `fetch-prospects-events`.
- **Lists:** work from lists pasted in chat or text from attached files, in batches that respect the limits above.

## Input

- **Account** (required): a company name or domain.
- **Attendees** (recommended): names with the company, or emails. Without them the brief stays generic.
- **Meeting context** (strongly recommended): purpose, stakes, deal stage, what the user wants to walk out with, known tensions, competitors, what they are selling, and hypotheses to test. If it is missing, ask once; otherwise prepare general notes and say so.

## Workflow

1. **Anchor.** Restate the purpose and desired outcome in 1-2 sentences and pick 3-5 priority topics and any hypotheses.
2. **Match the company** with `match-business`, checking for the shell-entity pattern (big brand, 1-50 employees, "Corporate Managing Offices") and retrying with the alternate domain.
3. **Enrich the company** in one `enrich-business` call, listing the types in `enrichments`: firmographics and technographics, funding-and-acquisitions, strategic-insights and challenges, workforce-trends, competitive-landscape and company-hierarchies, linkedin-posts and website-changes. Gather broadly now; dec

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---
name: score-leads
description: "Score and tier a list of target accounts (A, B, C by ICP fit, buying intent, recent trigger events and hiring momentum) or a list of people (Hot, Warm, Cold against a buyer persona and ICP), with a one-line 'why now' per row. Use for 'score these accounts', 'rank this list by ICP fit', 'prioritize my leads', 'which of these should I call first', 'tier my inbound leads'. Works on lists pasted in chat. Not for building a new list (use list-builder) or for research on a single company (use account-research)."
---

# Score Leads

Resolve a list, gather evidence for each row, then apply a transparent rubric the user can adjust. The scoring is your reasoning over the returned data; there is no built-in scoring engine.

## Connector rules

Use only the AgentSource tools in your tool list. Each tool's live description and input schema are authoritative; if this skill disagrees with a tool, follow the tool.

- **`tool_reasoning`** is required on every call: the user's original request in their exact words (up to 2000 characters), reused unchanged across chained calls for the same task. Never replace it with a per-step rationale or add personal data the user did not write.
- **Sessions:** reuse `session_id` from the previous response while continuing the same request; omit it to start a new request. Never invent one.
- **Chaining by ID:** pass `business_id` and `prospect_id` values from `match-business`, `match-prospects` or `fetch-entities` results. Do not re-match entities that a fetch already returned.
- **`autocomplete` first**, one concept per call, for `linkedin_category`, `naics_category`, `company_tech_stack_tech`, `job_title`, `interests`, `skills`, `business_intent_topics`, and US `city_region`. Use the returned values as-is. Use ISO codes directly for countries (`US`) and regions (`US-CA`).
- **What you see:** every tool returns full results inline. `fetch-entities` returns up to `page_size` rows per call (at most 500) and a `next_cursor` for the next page; `fetch-entities-statistics` returns aggregates over every match.
- **Credits:** every data call uses credits on the user's AgentSource account. Request only the rows and enrichments the task needs, count with `fetch-entities-statistics` before a large pull, and confirm with the user before fetching more than 100 rows or enriching contact details for more than 50 people.
- **Limits per call:** `match-business` 50 companies, `match-prospects` 40 people, `enrich-business` and `enrich-prospects` 50 IDs, `fetch-businesses-events` 20 companies. Name several enrichment types in one `enrichments` list rather than one call each.
- **Presenting:** show returned records in a table with the fields that matter for the task. Never mention session IDs or internal table names.
- **Event details:** if a fetch used the `events` filter and the user did not already ask for event details, ask before calling `fetch-businesses-events` or `fetch-prospects-events`.
- **Lists:** work from lists pasted in chat or text from attached files, in batches that respect the limits above.

## Choose a mode

- **Accounts:** the list is companies (names, domains). Output is a tier A/B/C with a score per account.
- **People:** the list is leads (emails, LinkedIn URLs, or name plus company). Output is Hot, Warm or Cold per person.

## Input

- **The list** (required), up to 50 accounts or 40 people per match call.
- **ICP** (required): industries, headcount and revenue buckets, countries or regions, optional tech vendors, optional intent topics. For people, also the **buyer persona**: titles, seniority, department.
- **Optional:** a source label per lead (demo request, pricing inquiry, webinar) passed through as given; weights; tier thresholds; trigger window (default 90 days).

If the ICP is missing, ask for it; do not invent one.

## How many rows to score

Every enrichment and event call returns full values, but each one uses credits per row. Score up to 50 rows in one pass

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---
name: talent-sourcing
description: "Source candidates from a job description or role brief, or map the talent bench for a function across target or competitor companies with movement signals such as layoffs and leadership changes. Use for 'find candidates for a senior Go engineer in Berlin', 'build a talent pipeline for a VP Sales', 'who could fill this role', 'map the data engineering teams at our competitors', 'where can we hire from'. Recruiting and talent intelligence only. Not for sales prospecting (use list-builder) or for researching a named person (use enrich-contact)."
---

# Talent Sourcing

Turn a role brief into a candidate search, or map where a function's talent sits across companies.

## Connector rules

Use only the AgentSource tools in your tool list. Each tool's live description and input schema are authoritative; if this skill disagrees with a tool, follow the tool.

- **`tool_reasoning`** is required on every call: the user's original request in their exact words (up to 2000 characters), reused unchanged across chained calls for the same task. Never replace it with a per-step rationale or add personal data the user did not write.
- **Sessions:** reuse `session_id` from the previous response while continuing the same request; omit it to start a new request. Never invent one.
- **Chaining by ID:** pass `business_id` and `prospect_id` values from `match-business`, `match-prospects` or `fetch-entities` results. Do not re-match entities that a fetch already returned.
- **`autocomplete` first**, one concept per call, for `linkedin_category`, `naics_category`, `company_tech_stack_tech`, `job_title`, `interests`, `skills`, `business_intent_topics`, and US `city_region`. Use the returned values as-is. Use ISO codes directly for countries (`US`) and regions (`US-CA`).
- **What you see:** every tool returns full results inline. `fetch-entities` returns up to `page_size` rows per call (at most 500) and a `next_cursor` for the next page; `fetch-entities-statistics` returns aggregates over every match.
- **Credits:** every data call uses credits on the user's AgentSource account. Request only the rows and enrichments the task needs, count with `fetch-entities-statistics` before a large pull, and confirm with the user before fetching more than 100 rows or enriching contact details for more than 50 people.
- **Limits per call:** `match-business` 50 companies, `match-prospects` 40 people, `enrich-business` and `enrich-prospects` 50 IDs, `fetch-businesses-events` 20 companies. Name several enrichment types in one `enrichments` list rather than one call each.
- **Presenting:** show returned records in a table with the fields that matter for the task. Never mention session IDs or internal table names.
- **Event details:** if a fetch used the `events` filter and the user did not already ask for event details, ask before calling `fetch-businesses-events` or `fetch-prospects-events`.
- **Lists:** work from lists pasted in chat or text from attached files, in batches that respect the limits above.

## Choose a mode

- **Pipeline:** the user has a role to fill and wants candidates.
- **Bench map:** the user wants to see who does a function across a set of companies and where talent may be open to a move.

## Fair-hiring and privacy rules

- Never filter, score or comment on protected characteristics (age, gender, ethnicity, religion, disability, nationality, family status) or on proxies for them, such as graduation year to infer age or a name to infer gender or origin. If the brief asks for that, decline that criterion and say why.
- Use only job-related evidence that the data shows. Mark a skill the profile does not show as "unverified" instead of inferring it.
- Use contact data only for recruiting outreach about a real role, and remind the user to honor opt-outs and local rules (GDPR, CCPA).
- Do not pull personal contact data during a bench map.

## Pipeline mode

### Input

Role (title, seniority, function); must-haves and

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