Fivos Account Enrichment

Fivos Account Enrichment

Company-level enrichment. Runs after sourcing, before contact pulls. All enrichment research runs through Clay (mcp__claude_ai_Clay__*), which is already wired in this environment. Clay orchestrates the underlying providers (BuiltWith, Crustdata, Firecrawl, PDL, etc.) under one roof.

Hard rules

• Clay is the orchestrator. Do not call per-provider APIs directly from this skill. Clay handles provider routing, credit accounting, and dedup inside a table.
• One Clay table per (client, campaign). Never mix clients in the same table - makes credit accounting and list reuse impossible.
• Always pilot before the full run. A 1-3 row pilot catches column-spec mistakes that would otherwise spend across hundreds of rows.

Inputs

• Company CSV from fivos-account-sourcing (qualified).
• Column spec - the custom fields to add. Typical sets:
• Tech stack (tech_stack, primary_cloud, crm, warehouse).
• Funding (last_round_stage, last_round_amount_usd, last_round_date, total_raised, investors).
• Hiring signals (open_roles_count, open_roles_target_titles, hiring_velocity_90d).
• Headcount history (headcount_now, headcount_90d_ago, headcount_growth_pct_90d).
• Web / content signals (blog_last_post_date, homepage_last_change_date, has_press_page).
• Compliance / security (soc2, iso_27001, hipaa, gdpr_dpo_listed).
• Budget ceiling (optional).
• Client tag + campaign tag (required).

Outputs

1. Enriched CSV exported from the Clay table with all requested columns.
2. Running Clay credit usage ledger.
3. Coverage report per column.

Provider-to-column cheat sheet (what Clay routes to under the hood)

| Column type | Primary provider | Fallback | |---|---|---| | Tech stack | BuiltWith | Crustdata (coarser), Firecrawl (homepage scrape) | | Funding graph | Crustdata | AI Ark, Apollo | | Hiring signals | PeopleDataLabs (PDL) | Crustdata, Firecrawl careers-page | | Headcount history | PDL | Ocean.io, AI Ark | | Revenue estimates | Crustdata | Apollo, AI Ark | | Web signals (blog, homepage, press) | Firecrawl | Apify | | Compliance flags (SOC2, HIPAA, etc.) | Firecrawl (footer / trust page) | Manual validation | | Industry taxonomy | Ocean.io (NAICS) + AI Ark (plain-english) | keep both | | Custom intent signals | Exa | Firecrawl |

You don't call these providers directly from this skill - you tell Clay which column to add and Clay picks the provider.

Workflow

Step 1 - Column plan + cost estimate

For each requested column, map to a Clay data point (using the cheat sheet above). Get current credit availability:

mcp__claude_ai_Clay__get-credits-available({})

Print a condensed plan:

Enrichment plan -  / 

Clay credits available: N
Columns requested:
  - tech_stack (BuiltWith)
  - last_round_stage (Crustdata)
  - open_roles_count (PDL)
  ...

Estimated credits used: ~X (Y rows x Z credits/row average)
Est. cost: $

Proceed? Manager approval required if over budget.

Gate on approval if over budget; auto-proceed otherwise.

Step 2 - Create the Clay table and ingest the CSV

Single Clay call:

mcp__claude_ai_Clay__add-company-data-points({
  table_name: "__accounts",
  source_csv: "",
  data_points: [
    "tech_stack",
    "last_round_stage",
    "last_round_amount_usd",
    "open_roles_count",
    "open_roles_target_titles",
    ...
  ]
})

For companies not already in the table, first use:

mcp__claude_ai_Clay__find-and-enrich-company({...})

Step 3 - Pilot (1-3 rows)

Before running the full set, run the column enrichment against 1-3 rows and inspect:

• Are the returned values sane?
• Is coverage acceptable?
• Any columns that came back null for all pilot rows? → revisit column spec or provider.

Step 4 - Full run

Once the pilot looks right, extend the enrichment to all rows. Monitor the Clay run:

mcp__claude_ai_Clay__get-task({ task_id:  })

Step 5 - Ask questions / custom research

For ICP-specific intel not in a standard column (e.g. "do they use OpenAI or Anthropic", "are they hiring for AI engineers"), use Clay's natural-language research:

mcp__claude_ai_Clay__ask-question-about-accounts({
  table_name: "__accounts",
  question: "Does this company publicly use OpenAI or Anthropic in their product?"
})

This is the replacement for "custom scrapes" - Clay spins up the research per row and writes a column with reasoning.

Step 6 - Coverage audit

Compute coverage per column. Flag any high-priority column below 70% coverage - usually a Clay fallback can close the gap (e.g. swap BuiltWith for Firecrawl homepage scrape).

Step 7 - Export + handoff

Export the enriched table from Clay as a CSV. Hand off to fivos-contact-sourcing - the contact step uses company-level signals (hiring, funding) to drive persona spec and contact-count-per-account.

Cost controls

• Never enrich columns without a downstream consumer. If the copywriter doesn't need it and scoring doesn't weight it, drop it.
• Always pilot on 1-3 rows.
• Use mcp__claude_ai_Clay__get-credits-available before every large run to avoid mid-run credit exhaustion.

References

• ~/.agents/skills/gtm-meta-skill/enriching-and-researching.md - enrichment patterns, coalescing, multi-pass pipelines.
• ~/.agents/skills/gtm-meta-skill/provider-playbooks/builtwith.md
• ~/.agents/skills/gtm-meta-skill/provider-playbooks/crustdata.md
• ~/.agents/skills/gtm-meta-skill/provider-playbooks/firecrawl.md
• ~/.agents/skills/gtm-meta-skill/provider-playbooks/peopledatalabs.md
• ~/.agents/skills/gtm-meta-skill/provider-playbooks/exa.md

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: fivosaresti
• Source: fivosaresti/workflows-outbound-skills
• License: MIT

Install and usage instructions live in the source repository linked above.