Lead Enrich

Research a person (name + company) using Serper.dev Google Search. Uses the agent's built-in model for extraction. No external LLM API needed.

Works standalone — just a Serper key. Pairs with Outreach Magic for credit-saving dedup (skip Serper when leads already have LinkedIn/email) and persistent storage across sessions.

Credit-saving (with OM): checks outreachmagic first. If the lead already has LinkedIn + email at the same company, zero Serper credits spent. LinkedIn without email skips Serper — use email-finder for trykitt. Email-only records still get LinkedIn searches (1–2 Serper credits). Name matches at a different company return ambiguous so APIs aren't wasted.

Setup

Standalone (no OM)

Just a Serper key. Run serper-search directly. Results print as JSON.

Key	For
`SERPER_API_KEY`	serper.dev — Google Search API

python3 scripts/enrich.py serper-search --query '"Acme Corp" official website'
python3 scripts/enrich.py serper-search --query 'site:linkedin.com/in Jane Doe Acme Corp'
# → pipe results to your model for extraction, or use serper-format

Set SERPER_API_KEY in your environment or via ~/.hermes/skills/lead-enrich/config.json.

With Outreach Magic (dedup + save)

Adds pre-flight dedup (skip leads already in OM) and saves structured enrichment to your local SQLite pipeline. Requires outreachmagic skill with pipeline.py login.

Key	For
All standalone keys above +
`OUTREACHMAGIC_AGENT_KEY`	OM login via `pipeline.py login`

python3 scripts/enrich.py check "Jane Doe" "Acme Corp"        # 0 credits
python3 scripts/enrich.py batch-check --workspace W input.json # batch dedup

Save your Serper key in Outreach Magic portal → Settings → API Keys, then run pipeline.py sync-secrets. Verify with enrich.py config (serper_api_key_source should be agent_secrets).

3. Email finding (email-finder skill)

Email find is not in lead-enrich v2+. Install email-finder with outreachmagic using the platform install guide. Or install standalone via npx skills add outreachmagic/email-finder.

After Serper enrichment saves company_domain, run:

python3 ~/.hermes/skills/email-finder/scripts/email_finder.py find --name "Jane Doe" \
  --domain acme.com --linkedin "https://linkedin.com/in/janedoe" --save

See references/email-finder.md and the email-finder skill docs.

Workspace rollups (no Serper credits): after saving leads, use outreachmagic workspace summary --workspace <slug> --json for tag counts and LinkedIn connection accepted per sender. On large workspaces (>2,000 leads), add --tags-only for faster tag counts. Local DB only — pull optional.

Common workflows

User says	You do
"Research this person"	Serper search → save via `import-profiles`
"Enrich my CSV"	`batch-check` / import → enrich missing fields → save to OM
"Find their email too"	After enrich, hand off to email-finder

Sales Nav / Vayne CSVs: use outreachmagic import-profiles --file … (auto-detects columns).

CSV / award-list workflow (preferred for 10+ people)

Paths like outreachmagic/imports/awards.csv are relative to your workspace directory (where the agent runs the command), not the skill install folder.

# 0 credits — dedup entire file first (auto-stamps serper_attempted on complete rows)
python3 scripts/enrich.py batch-check --workspace your_workspace outreachmagic/imports/awards.csv

# Re-run dedup skipping leads already tagged serper_attempted
python3 scripts/enrich.py batch-check --workspace your_workspace --skip-tagged outreachmagic/imports/awards.csv

# Serper only for rows that need LinkedIn/domain (skip team_award, exists_linkedin_*, skipped_serper_attempted)

# After research — patch title/industry only (0 Serper credits)
python3 scripts/enrich.py backfill --fields title,industry --workspace your_workspace outreachmagic/imports/patch.csv

batch-check accepts .json or .csv. backfill requires email or linkedin per row; uses chunked import-profiles via companion_common (200 rows/chunk, up to 300s/chunk; fills empty fields; add --overwrite to replace).

When to Use

User says "research this person" / "look up Jane Doe at Acme"
User wants to enrich a list of prospects before outreach
User asks "do we already have this lead?" before researching
User provides a CSV/JSON of names and companies to enrich in bulk
User mentions Serper, lead enrichment, or person research

Agent Behavior Rules (Important)

Dedup first (with OM). Before any Serper API call, run enrich.py check. If the lead has LinkedIn + email at the same company, skip Serper entirely. If LinkedIn exists but no email, skip Serper and offer email-finder when the user wants an address. Never spend Serper credits on leads already complete in outreachmagic. Standalone: skip check — run serper-search directly.
Serper only. Prefer enrich.py serper-search --query "..." (stdlib HTTP, key from config/env). Or use curl with $SERPER_API_KEY — never embed the key in chat logs. Never scrape Google or LinkedIn directly.
Built-in model only. You (the agent) extract JSON from Serper results. No external LLM APIs (no Gemini, no OpenAI) — your own reasoning is the extraction engine.
Complete research before saving. Run the full search ladder first, then save once.
Save via outreachmagic (with OM). Use import-profiles for leads with LinkedIn. Always append serper_attempted to tags on save (included automatically in map-to-om output). For leads without LinkedIn but with a known lead_id, stamp the tag via stamp-attempted or import-profiles with id + tags — do not rely on notes alone. For read-only dedup checks use pipeline.py query or enrich.py check — never raw INSERT/UPDATE. Never run both save paths for the same person. Standalone: save JSON output to a file — no OM needed.
Tag after enrichment (with OM). Every lead that goes through Serper must get serper_attempted on save. Prevents re-processing on future runs.
Check tag before Serper (with OM). Before spending Serper credits, check for serper_attempted (via enrich.py check --skip-tagged or skip_reason in check output). If present and LinkedIn is still empty, skip unless the user explicitly wants a retry (e.g. stale >30 days).
Transparency. Show which Serper queries ran, confidence, and what was saved. The user should see exactly where their credits went.
Batch wisely. Cap at 50 people per run. For CSV/award lists run batch-check once on the whole file (JSON or CSV) before any Serper. Process Serper only for statuses that need LinkedIn/domain. Skip team_award, exists_linkedin_email, and skipped_serper_attempted rows.
Email finding — use email-finder. After enrichment saves company_domain, hand off to email-finder (email_finder.py find --save). See references/email-finder.md. Never fabricate or pattern-guess emails in this skill.

Quick Start

# Single person (most common)
python3 scripts/enrich.py check "Jane Doe" "Acme Corp"
# → if "not_found", proceed with Serper search pack below

# With workspace (associates lead with your pipeline workspace)
python3 scripts/enrich.py check --workspace your_workspace "Jane Doe" "Acme Corp"

# Batch from JSON or CSV (run this before Serper on lists)
python3 scripts/enrich.py batch-check --workspace your_workspace outreachmagic/imports/people.json
python3 scripts/enrich.py batch-check --workspace your_workspace --skip-tagged outreachmagic/imports/awards.csv

# Stamp serper_attempted after failed LinkedIn lookup (when lead_id is known)
python3 scripts/enrich.py stamp-attempted --workspace your_workspace --lead-ids 42,43 \
  --notes "No LinkedIn found via Serper"

# Backfill title/industry on existing leads (linkedin or email required)
python3 scripts/enrich.py backfill --fields title,industry outreachmagic/imports/patch.csv

# Update skill safely from GitHub release (checksum-verified)
python3 scripts/enrich.py update --check
python3 scripts/enrich.py update

update verifies SHA256 checksums from update-manifest.json before replacing files. If checksums are missing or mismatched, the update aborts.

Core Workflow

Phase 1 — Dedup Check (0 credits)

For each person, run:

# Without workspace (org-wide lookup)
python3 scripts/enrich.py check "Jane Doe" "Acme Corp"

# With workspace (scoped to your pipeline workspace)
python3 scripts/enrich.py check --workspace your_workspace "Jane Doe" "Acme Corp"

Output per person:

Status	Meaning	Action
`exists_linkedin_email`	Same company, LinkedIn + email	Skip Serper and email APIs
`exists_linkedin_no_email`	Same company, LinkedIn, no email	Skip Serper → email-finder if user wants email
`exists_no_linkedin_email`	Same company, email, no LinkedIn	LinkedIn Serper queries only
`exists_no_linkedin`	Same company, neither	LinkedIn Serper queries only
`skipped_serper_attempted`	Has `serper_attempted` tag, no LinkedIn	Skip Serper — already tried
`ambiguous`	Name match, company mismatch	Run full Serper pack — do not skip
`not_found`	No match	Run full Serper search pack
`team_award`	Team/group row (no individual)	Skip Serper — tag `team_award`, add contact note
`dedup_disabled`	`dedup_before_search: false` in config	Run Serper as requested

Check output includes tags and optional skip_reason (has_linkedin or skipped_serper_attempted). Uses batch-lead-lookup (local, zero Serper credits).

Phase 2 — Serper Search Pack

Only run for people who need it. 2–4 searches per person depending on result quality:

Serper Credit Budget Estimator

Per person minimum: 0 Serper credits (exists_linkedin_email).
Common path: 2 credits (2a strict company + 2c LinkedIn profile).
Per person hard max: 4 credits when all fallbacks are needed (2a + 2b + 2c + 2e).
Batch formula: min=0, max=4*N where N is people in the run.
Batch cap example: with N=50, hard max is 200 credits (worst case).

2a. Company discovery — strict (always)

Preferred (no key in shell history):

python3 scripts/enrich.py serper-search --query '"Acme Corp" official website' --label company_discovery_strict

Or retry with a simpler query:

python3 scripts/enrich.py serper-search --query 'Acme Corp website' --label company_discovery_broad

2b. Company discovery — broad (conditional)

Run only if 2a returns no organic results with an http:// or https:// link:

python3 scripts/enrich.py serper-search --query 'Acme Corp official website' --label company_discovery_broad

(Same template, unquoted company name.)

2c. LinkedIn profile (always)

Build query (unquoted company — matches variant employer names in snippets):

site:linkedin.com/in {First Last} {up to 5 words of role} {Company Name}

Example:

python3 scripts/enrich.py serper-search \
  --query 'site:linkedin.com/in Jane Doe VP Marketing Acme Corp' \
  --label linkedin_profile

Phase 3 — Model Extraction

Pass the formatted Serper results to yourself (the agent model) with this system instruction:

You are a research assistant. The user message contains Serper.dev Google search
results in labeled sections. You do NOT have live web search — use only the pasted
blocks.

Task: for ONE specific person at ONE company, extract:
- company_domain: registrable hostname only (no path, no www), or empty string
- company_website: full https:// homepage URL if supported, or empty string
- linkedin_url: https://linkedin.com/in/… for the named person at this company, or empty string
- confidence: high | medium | low
- note: optional explanation of ambiguity or gaps

Rules:
- Every non-empty URL must appear in the Serper blocks (minor query-string normalization ok)
- Name on LinkedIn may differ from input (nickname vs legal name) — match on same human + employer
- Before accepting `linkedin_url`, verify match quality:
  - Extract first + last name tokens from `Full name: {full_name}` (ignore punctuation)
  - The chosen `/in/` result must have *both* tokens present in either the result `title` or `snippet`
  - If no `/in/` result meets the token requirement, return `linkedin_url` as an empty string and set `confidence` to `low`
- Company public site may use a different banner name than input — prefer official evidence
- When resolving `company_domain`:
  - Prefer `knowledgeGraph.website` when present
  - Reject common aggregators/registries (examples: `naceweb.org`, `usnews.com`, `wikipedia.org`, `niche.com`, `facebook.com`, `instagram.com`, `twitter.com`)
  - If `company_name` appears to be missing or matches the person name closely, return empty string
- Never fabricate URLs or slugs

Respond ONLY with a single JSON object (no markdown fences):
{"company_domain":"","company_website":"","linkedin_url":"","confidence":"medium","note":""}

Use this user message template:

### Target person
Full name: {full_name}
Stated role/title: {stated_role}
Company (as provided): {company_name}

### Search results
{formatted_serper_sections}

### Task
Return the JSON object described in the system instruction.

Then parse your own response: strip markdown fences, extract the JSON object.

LinkedIn harvest fallback: if linkedin_url is empty after extraction, scan the raw Serper organic results for /in/ URLs where the title contains both first and last name tokens. Prefer matches whose snippet/title also mention the employer/company.

Phase 4 — Save via outreachmagic

Map extracted fields to outreachmagic:

Research field	outreachmagic field
`full_name`	`name`
`stated_role`	`job_title`
`company_name`	`company`
`linkedin_url`	`linkedin`
`company_domain`	`company_domain` (structured) + optional `notes`
`company_website`	→ `notes`
`confidence`	→ `notes`
`note`	→ `notes`
`tags`	`tags` (JSON array)
`import_name`	→ `notes` prefix

If LinkedIn found:

Every import from this skill sets --source lead_enrich and --source-detail "lead-enrich" by default. If an import_name is provided, detail appends as "lead-enrich/{import_name}".

# Org-wide (no workspace)
python3 {outreachmagic_home}/scripts/pipeline.py import-profiles \
  --source lead_enrich --source-detail "lead-enrich" \
  --json '[{"name":"Jane Doe","company":"Acme Corp","job_title":"VP Marketing","linkedin":"linkedin.com/in/janedoe","company_domain":"acme.com","tags":["nace","serper_attempted"]}]'

# Scoped to a workspace
python3 {outreachmagic_home}/scripts/pipeline.py import-profiles \
  --workspace your_workspace \
  --source lead_enrich --source-detail "lead-enrich" \
  --json '[{"name":"Jane Doe","company":"Acme Corp","job_title":"VP Marketing","linkedin":"linkedin.com/in/janedoe","company_domain":"acme.com","tags":["nace","serper_attempted"]}]'

If no LinkedIn, no email: When lead_id is known (from batch-check), stamp attempt state — do not bury failure only in notes:

python3 scripts/enrich.py stamp-attempted --workspace your_workspace --lead-ids 42 \
  --notes "No LinkedIn found via Serper"

Or via import-profiles when you also have name + company:

python3 {outreachmagic_home}/scripts/pipeline.py import-profiles \
  --workspace your_workspace \
  --source lead_enrich --source-detail "lead-enrich/no-linkedin" \
  --json '[{"id":42,"name":"Jane Doe","company":"Acme Corp","tags":["nace","serper_attempted"],"notes":"No LinkedIn found"}]'

Without a lead_id, use add-lead with notes (last resort) or report unsaved.

Email finding (email-finder skill)

After Phase 4 save, if the user wants an email and company_domain is known, use the email-finder companion — not this skill:

python3 ~/.hermes/skills/email-finder/scripts/email_finder.py find \
  --name "Jane Doe" --domain acme.com \
  --linkedin "https://linkedin.com/in/janedoe" --save

See references/email-finder.md and email-finder's email-finding-research.md.

Phase 5 — Report

Summarize per person:

Jane Doe @ Acme Corp
  ✅ Company: acme.com | https://acme.com
  ✅ LinkedIn: linkedin.com/in/janedoe
  🟢 Confidence: high
  💾 Saved to outreachmagic (lead #42)
  🔍 Serper: 2 queries
  📧 Email: use email-finder if needed

Email-only mode

When contacts are already enriched (have company_domain + linkedin in CSV or outreachmagic) but lack email:

Run batch-check — process only exists_linkedin_no_email (and optionally not_found rows that already have domain in the file).
Skip Serper entirely (0 Serper credits).
Run email-finder batch-find for each person with domain + LinkedIn.
Respect trykitt_attempted tag — email-finder skips rows already tagged.
Respect serper_attempted tag — lead-enrich skips rows already researched.

Useful after a prior enrichment pass or when importing a pre-researched list.

Input Formats

Single person

Research Jane Doe, VP Marketing at Acme Corp

Or with workspace:

Research Jane Doe at Acme Corp --workspace your_workspace

Batch (JSON file or inline)

{
  "people": [
    {"full_name": "Jane Doe", "company_name": "Acme Corp", "stated_role": "CEO"},
    {"full_name": "John Smith", "company_name": "Beta Inc"}
  ],
  "workspace": "your_workspace",
  "tags": ["nace"],
  "import_name": "NACE 2026 attendee"
}

Max 50 people per run.

Config Reference

config.json (copy from config.example.json):

Key	Required	Default	Description
`serper_api_key`	Yes*	—	Serper.dev API key (*portal → `agent_secrets.env` via sync-secrets)
`serper_endpoint`	No	`https://google.serper.dev/search`	API endpoint
`outreachmagic_home`	No	auto-detect	Path to outreachmagic skill
`max_people_per_run`	No	50	Batch size limit
`dedup_before_search`	No	true	Check outreachmagic before Serper
`serper_num_results`	No	10	Results per Serper query
`serper_gl`	No	`us`	Country code
`serper_hl`	No	`en`	Language

Enrichment query patterns (SQL)

Use via pipeline.py query --sql '…' --params '[…]' --json.

Leads that still need Serper (no LinkedIn, not yet attempted):

SELECT l.id, l.name, l.company
FROM leads l
JOIN workspace_lead_tags n ON n.lead_id = l.id AND n.tag = ?
JOIN workspaces w ON n.workspace_id = w.id
WHERE w.slug = ?
  AND (l.linkedin_url IS NULL OR l.linkedin_url = '')
  AND l.id NOT IN (
    SELECT lead_id FROM workspace_lead_tags
    WHERE tag = 'serper_attempted' AND workspace_id = w.id
  )

Params: ["nace", "your_workspace"]

Enrichment attempted but failed (retry-eligible when stale):

SELECT l.id, l.name, l.company, l.updated_at
FROM leads l
JOIN workspace_lead_tags s ON s.lead_id = l.id AND s.tag = 'serper_attempted'
JOIN workspaces w ON s.workspace_id = w.id
WHERE w.slug = ?
  AND (l.linkedin_url IS NULL OR l.linkedin_url = '')
  AND l.updated_at < datetime('now', '-30 days')

Params: ["your_workspace"]

What this skill does NOT do

❌ Call Outreach Magic person-research API (/v1/person-research)
❌ Use external LLM APIs (Gemini, OpenAI, etc.) for extraction
❌ Scrape HTML pages or LinkedIn directly
❌ Guess email addresses (use email-finder skill)
❌ Write raw SQL or ad-hoc DB mutations (use import-profiles / add-lead; reads via pipeline.py query)
❌ Upload to remote servers (local-only by default)

Platform Support

Platform	Install	Skill path
Hermes	outreachmagic/outreachmagic — `install.sh --platform hermes`	`~/.hermes/skills/lead-enrich/`
Cursor	`install.sh --platform cursor`	`~/.cursor/skills/lead-enrich/`
Claude Code	`install.sh --platform claude`	`~/.claude/skills/lead-enrich/`

Hermes: Real files live under ~/.hermes/skills/. Each profile uses symlinks only (profiles/<name>/skills/lead-enrich → ../../../skills/lead-enrich). Do not copy the skill into a profile directory.

Learn more at outreachmagic.io.

Troubleshooting

Problem	Fix
Stale skill or empty DB	Re-run `install.sh --platform <name>`. Check `pipeline.py paths` for `warning`.
"No outreachmagic found"	Set `outreachmagic_home` in config.json to the absolute path
Serper 400 "not allowed"	Query too restrictive — fallback to simpler template
`import-profiles` rejects row	Requires email, LinkedIn, or `id` (lead_id). Use `stamp-attempted` for tag-only updates
Serper credits wasted on re-runs	Use `batch-check --skip-tagged`; ensure `serper_attempted` is stamped on save
Empty extraction	Serper results too thin — try broad queries, or mark confidence `low`
`ambiguous` on check	Name matched wrong company — run Serper or `check --force`
Team / group award row	`batch-check` returns `team_award` — skip research
outreachmagic not found	Install outreachmagic/outreachmagic or set `outreachmagic_home`
Need email find	Installed with the suite — see `references/email-finder.md`

Demander à votre IA préférée

Documentation