# Company Prospecting

> Build and maintain a separate company-to-people outreach prospect tracker for job applications. Use when you want the top 3 recruiter, alumni, engineer, or employee contacts per company in markdown, plus Apollo email lookup status that stays separate from application-trackers/applications.md.

- Skill: `speciliam/company-prospecting` (Agent Skill, multi-file: 6 files)
- Install (CLI): `npx skillmds@latest add speciliam/company-prospecting`
- Raw SKILL.md: https://api.skillmd.com/api/skills/speciliam/company-prospecting/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Docs & Writing
- Author: SpeciLiam (https://skillmd.com/u/speciliam)
- Updated: 2026-09-21
- Page: https://skillmd.com/skills/speciliam/company-prospecting

---


# Company Prospecting

Use this skill when you want a separate tracker of who to contact at each company before email outreach.

For full recruiting sessions, start with `recruiting-pipeline`; it will call this skill after LinkedIn lanes identify companies that still need deeper recruiter/engineer prospecting or Apollo lookup.

## Goal

Turn each company into a small prospect list:

- start from active rows in `application-trackers/applications.md`
- create a separate queue in `application-trackers/outreach-prospects.md`
- find up to 3 strong people per company
- look up their Apollo emails
- keep company-level and person-level progress out of the application tracker

Apollo lookup should be used to find a real matched email. Do not guess email patterns from a person's name and employer.

## Default ranking

For each company, prioritize prospects in this order:

1. recruiter for the role or hiring team
2. University of Georgia alumni at the company
3. engineer on the likely team
4. another relevant employee with a visible path to reply

Every queued company should have at least one recruiter prospect and one engineer prospect when both can be found. If recruiter confidence is low, still keep the engineer lane rather than treating recruiter outreach as enough.

## Seed the separate tracker

Create or refresh the company queue from the organized tracker data when available, with markdown fallback:

```bash
python3 skills/company-prospecting/scripts/sync_company_prospect_tracker.py
```

Useful filters:

```bash
python3 skills/company-prospecting/scripts/sync_company_prospect_tracker.py --limit 20
python3 skills/company-prospecting/scripts/sync_company_prospect_tracker.py --company "Navan"
```

This keeps `application-trackers/outreach-prospects.md` as the source of truth for prospecting.

The sync script reads `application-visualizer/src/data/tracker-data.json` first because it has normalized rows and links. If that generated cache is missing, it falls back to `application-trackers/applications.md`.

Refresh the cache before a large prospecting pass:

```bash
python3 skills/application-visualizer-refresh/scripts/refresh_visualizer_data.py
```

## Find the next companies that still need names

```bash
python3 skills/company-prospecting/scripts/build_company_prospect_targets.py
```

This highlights companies that still have fewer than 3 prospects or are missing Apollo emails.

## Record prospects

After identifying a person on LinkedIn, record them immediately:

```bash
python3 skills/company-prospecting/scripts/record_company_prospect.py \
  --company "Navan" \
  --posting-key "4401351489" \
  --priority 1 \
  --target-type recruiter \
  --name "Jane Smith" \
  --title "Technical Recruiter" \
  --linkedin-url "https://www.linkedin.com/in/jane-smith/" \
  --email-status "Needs Apollo"
```

After Apollo finds an email, update the same row:

```bash
python3 skills/company-prospecting/scripts/record_company_prospect.py \
  --company "Navan" \
  --posting-key "4401351489" \
  --priority 1 \
  --name "Jane Smith" \
  --apollo-email "jane@navan.com" \
  --email-status "Ready"
```

Use `target-type` values like `recruiter`, `alumni`, `engineer`, or `general`.

## Apollo queue export

Export the exact rows that still need Apollo verification:

```bash
python3 skills/company-prospecting/scripts/export_apollo_queue.py
```

Useful filters:

```bash
python3 skills/company-prospecting/scripts/export_apollo_queue.py --limit 25
python3 skills/company-prospecting/scripts/export_apollo_queue.py --format csv
python3 skills/company-prospecting/scripts/export_apollo_queue.py --company "Navan"
```

This only emits prospects whose email is still missing or marked as needing Apollo.

Recommended statuses:

- `Needs Apollo` when the prospect is selected but not looked up yet
- `Needs Review` when Apollo returns a weak or ambiguous match
- `Ready` when Apollo returns a verified email worth using
- `No Match` when Apollo cannot find a reliable work email

## Expected workflow

1. run `resume-tailor`
2. update `applications.md`
3. run this skill to seed the separate company prospect tracker
4. for each queued company, find the top 3 names on LinkedIn
5. export the `Needs Apollo` queue and use Apollo to look for real email addresses
6. record names, titles, LinkedIn URLs, Apollo emails, and status in `outreach-prospects.md`
7. use that tracker for outreach batching later

## Guardrails

- Keep this tracker separate from `applications.md`.
- Prefer real people over generic company inboxes.
- Do not invent email addresses or infer them from common company patterns.
- Mark uncertain or partial Apollo matches in `Notes`.
- Keep the top 3 focused; do not spray extra names into the tracker unless the user asks.