Full pipeline from signal discovery to outreach-ready campaign in a single run. No company list required — this skill discovers recently-funded companies from scratch, qualifies them against your context, finds the right people, drafts personalized emails, and packages everything for your outreach tool.
When to Auto-Load
Load this composite when:
- User says "find recently funded companies and reach out to them"
- User says "end-to-end funding signal", "funding pipeline", "funding outreach from scratch"
- User wants to go from zero to outreach campaign based on funding signals
- User has no existing company list but wants to target companies that just raised
Do NOT load if:
- User already has a company list → use
funding-signal-outreachinstead - User only wants to monitor signals without outreach → use
funding-signal-monitorinstead
Architecture
This composite chains funding-signal-monitor (discovery) into funding-signal-outreach (qualification → contacts → emails → handoff). The monitor's output feeds directly into the outreach pipeline.
┌──────────────────────────────────────────────────────────────────────────┐
│ END-TO-END FUNDING SIGNAL │
│ │
│ ┌───────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────┐ │
│ │ DISCOVER │──▶│ QUALIFY │──▶│ FIND │──▶│ DRAFT │──▶│LAUNCH│ │
│ │ Signals │ │ & Rank │ │ People │ │ Emails │ │ │ │
│ └───────────┘ └──────────┘ └──────────┘ └──────────┘ └──────┘ │
│ │ │ │ │ │ │
│ Multi-source Your company Buyer personas Signal-based Tool- │
│ web scan context (LLM) + contact tool personalization agnostic│
│ (no input list) (LLM) export │
└──────────────────────────────────────────────────────────────────────────┘
Step 0: Configuration (One-Time Setup)
On first run, collect and store all configuration needed for the full pipeline. Skip on subsequent runs.
Signal Discovery Config
| Question | Options | Stored As |
|---|---|---|
| What funding stages are you targeting? | Seed, Series A, Series B, Series C, Series D+ | target_stages |
| Any industry filters? | SaaS, AI, fintech, healthtech, devtools, etc. (or "all") | target_industries |
| Minimum funding amount? | e.g. "$5M" or none | min_amount |
| How far back should we search? | 7 / 14 / 30 days | lookback_days |
Contact Finding Config
| Question | Options | Stored As |
|---|---|---|
| How should we find contacts? | Apollo / LinkedIn Sales Nav / Clearbit / Web search / Manual | contact_tool |
| Do you have API access? | Yes (provide key) / No (use web search) | contact_api_access |
Outreach Config
| Question | Options | Stored As |
|---|---|---|
| Where do you want outreach sent? | Smartlead / Instantly / Outreach.io / Lemlist / Apollo / CSV export | outreach_tool |
| Email or multi-channel? | Email only / Email + LinkedIn | outreach_channels |
Your Company Context
| Question | Purpose | Stored As |
|---|---|---|
| What does your company do? (1-2 sentences) | Qualification + email personalization | company_description |
| What problem do you solve? | Email hook | pain_point |
| Who are your ideal buyers? (titles, departments) | Contact finding filters | buyer_personas |
| Name 2-3 proof points (customers, metrics, results) | Email credibility | proof_points |
| What's your product's price range? (SMB / Mid-Market / Enterprise) | Funding stage qualification | price_tier |
Store config in: clients/<client-name>/config/end-to-end-funding-signal.json or equivalent.
Step 1: Discover Funding Signals
Purpose: Scan multiple web sources for recent funding announcements matching the target criteria. No company list input needed.
This step uses the funding-signal-monitor skill's multi-source approach.
Input Contract
target_stages: string[] # From config: ["Series A", "Series B"]
target_industries: string[] # From config (optional)
min_amount: string | null # From config (optional)
lookback_days: integer # From config (default: 7)
Process
Run all source searches in parallel:
A) Web Search (WebSearch tool — free)
Run 4-6 varied queries:
"Series A announced this week 2026""Series B funding round 2026""startup raised Series A""raised $" AND "Series" AND "2026""[industry] startup funding"(if industry filter specified)
For each result, extract: company name, amount, stage, date, lead investors.
B) Twitter Search (twitter-scraper — ~$0.05-0.10)
python3 skills/twitter-scraper/scripts/search_twitter.py \
--query "\"excited to announce\" AND (\"raised\" OR \"Series A\" OR \"Series B\" OR \"funding\")" \
--since <lookback-start> --until <today> --max-tweets 50 --output json
Funding announcements often break on Twitter first via founder posts.
C) Hacker News (funding-signal-monitor helper script — free)
python3 skills/funding-signal-monitor/scripts/search_funding.py \
--stages "Series A,Series B" --days 7 --min-points 5 --output json
D) Reddit Search (reddit-scraper — ~$0.05-0.10)
python3 skills/reddit-scraper/scripts/search_reddit.py \
--subreddit "startups,SaaS,technology" \
--keywords "raised,Series A,Series B,funding round" \
--days 7 --sort hot --output json
Consolidation
- Deduplicate across sources. Same company from multiple sources = higher confidence.
- Filter by target stages, industries, and min amount.
- Score each company:
- +3: Appears in multiple sources
- +2: Stage matches target exactly
- +2: Industry matches target
- +1: High cloud likelihood (tech/SaaS/AI)
- +1: Announced within last 3 days
- -1: Stage outside target range
- -2: Non-tech industry (unless specifically targeted)
- Rank by score descending.
Output Contract
discovered_companies: [
{
name: string
domain: string
industry: string
funding_amount: string
funding_stage: string
funding_date: string
lead_investors: string[]
source_urls: string[]
sources: string[] # ["web", "twitter", "hn", "reddit"]
confidence: "high" | "medium"
score: integer
outreach_angle_hint: string # Stage-based angle from monitor
}
]
Human Checkpoint
Found X companies with recent funding matching your criteria:
| Rank | Company | Amount | Stage | Date | Sources | Confidence |
|------|---------|--------|-------|------|---------|------------|
| 1 | Acme | $15M | A | 2026-03-28 | web, twitter, hn | High |
| 2 | Beta | $40M | B | 2026-03-25 | web | Medium |
| ... | ... | ... | ... | ... | ... | ... |
Proceed to qualification? You can remove any companies before continuing.
Step 2: Qualify & Prioritize
Purpose: Rank discovered companies by relevance to your product. Pure LLM reasoning — no external tools needed.
Input Contract
discovered_companies: [...] # From Step 1 output
your_company: {
description: string
pain_point: string
buyer_personas: string[]
proof_points: string[]
price_tier: string
}
Process
For each discovered company, evaluate:
| Criterion | Weight | Assessment |
|---|---|---|
| Stage fit | High | Does funding stage match your price tier? A → SMB/mid-market, C → enterprise |
| Industry relevance | High | Is their industry one where your product solves a real problem? |
| Timing urgency | Medium | <14 days = urgent. 14-30 = viable. 30+ = cooling |
| Size signal | Medium | Post-raise team size. Enough people to need your product? |
| Round size | Low | Larger rounds = more budget for tooling |
Assign priority tiers:
- Tier 1 (Act Today): Stage fit + industry relevance + funded within 14 days
- Tier 2 (Act This Week): Two of three criteria, or 15-30 days with strong fit
- Tier 3 (Queue): Marginal fit or 30+ days old. Worth reaching out but not urgent
- Drop: No relevance to your product/market
For each qualified company, generate:
- Relevance reasoning: 1-2 sentences on why they'd care about your product now
- Outreach angle: Specific hook connecting their funding to your value
- Recommended approach: Direct pain-point, aspirational growth, or operational efficiency
Output Contract
qualified_companies: [
{
...discovered_company_fields,
priority_tier: "tier_1" | "tier_2" | "tier_3"
relevance_reasoning: string
outreach_angle: string
recommended_approach: string
estimated_team_size: string
}
]
dropped_companies: [
{ name: string, drop_reason: string }
]
Human Checkpoint
## Qualification Results
### Tier 1 — Act Today (X companies)
| Company | Stage | Amount | Angle | Why |
|---------|-------|--------|-------|-----|
### Tier 2 — Act This Week (X companies)
| Company | Stage | Amount | Angle | Why |
|---------|-------|--------|-------|-----|
### Tier 3 — Queue (X companies)
| Company | Stage | Amount | Angle | Why |
|---------|-------|--------|-------|-----|
### Dropped (X companies)
| Company | Reason |
|---------|--------|
Approve before we find contacts? You can promote, demote, or drop any company.
Step 3: Find Decision-Makers
Purpose: For each qualified company, find people matching your buyer personas.
Input Contract
qualified_companies: [...] # From Step 2 output
buyer_personas: [
{
title_patterns: string[]
department: string
seniority: string
role_type: "buyer" | "champion" | "user"
}
]
contact_tool: string # From config
max_contacts_per_company: integer # Default: 3-5
Process
For each qualified company, use the configured contact_tool:
- Search for people matching buyer personas (title/seniority filters)
- Collect per person: full name, title, email, LinkedIn URL, role type
- Prioritize: Buyers → Champions → Users
- Cap at max_contacts_per_company (3-5)
- Deduplicate against
contact-cacheto avoid repeat outreach across runs
Output Contract
contacts: [
{
person: {
full_name: string
first_name: string
last_name: string
title: string
email: string | null
linkedin_url: string | null
role_type: "buyer" | "champion" | "user"
}
company: {
name: string
domain: string
funding_amount: string
funding_stage: string
funding_date: string
priority_tier: string
outreach_angle: string
relevance_reasoning: string
}
}
]
contacts_without_email: [...] # Same structure, flagged for manual lookup
Human Checkpoint
## Contacts Found
### Acme Corp (Tier 1 — Series A, $15M)
| Name | Title | Role | Email | LinkedIn |
|------|-------|------|-------|----------|
### Beta Inc (Tier 2 — Series B, $40M)
| Name | Title | Role | Email | LinkedIn |
|------|-------|------|-------|----------|
Total: X contacts across Y companies (Z without email)
Approve before we draft emails?
Step 4: Draft Personalized Emails
Purpose: For each contact, draft a personalized email sequence connecting the funding signal to your product's value. Pure LLM reasoning.
Input Contract
contacts: [...] # From Step 3 output
your_company: {
description: string
pain_point: string
proof_points: string[]
}
sequence_config: {
touches: integer # Default: 3
timing: integer[] # Default: [1, 5, 12] (days)
personalization_tier: 1 | 2 | 3 # Default: 2
tone: string # Default: "casual-direct"
cta: string # Default: "15-min call"
}
Process
-
Select framework: Funding signal → Signal-Proof-Ask (reference raise, show proof, soft ask). If funding is for the exact problem you solve → BAB (before/after).
-
Build personalization context per contact:
Field Source Signal reference Step 1 — "Congratulations on the $15M Series A" Company context Step 2 — "As you scale post-raise..." Role-specific pain Step 3 role_type — Buyer→budget, Champion→friction, User→workflow Proof point Config — "Companies like [peer] use us to..." Outreach angle Step 2 — "Scale fast with fresh capital" -
Generate emails per
email-draftingskill rules:- Touch 1: 50-90 words. Hook with funding signal + proof + soft CTA
- Touch 2: 30-50 words. New angle (different proof point or asset offer)
- Touch 3: 20-40 words. Social proof drop or breakup
- All hard rules apply: no filler, no "just checking in", one CTA per email
-
By personalization tier:
- Tier 1: One template per touch with merge fields
- Tier 2: One template per (role_type + priority_tier) combination
- Tier 3: Unique email per contact
Output Contract
email_sequences: [
{
contact: { full_name, email, company_name, priority_tier, ... }
sequence: [
{
touch_number: integer
send_day: integer
subject: string
body: string
framework: string
word_count: integer
}
]
}
]
Human Checkpoint
Present 3-5 sample sequences (one per tier/role combination):
## Sample Emails for Review
### Jane Doe, VP Sales @ Acme Corp (Tier 1, Series A $15M)
**Touch 1 — Day 1**
Subject: Before the Series A hiring sprint
> [full email]
**Touch 2 — Day 5**
Subject: How [peer company] handled post-raise scaling
> [full email]
**Touch 3 — Day 12**
Subject: One last thought
> [full email]
Approve these samples? I'll generate the rest in the same style.
Iterate? Tell me what to change (tone, length, angle, CTA).
Step 5: Launch Campaign
Purpose: Package contacts + email sequences for the configured outreach tool.
Input Contract
email_sequences: [...] # From Step 4 output
outreach_tool: string # From config
outreach_channels: string # From config
Process
| Tool | Action |
|---|---|
| Smartlead | Chain to cold-email-outreach Smartlead MCP automation |
| Instantly | Generate Instantly-format CSV |
| Outreach.io | Generate Outreach-compatible CSV |
| Lemlist | Generate Lemlist-format CSV |
| Apollo | Generate Apollo sequence import CSV |
| CSV export | Generate generic CSV with all fields |
If outreach_channels includes LinkedIn:
- Chain to
linkedin-outreachfor LinkedIn message sequences - Output CSV for LinkedIn automation tool
Output Contract
campaign_package: {
tool: string
file_path: string
contact_count: integer
sequence_touches: integer
estimated_send_days: integer
next_action: string
}
Human Checkpoint
## Campaign Ready
Tool: [configured tool]
Contacts: X people across Y companies
Sequence: 3 touches over 12 days
Tier breakdown: Z Tier 1, W Tier 2, V Tier 3
File: output/{campaign-name}-{date}.csv
Ready to launch? (Final gate before emails are sent or files are created)
Execution Summary
| Step | What | Tool | Checkpoint | Time |
|---|---|---|---|---|
| 0 | Config | None | First run only | 5 min (once) |
| 1 | Discover signals | WebSearch + Twitter + HN + Reddit | Review discovered companies | 3-5 min |
| 2 | Qualify & rank | LLM reasoning | Approve tier rankings | 2-3 min |
| 3 | Find people | Configurable (Apollo, LinkedIn, etc.) | Approve contact list | 2-3 min |
| 4 | Draft emails | LLM reasoning | Review samples, iterate | 5-10 min |
| 5 | Launch | Configurable (Smartlead, CSV, etc.) | Final approval | 1 min |
Total: ~15-25 minutes from "find me funded companies to reach out to" to outreach-ready campaign.
Cost
| Component | Cost |
|---|---|
| Web Search | Free |
| Hacker News (Algolia) | Free |
| Twitter scraper (Apify) | ~$0.05-0.10 |
| Reddit scraper (Apify) | ~$0.05-0.10 |
| Contact finding (varies by tool) | $0-0.50 |
Typical run: $0.10-0.70 total depending on contact tool.
Tips
- Run weekly — funding signals have a 1-3 week outreach window before vendor flood
- Tier 1 contacts within 48 hours of funding announcement for maximum impact
- 3-5 contacts per company is the sweet spot to avoid carpet-bombing
- Multi-source appearances are the strongest signal — a company on TechCrunch + HN + Twitter is higher quality
- Don't mention the funding amount in emails unless public and impressive. Focus on what the funding means for them
- Track contacts in
contact-cacheto avoid duplicate outreach across weekly runs - Web Search is your best discovery source — Twitter and HN provide supplementary early signals
Example Prompt
"Find companies that raised Series A or B in the last week. We sell developer tools for API monitoring. Our buyers are VP Engineering and CTOs. Target SaaS and AI companies. Use web search for contacts and export to CSV."
The agent should run the full pipeline: discover → qualify → find contacts → draft emails → export CSV.