End To End Leadership Change Signal

End-to-End Leadership Change Signal

Full pipeline from signal discovery to outreach-ready campaign in a single run. No company list required — this skill discovers recent leadership changes from the web, verifies them via Apollo, evaluates relevance to your product, drafts personalized outreach, and packages everything for your outreach tool.

Why leadership changes work: New leaders re-evaluate everything in their first 90 days. They inherit a vendor stack they didn't choose, a team they didn't build, and KPIs they need to hit fast. They're the most receptive buyers in any organization because they want quick wins, have mandate (and often budget) to make changes, and haven't yet formed loyalty to existing vendors.

What makes this "end-to-end": The base leadership-change-outreach skill requires a company list as input. This composite adds a discovery layer — scanning multiple web sources for leadership change announcements — so you can go from zero to outreach campaign without any input list.

When to Auto-Load

Load this composite when:

• User says "find companies with new leadership and reach out to them"
• User says "end-to-end leadership change signal", "leadership change pipeline", "executive change outreach from scratch"
• User wants to go from zero to outreach campaign based on leadership change signals
• User has no existing company list but wants to target companies with new executives

Do NOT load if:

• User already has a company list → use leadership-change-outreach instead
• User only wants to monitor leadership changes without outreach → use web search manually

Included Skills

Architecture

┌──────────────────────────────────────────────────────────────────────────────┐
│                  END-TO-END LEADERSHIP CHANGE SIGNAL                         │
│                                                                              │
│  ┌───────────┐   ┌──────────┐   ┌──────────┐   ┌──────────┐   ┌──────────┐ │
│  │ DISCOVER  │──▶│ VERIFY & │──▶│ EVALUATE │──▶│  DRAFT   │──▶│  LAUNCH  │ │
│  │ Signals   │   │ ENRICH   │   │ & RANK   │   │ Outreach │   │          │ │
│  └───────────┘   └──────────┘   └──────────┘   └──────────┘   └──────────┘ │
│       │                │              │              │               │       │
│  Multi-source     Apollo free    Role relevance  Timing-aware    Tool-      │
│  web scan         search +       + timing +      personalized   agnostic    │
│  (no input list)  enrichment     background       sequences     export      │
│                   by ID          scoring          (LLM)                     │
└──────────────────────────────────────────────────────────────────────────────┘

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

Leader Relevance Mapping

Contact Finding Config

Outreach Config

Your Company Context

Store config in: clients/<client-name>/config/end-to-end-leadership-change-signal.json or equivalent.

Step 1: Discover Leadership Change Signals

Purpose: Scan multiple web sources for recent leadership changes matching the target criteria. No company list input needed.

Input Contract

target_titles: string[]            # From config: ["VP Sales", "CRO", "CMO"]
target_industries: string[]        # From config (optional)
lookback_days: integer             # From config (default: 90)
min_company_size: string | null    # From config (optional)

Process

Run all source searches in parallel:

A) Web Search (WebSearch tool — free)

Run 6-8 varied queries:

• "appointed as VP Sales" OR "named VP of Sales" 2026
• "new CRO" OR "new Chief Revenue Officer" announced 2026
• "joins as" AND ("VP" OR "Chief") AND 2026
• "promoted to VP" OR "promoted to Chief" 2026
• "excited to announce" AND ("VP" OR "SVP" OR "Chief") AND ("hired" OR "appointed" OR "joins")
• site:businesswire.com OR site:prnewswire.com "appointed" AND ("VP" OR "Chief") 2026
• "[industry] company" AND ("new hire" OR "appointed") AND ("VP" OR "Chief") (if industry filter specified)
• "executive appointment" OR "leadership appointment" AND "[target title]" 2026

For each result, extract: person name, new title, company name, company domain, previous company, previous title, announcement date, source URL.

B) Twitter Search (twitter-scraper — ~$0.05-0.10)

python3 skills/twitter-scraper/scripts/search_twitter.py \
  --query "\"excited to share\" OR \"thrilled to announce\" AND (\"VP\" OR \"Chief\" OR \"Head of\") AND (\"new role\" OR \"joining\" OR \"starting\")" \
  --since <lookback-start> --until <today> --max-tweets 50 --output json

Founder/executive "new role" announcements often appear on Twitter/X before press releases. These are high-signal because the person themselves is announcing.

C) Hacker News (hacker-news-scraper — free)

python3 skills/hacker-news-scraper/scripts/search_hn.py \
  --query "appointed VP,new CRO,new CMO,executive hire" \
  --days <lookback_days> --min-points 3 --output json

HN "Who's Hiring" threads and company announcement posts sometimes surface executive moves at tech companies.

D) Reddit Search (reddit-scraper — ~$0.05-0.10)

python3 skills/reddit-scraper/scripts/search_reddit.py \
  --subreddit "startups,SaaS,technology,business" \
  --keywords "new VP,new CRO,new CMO,appointed,executive hire,leadership change" \
  --days <lookback_days> --sort hot --output json

E) LinkedIn Activity (linkedin-profile-post-scraper — optional)

If available, search for "new role" and "starting a new position" announcements from people with target titles. LinkedIn is the richest source of leadership change signals — most executives post when they change roles.

Consolidation

1. Deduplicate across sources. Same person+company from multiple sources = higher confidence.
2. Filter by target titles, industries, and min company size.
3. Score each discovered change:
   • +3: Appears in multiple sources
   • +3: Person announced it themselves (Twitter/LinkedIn post)
   • +2: Title matches target buyer titles exactly
   • +2: External hire (not internal promotion)
   • +1: Announced within last 30 days
   • +1: Industry matches target
   • +1: Company recently raised funding (compounding signal)
   • -1: Title is tangential to target
   • -2: Announcement is 60+ days old
4. Rank by score descending.
5. Extract domains for all discovered companies (needed for Step 2 Apollo verification).

Output Contract

discovered_changes: [
  {
    person_name: string
    new_title: string
    company_name: string
    company_domain: string
    company_industry: string | null
    previous_company: string | null
    previous_title: string | null
    announcement_date: string
    source_urls: string[]
    sources: string[]               # ["web", "twitter", "hn", "reddit", "linkedin"]
    self_announced: boolean         # Person announced it themselves
    confidence: "high" | "medium"
    score: integer
  }
]

Human Checkpoint

Found X leadership changes matching your criteria:

| Rank | Person | New Title | Company | Previous Role | Date | Sources | Confidence |
|------|--------|-----------|---------|---------------|------|---------|------------|
| 1    | Jane Smith | VP Sales | Acme Corp | Dir Sales @ Beta Inc | 2026-03-15 | web, twitter, linkedin | High |
| 2    | Tom Brown | CRO | Gamma Inc | VP Rev @ Delta Co | 2026-03-01 | web | Medium |
| ...  | ...    | ...       | ...     | ...           | ...  | ...     | ...        |

Proceed to Apollo verification? You can remove any entries before continuing.

Step 2: Verify & Enrich via Apollo

Purpose: Verify discovered leadership changes using Apollo's employment history data, and enrich with email, LinkedIn URL, and full career history. This replaces Step 1 of leadership-change-outreach — instead of scanning all employees at given companies, we target-verify the specific people we discovered.

Input Contract

discovered_changes: [...]           # From Step 1 output
titles: string[]                    # From config (Apollo search titles)
lookback_days: integer              # From config

Process

Phase 1: Apollo Free Search — Targeted Verification

For each discovered company domain, search Apollo for the specific person:

filters = {
    'q_organization_domains': company_domain,
    'person_titles': [discovered_new_title],  # Narrow to the specific title
    'per_page': 25,
    'page': 1
}

Match results against the discovered person name (fuzzy match — Apollo obfuscates last names in free tier, so match on first name + title + company).

Bonus discoveries: While searching for the known person, Apollo may surface OTHER recent leadership changes at the same company that web search missed. Include these as bonus finds.

Phase 2: Local Post-Filter

Apply the strict post-filter from leadership-change-outreach to remove noise:

• Reject non-GTM functions (engineering, talent, legal, etc.)
• Reject regional/sub-segment roles (AVP, EMEA-specific, etc.)
• Reject Apollo garbage ("related to search terms")
• Require valid title prefix (VP/SVP/Head of/Chief/CRO/CMO/CCO/President)

Phase 3: Apollo Enrichment by ID (1 credit per person)

For each matched person, enrich using the id from free search:

url = "https://api.apollo.io/api/v1/people/match"
payload = {"api_key": api_key, "id": person_id}

Returns: full name, employment_history (with start_date/end_date), linkedin_url, email + email_status, location.

Rate limiting: 0.5s delay every 5 requests. Respect Retry-After on 429s.

Phase 4: Change Verification

Cross-reference Apollo employment history against web-discovered data:

• Confirmed: Apollo start_date on current role aligns with web announcement date
• Enriched: Apollo provides additional details not in web announcement (previous roles, tenure)
• Corrected: Apollo shows different timing or title than web reported — use Apollo as ground truth
• Unverified: Person not found in Apollo — keep web data but flag as unverified

Determine change type from employment history:

• new_hire: Previous role was at a different company
• internal_promotion: Previous role was at the same company

Output Contract

verified_changes: [
  {
    company: {
      name: string
      domain: string
      industry: string | null
    }
    new_leader: {
      full_name: string
      new_title: string
      start_date: string              # ISO date (month granularity from Apollo, or announcement date)
      previous_company: string
      previous_title: string
      change_type: "new_hire" | "internal_promotion"
      linkedin_url: string | null
      email: string | null
      email_status: string | null
      city: string | null
      state: string | null
      country: string | null
      verification: "confirmed" | "enriched" | "corrected" | "unverified"
    }
    discovery: {
      sources: string[]
      self_announced: boolean
      score: integer
    }
  }
]
bonus_discoveries: [...]             # Additional changes found via Apollo that weren't in web results

Output Files

Save two files:

1. CSV (leadership-change-scan.csv) — all verified people sorted by start_date descending
2. Markdown (leadership-change-discovery.md) — formatted report with source summary

Human Checkpoint

## Apollo Verification Results

Discovered: X changes → Verified: Y → Bonus discoveries: Z

| Person | Title | Company | Started | Change Type | Verification | Email |
|--------|-------|---------|---------|-------------|-------------|-------|
| Jane Smith | VP Sales | Acme Corp | 2026-03-01 | new_hire | Confirmed | jane@acme.com (verified) |
| Tom Brown | CRO | Gamma Inc | 2026-02-01 | new_hire | Enriched | tom@gamma.io (guessed) |

Credits used: Y + Z

Proceed with relevance evaluation? (Y/n)

Step 3: Evaluate Relevance & Prioritize

Purpose: For each verified leadership change, evaluate whether the new leader is relevant to your product and determine the best outreach approach. Pure LLM reasoning — no external tools needed.

This step follows the leadership-change-outreach Step 2 evaluation framework exactly.

Input Contract

verified_changes: [...]              # From Step 2 output
your_company: {
  description: string
  pain_point: string
  proof_points: string[]
  quick_wins: string[]
  before_state: string
}
buyer_leader_titles: string[]
champion_leader_titles: string[]
aligned_leader_titles: string[]

Process

For each leadership change, evaluate across three dimensions:

A) Role Relevance

B) Timing Window

C) Background Signal

D) Discovery Signal Boost

Layer the discovery signal strength on top of the standard evaluation:

• Self-announced (posted about it on Twitter/LinkedIn): +1 tier. They're publicly signaling openness — great time to engage.
• Multi-source (appeared in 3+ sources): Higher profile change, likely a strategic hire with budget behind it.
• Compounding signal (company also recently raised / is hiring aggressively): Multiple buying signals compound. Prioritize these.

Scoring

• Tier 1 (Act Today): Direct buyer + <30 days in role + external hire. OR: any role relevance + self-announced + <30 days.
• Tier 2 (Act This Week): Direct buyer 30-60 days in, OR champion <30 days, OR came from a customer/competitor customer, OR multi-source with compounding signals.
• Tier 3 (Queue): Aligned mandate, OR 60-90 days in role, OR internal promotion with champion title.
• Drop: No role relevance, OR >90 days in role with weak fit.

For each qualified leader, generate:

• Relevance reasoning: Why this leader would care about your product right now
• Outreach angle: The specific hook based on their role + timing + background
• Key insight: One thing about their situation that makes the outreach personal

Output Contract

qualified_leaders: [
  {
    ...verified_change_fields,
    role_relevance: "direct_buyer" | "champion" | "aligned_mandate"
    timing_window: "honeymoon" | "assessment" | "action" | "established"
    background_signal: string
    discovery_boost: string | null    # "self_announced", "multi_source", "compounding"
    priority_tier: "tier_1" | "tier_2" | "tier_3"
    relevance_reasoning: string
    outreach_angle: string
    key_insight: string
  }
]
dropped_leaders: [
  { name: string, company: string, drop_reason: string }
]

Human Checkpoint

## Relevance Evaluation

### Tier 1 — Act Today (X leaders)
| Leader | Company | Title | Days In | Type | Boost | Angle |
|--------|---------|-------|---------|------|-------|-------|
| Jane Smith | Acme | VP Sales | 15 | Direct buyer, Honeymoon | Self-announced | "You just posted about scaling outbound at Acme..." |

### Tier 2 — Act This Week (X leaders)
| ... |

### Tier 3 — Queue (X leaders)
| ... |

### Dropped (X leaders)
| Leader | Company | Reason |
|--------|---------|--------|

Approve before we draft outreach? You can promote, demote, or drop any leader.

Step 4: Draft Personalized Outreach

Purpose: Draft outreach to each new leader that demonstrates you understand their situation. Pure LLM reasoning.

Input Contract

qualified_leaders: [...]             # From Step 3 output
your_company: {
  description: string
  pain_point: string
  proof_points: string[]
  quick_wins: string[]
  before_state: string
}
sequence_config: {
  touches: integer                   # Default: 3
  timing: integer[]                  # Default: [1, 5, 12]
  tone: string                      # Default: "professional-sharp"
  cta: string                       # Default: "15-min intro call"
}

Process

1. Enrich personalization context (optional, high-value):

   • Use linkedin-profile-post-scraper to check if the leader posted about their new role or shared priorities
   • Use web search to check for any company news beyond the leadership change

2. Select framework based on role relevance:

   • Direct buyer → Signal-Proof-Ask (reference the role change, show proof, ask for time)
   • Champion → BAB (before: the current state they inherited / after: what it looks like with your product / bridge: quick wins in 30 days)
   • Aligned mandate → PAS (problem: what their mandate implies / agitate: why current tools fall short / solve: your product)

3. Build personalization from enriched profile:

   Personalization Element	Source	Example
   Role change reference	Step 1 discovery	"Congrats on the VP Sales role at Acme"
   Timing-aware framing	Step 3 timing_window	"Now that you've had a month to assess..."
   Background connection	Step 3 background_signal	"At Competitor Inc you used [similar tool]..."
   Self-announcement reference	Step 1 self_announced	"Your post about scaling outbound resonated..."
   Company context	Step 2 enrichment	"With Acme's recent growth plans..."
   Quick win offer	Config quick_wins	"Most VPs see [result] within their first 30 days with us"

4. Adapt email angle by timing window:

   Window	Touch 1 Approach	Subject Line Pattern
   Honeymoon (0-30d)	Welcome + quick win offer. Light touch — they're still onboarding.	"Quick win for your first 90 days at {company}"
   Assessment (31-60d)	Acknowledge they've been evaluating. Offer peer comparison.	"What other {title}s are doing differently"
   Action (61-90d)	They're making decisions now. Be direct about value.	"{Product} for {company}'s {goal}"

5. Follow email-drafting skill rules:

   • Touch 1: 50-90 words. Reference the role change + one personalization hook + soft CTA.
   • Touch 2: 30-50 words. New proof point or quick-win offer.
   • Touch 3: 20-40 words. Peer social proof or graceful breakup.
   • Tone: professional-sharp by default. Executives respond to conciseness and specificity, not chattiness.

Output Contract

email_sequences: [
  {
    leader: { full_name, email, title, company_name, role_relevance, timing_window, priority_tier }
    sequence: [
      {
        touch_number: integer
        send_day: integer
        subject: string
        body: string
        framework: string
        personalization_elements: {
          role_change: string
          timing: string
          background: string
          company_context: string
          linkedin_reference: string | null
        }
        word_count: integer
      }
    ]
  }
]

Human Checkpoint

Present samples covering different timing windows and role types:

## Sample Outreach for Review

### Jane Smith, VP Sales @ Acme Corp
Tier 1 | Direct buyer | Honeymoon window (15 days) | Previously at Competitor Inc | Self-announced on LinkedIn

**Touch 1 — Day 1**
Subject: Quick win for your first 90 days at Acme
> Hi Jane — congrats on the move to Acme. Your post about building a
> "repeatable outbound engine" is exactly what drew me in.
>
> [Product] is what [peer company] brought in during a similar transition —
> [specific result] within 30 days.
>
> Worth a 15-minute intro?

**Touch 2 — Day 5**
Subject: The playbook from Competitor Inc → Acme
> [full email referencing their background]

**Touch 3 — Day 12**
Subject: One last thought
> [breakup 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. Identical to leadership-change-outreach Step 5.

Input Contract

email_sequences: [...]               # From Step 4 output
outreach_tool: string                # From config
outreach_channels: string            # From config

Process

If outreach_channels includes LinkedIn:

• Chain to linkedin-outreach for 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]
Signal type: Leadership change
Contacts: X new leaders 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

Total: ~15-25 minutes from "find me companies with new leaders to reach out to" to outreach-ready campaign.

Cost

Typical run: $0.10-0.30 + 20-40 Apollo credits depending on discovery volume.

Key Difference from Base Leadership Change Outreach

The base skill casts a wide net across known companies. This end-to-end version discovers the signal first, then uses Apollo for verification and enrichment — typically using fewer credits because you're only enriching confirmed changes rather than scanning entire orgs.

Tips

• Run bi-weekly or monthly — leadership changes happen constantly but the 30-60 day outreach window means you don't need real-time monitoring
• Self-announced changes are the highest-quality signals — the person actively shared their move, making them more receptive to engagement
• External hires are stronger signals than internal promotions — external hires re-evaluate everything, internal promotions already know the vendor stack
• The 30-60 day window is the sweet spot — too early and they're onboarding, too late and they've already decided
• Reference their LinkedIn "new role" post if they made one — it shows you've done your homework and reveals their stated priorities
• Don't mention the predecessor — saying "replacing John" can be awkward. Just reference the role and the company
• Quick wins beat big transformations — new leaders need early credibility. Position as "a win in your first quarter" not "a 6-month implementation"
• If they came from a customer of yours, that's the strongest hook — lead with "You used [product] at [old company] — want to bring it to [new company]?"
• Compounding signals are gold — a new VP Sales at a company that just raised Series B and is hiring 5 SDRs? That's three signals pointing to the same buying moment
• Track contacts in contact-cache to avoid duplicate outreach across runs

Example Prompt

"Find companies that recently hired new VPs of Sales or CROs. We sell AI-powered sales coaching software. Our buyers are VP Sales and CRO. Target SaaS companies with 100+ employees. Use Apollo for verification and export to CSV."

The agent should run the full pipeline: discover → verify via Apollo → evaluate relevance → draft emails → export CSV.