Skills
skills/nikiandr/goose-skills/news-signal-outreach

news-signal-outreach

SKILL.md

News Signal Outreach

The catch-all signal composite. Every other composite handles a specific signal type (funding, hiring, leadership change, champion move). This one handles everything else — any piece of news or public event that could create a reason to reach out.

A regulation change. A product recall. A competitor acquisition. A market expansion. A layoff. An earnings miss. A new partnership. An industry report. A conference keynote. A viral LinkedIn post. Any external event that shifts a company's priorities, creates urgency, or opens a window for your product.

Why this composite exists: The world generates an infinite stream of potential outreach triggers. The four structured signal composites handle the most common patterns. This composite handles the long tail — the unpredictable, opportunistic moments that often produce the best outreach because nobody else is sending a templated sequence about them.

When to Auto-Load

Load this composite when ANY of these are true:

  • User shares ANY URL (LinkedIn post, article, tweet, blog, news) and asks about a company or person mentioned in it
  • User says "came across", "saw this post", "found this article", "check this out", "is this relevant", "is this company a fit", "should we reach out"
  • User mentions a company or person they discovered from an external source (social media, news, conference, podcast, newsletter) and asks about relevance or fit
  • User asks "can we reach out to anyone based on this?"
  • User says "check if this news is relevant to our prospects", "news-based outreach", "trigger-based outreach"
  • User has a list of companies and wants to check recent news for outreach angles
  • The news doesn't fit neatly into funding, hiring, leadership change, or champion move categories
  • An upstream workflow surfaces a news item that needs evaluation

Key principle: If the user shares an external signal (URL, post, article, mention) and asks ANY question about the companies/people in it — load this composite. Don't wait for the word "outreach." The composite handles both evaluation-only (Steps 1-3) and full outreach (Steps 1-6).

Input Flexibility

This composite accepts three input modes:

Mode Input Example
News → Companies A news item. Extract companies/people mentioned, qualify them. "Here's an article about new FDA regulations on telehealth"
Companies → News A list of companies. Find recent news about them, evaluate relevance. "Check these 50 companies for any news we can use as an outreach angle"
Person → News A person or list of people. Find recent news about them or their company, evaluate relevance. "Check if any of these prospects have been in the news"

Step 0: Configuration (One-Time Setup)

On first run for a client/user, collect and store these preferences. Skip on subsequent runs.

ICP Definition

Question Purpose Stored As
What does your company do? (1-2 sentences) Relevance matching company_description
What problem do you solve? Connection angle identification pain_point
What industries do you sell to? ICP filter target_industries
What company sizes? ICP filter target_company_size
What geographies? ICP filter (optional) target_geographies
Any disqualifiers? Hard no's disqualifiers
Who are your buyers? (titles) Contact finding buyer_titles
Who are your champions? (titles) Contact finding champion_titles
Who are your users? (titles) Contact finding user_titles

Your Company Context

Question Purpose Stored As
What specific outcomes does your product deliver? Relevance angle building product_outcomes
Name 2-3 proof points (customers, metrics) Email credibility proof_points
What categories of news are most relevant to your product? Helps prioritize relevant_news_categories

Examples of relevant_news_categories:

# For a cybersecurity product:
relevant_news_categories: ["data breach", "compliance regulation", "security incident",
  "digital transformation", "cloud migration", "IPO/going public"]

# For a sales AI product:
relevant_news_categories: ["sales team scaling", "market expansion", "new product launch",
  "competitor acquisition", "cost cutting", "revenue miss"]

# For an HR tech product:
relevant_news_categories: ["layoffs", "rapid hiring", "remote work policy",
  "DEI initiative", "union activity", "culture crisis"]

Signal Detection Config

Question Options Stored As
How should we find news? Web search / Google News / RSS feeds / Social media news_tool
How far back should we look? (when scanning companies for news) 7 / 14 / 30 / 60 days lookback_days

Contact Finding & Outreach Config

Question Options Stored As
How should we find contacts? Apollo / LinkedIn / Clearbit / Web search contact_tool
Where do you want outreach sent? Smartlead / Instantly / Outreach.io / CSV export outreach_tool
Email or multi-channel? Email only / Email + LinkedIn outreach_channels

Store config in: clients/<client-name>/config/signal-outreach.json or equivalent.

Step 1: Parse & Extract

Purpose: Take the raw news input — whatever form it arrives in — and extract structured entities (companies, people) and the core event.

Input Contract

Three modes:

Mode A: News → Companies/People

news_input: {
  mode: "news_to_targets"
  items: [
    {
      type: "url" | "text" | "structured"
      content: string               # URL to article, raw text, or structured summary
      source: string | null          # "TechCrunch", "LinkedIn post", "user provided", etc.
    }
  ]
}

Mode B: Companies → News

news_input: {
  mode: "targets_to_news"
  companies: [
    {
      name: string
      domain: string
      industry?: string
    }
  ]
  lookback_days: integer
}

Mode C: People → News

news_input: {
  mode: "people_to_news"
  people: [
    {
      full_name: string
      company: string
      linkedin_url?: string
    }
  ]
  lookback_days: integer
}

Process

Mode A: News → Companies/People

  1. Fetch and parse the news content:

    • If URL → fetch the page, extract article text
    • If raw text → use as-is
    • If structured → use as-is

  2. Extract entities:

    • Companies mentioned (name, role in the story — subject, affected party, partner, competitor)
    • People mentioned (name, title, company, role in the story)
    • The core event (what happened, in one sentence)
    • Event category (regulation, acquisition, partnership, product launch, market event, crisis, expansion, contraction, etc.)
    • Date of event
    • Affected industries

  3. Expand if needed: If the news implies a broader set of affected companies beyond those mentioned:

    • "New FDA regulation on telehealth" → all telehealth companies, not just ones in the article
    • "Major data breach at [company]" → the breached company AND their competitors (who can capitalize)
    • "Industry report shows X trend" → companies in that industry

Mode B: Companies → News

  1. For each company, search for recent news using configured news_tool:

    • Web search: "{company_name}" AND (news OR announced OR launches OR raises OR expands OR partners) within lookback_days
    • Filter results against relevant_news_categories from config
    • Extract the same fields as Mode A for each news item found

  2. Group results: Company → list of news items, ranked by relevance to your product

Mode C: People → News

  1. For each person, search for recent news/activity:

    • Web search: "{full_name}" AND "{company}" within lookback_days
    • LinkedIn activity (if available): recent posts, shares, comments
    • Look for: promotions, speaking engagements, published articles, quoted in press, new projects

  2. Group results: Person → list of news items/activity

Output Contract

extracted_signals: [
  {
    entity: {
      type: "company" | "person"
      name: string
      company: string               # Company name (same as name if type=company)
      domain: string | null
      role_in_news: string           # "subject", "affected", "partner", "competitor", "mentioned"
    }
    news: {
      headline: string              # One-line summary of what happened
      event_category: string        # "regulation", "acquisition", "expansion", "crisis", etc.
      event_date: string
      full_summary: string          # 2-3 sentence summary
      source_url: string | null
      affected_industries: string[]
    }
  }
]

Human Checkpoint

## Extracted Signals

Source: [news source/input description]
Event: [one-line summary]
Category: [event category]

### Companies/People Extracted
| Entity | Type | Role in News | Industry |
|--------|------|-------------|----------|
| Acme Corp | Company | Subject | Healthcare |
| Jane Doe | Person | Quoted (CEO) | Healthcare |
| HealthTech sector | Industry | Affected | Healthcare |

Also evaluating: X companies in [affected industry] not directly mentioned

Proceed with ICP qualification? (Y/n)

Step 2: Qualify Against ICP

Purpose: For each extracted entity, determine if they're an ICP fit. Drop companies/people that don't match. Pure LLM reasoning — inherently tool-agnostic.

Input Contract

extracted_signals: [...]              # From Step 1 output
icp_criteria: {
  target_industries: string[]
  target_company_size: string
  target_geographies: string[]
  disqualifiers: string[]
}
your_company: {
  description: string
  pain_point: string
}

Process

For each entity:

  1. If entity is a company:

    • Check industry against target_industries
    • Estimate company size (from news context or quick web search)
    • Check geography if relevant
    • Check against disqualifiers
    • Result: Pass / Fail with reasoning

  2. If entity is a person:

    • Identify their company
    • Qualify the company through the same ICP checks above
    • Additionally check: is this person's role relevant? (matches buyer_titles, champion_titles, or user_titles)
    • Result: Pass / Fail with reasoning

  3. For entities implied but not mentioned (e.g., "all telehealth companies" from a regulation news):

    • Use web search or existing company lists to identify specific companies in the affected space
    • Qualify each against ICP
    • This step may surface new companies not in your existing pipeline

Output Contract

icp_qualified: [
  {
    entity: { ... }                   # From Step 1
    news: { ... }                     # From Step 1
    icp_assessment: {
      fit: "strong" | "moderate"
      industry_match: boolean
      size_match: boolean | "unknown"
      reasoning: string               # Why they're a fit
    }
  }
]
icp_disqualified: [
  {
    entity_name: string
    reason: string
  }
]

Human Checkpoint

## ICP Qualification

### Qualified (X entities)
| Entity | Type | Industry | Size | ICP Fit | Reasoning |
|--------|------|----------|------|---------|-----------|
| Acme Corp | Company | Healthcare SaaS | ~200 | Strong | Core ICP industry, right size |
| MedTech Inc | Company | HealthTech | ~500 | Moderate | Adjacent industry, large |

### Disqualified (X entities)
| Entity | Reason |
|--------|--------|
| BigPharma Co | Enterprise (50K+ employees) — above target size |

Approve qualified list?

Step 3: Identify Connection Angle

Purpose: This is the critical thinking step. For each ICP-qualified entity, determine the specific connection between the news event and your product. Why should they care about your product RIGHT NOW because of THIS news? Pure LLM reasoning — inherently tool-agnostic.

Input Contract

icp_qualified: [...]                  # From Step 2 output
your_company: {
  description: string
  pain_point: string
  product_outcomes: string[]
  proof_points: string[]
  relevant_news_categories: string[]
}

Process

For each qualified entity, answer three questions:

Question 1: "Why does this news create urgency for our product?"

Map the news event category to a product relevance pattern:

Event Category How It Creates Urgency Example
Regulation change They need to comply, your product helps them comply or adapt faster "New data privacy law → they need [your compliance tool] before enforcement date"
Acquisition / Merger Systems need integration, processes need standardization, new leadership evaluates stack "Acquired a company → need to unify [function your product handles]"
Market expansion New market = new challenges, need tools that scale "Expanding to EMEA → need [your product] for localized [function]"
Product launch Scaling up means scaling operations "Launching enterprise tier → need [your product] to handle enterprise [function]"
Competitive pressure Competitor moved, they need to respond "Competitor launched [X] → they need to level up [area your product addresses]"
Cost cutting / Layoffs Do more with less, automation becomes essential "Cut 15% of staff → need [your product] to maintain output with smaller team"
Crisis / Incident Reactive buying — they need a solution NOW "Data breach → urgently need [your security product]"
Partnership New partner = new workflows, new opportunities "Partnered with [company] → need [your product] to support the integration"
Earnings / Growth Over-performing = scaling challenges. Under-performing = efficiency pressure "Revenue grew 3x → [function your product handles] can't keep up manually"
Industry trend / Report Category awareness is high, they're thinking about this "Industry report says [trend] → they're likely evaluating solutions in this space"
Person-level news Published an article, spoke at a conference, posted on LinkedIn about a topic you solve "Posted about [pain] → they're actively thinking about this problem"

Question 2: "What's the specific angle?"

Craft a one-sentence connection:

"Because [news event], [company] now needs [specific outcome your product delivers]."

Examples:

  • "Because Acme just acquired BetaCo, they need to unify two separate CRM systems — exactly what [product] does in 30 days."
  • "Because the new HIPAA amendment takes effect in Q3, [company] needs to audit their data handling — [product] automates this."
  • "Because [person] just posted about struggling with [pain], they're actively looking for a solution — [product] solves this."

Question 3: "How strong is this connection?"

Strength Criteria Example
Direct The news explicitly describes a problem your product solves Layoff in your product's department → they need automation
Adjacent The news implies a downstream need your product addresses Market expansion → implies scaling, which implies need for your tool
Thematic The news is in the same category as your product's domain Industry report about the trend you're in → awareness play

Output Contract

connection_angles: [
  {
    entity: { ... }
    news: { ... }
    icp_assessment: { ... }
    connection: {
      urgency_reason: string          # Why this news creates urgency
      specific_angle: string          # One-sentence connection
      connection_strength: "direct" | "adjacent" | "thematic"
      timing_note: string             # How time-sensitive this outreach is
      recommended_framework: string   # Which email framework fits best
    }
  }
]

Framework Selection Based on Connection Strength

Connection Strength Recommended Framework Why
Direct Signal-Proof-Ask The news IS the hook — reference it directly, show proof, ask
Adjacent PAS Problem (implied by the news) → Agitate (what happens if they don't act) → Solve
Thematic AIDA Attention (news reference) → Interest (how it relates to them) → Desire (your product) → Action

Human Checkpoint

## Connection Angles

### Direct Connections (X entities) — Act quickly
| Entity | News | Angle | Timing |
|--------|------|-------|--------|
| Acme Corp | Acquired BetaCo | "Need to unify CRM systems — [product] does this in 30 days" | This week (integration planning starts immediately) |

### Adjacent Connections (X entities)
| Entity | News | Angle | Timing |
|--------|------|-------|--------|
| MedTech Inc | Expanding to EMEA | "Localized [function] becomes a requirement — [product] supports 15 languages" | This month |

### Thematic Connections (X entities)
| Entity | News | Angle | Timing |
|--------|------|-------|--------|
| HealthCo | Industry report on [trend] | "They're likely evaluating [category] solutions" | Flexible |

Approve these angles before we find contacts?

Step 4: Find Relevant People

Purpose: For each qualified entity with a connection angle, find the right people to contact.

Input Contract

connection_angles: [...]              # From Step 3 output
buyer_titles: string[]                # From config
champion_titles: string[]             # From config
user_titles: string[]                 # From config
max_contacts_per_company: integer     # Default: 3-5

Process

  1. If the entity is already a person (Mode C or person mentioned in news):

    • They're the primary contact. Still find 1-2 additional contacts at their company (buyer if they're a champion, champion if they're a buyer) for multi-threading.

  2. If the entity is a company:

    • Use configured contact_tool to find people matching buyer_titles, champion_titles, user_titles
    • Prioritize people whose role is closest to the news event:
    News Category Prioritize These Contacts
    Regulation / Compliance Legal, Compliance, Operations leadership
    Acquisition / Merger COO, CTO, VP Operations, Integration leads
    Market expansion VP Sales, VP Marketing, Country/Regional leads
    Cost cutting / Layoffs COO, CFO, VP Operations
    Product launch CTO, VP Product, VP Engineering
    Crisis / Incident CISO, VP Engineering, CTO (for security), CEO/COO (for operational)
    General growth Default to buyer_titles from config

  3. For each contact, note their relevance to the news:

    • Are they directly affected by the news? (Their department, their function)
    • Are they the decision-maker for the response to this news?
    • Are they the person who will feel the pain this news creates?

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"
      news_relevance: string         # Why THIS person for THIS news
    }
    company: {
      name: string
      domain: string
    }
    connection: {
      specific_angle: string
      connection_strength: string
      urgency_reason: string
    }
    news: {
      headline: string
      event_category: string
      source_url: string | null
    }
  }
]

Human Checkpoint

## Contacts Found

### Acme Corp — "Acquired BetaCo" (Direct connection)
| Name | Title | Role | Why This Person |
|------|-------|------|----------------|
| Sarah Kim | COO | Buyer | Owns post-acquisition integration |
| David Park | VP Operations | Champion | Will manage unified workflows |
| Amy Chen | Director of Sales Ops | User | Directly affected by CRM unification |

### MedTech Inc — "Expanding to EMEA" (Adjacent connection)
| ... |

Total: X contacts across Y companies

Approve before we draft emails?

Step 5: Draft Personalized Outreach

Purpose: Draft outreach where the news event is the hook, your product is the solution, and the email demonstrates you understand their specific situation. Pure LLM reasoning — inherently tool-agnostic.

Input Contract

contacts: [...]                       # From Step 4 output
your_company: {
  description: string
  pain_point: string
  product_outcomes: string[]
  proof_points: string[]
}
sequence_config: {
  touches: integer                    # Default: 3
  timing: integer[]                   # Default varies by connection strength (see below)
  tone: string                       # Default: "casual-direct"
  cta: string                       # Default: "15-min call"
}

Process

  1. Adjust sequence timing by connection strength:

    Strength Timing Rationale
    Direct Day 1 / 3 / 7 Urgency is real — they're actively dealing with this
    Adjacent Day 1 / 5 / 12 Standard timing — urgency is implied, not immediate
    Thematic Day 1 / 7 / 14 Slower cadence — this is awareness, not crisis response

  2. Build the email around the news, not the product:

    The news is the subject. Your product is the punchline. Never lead with the product.

    Element Source How to Use
    News hook Step 1 news.headline Open with what happened — show you're informed
    Impact on them Step 3 connection.urgency_reason Explain what this means for their specific role
    Your angle Step 3 connection.specific_angle Connect the dots to your product naturally
    Proof Config proof_points Show a peer who faced a similar situation
    CTA Config Low-friction ask

  3. Email structure by connection strength:

    Direct connection (Signal-Proof-Ask):

    Hook: Reference the specific news event
Impact: What this means for them (1 sentence)
Proof: A peer who faced the same situation and used your product
Ask: Soft CTA

    Adjacent connection (PAS):

    Problem: The downstream challenge the news creates
Agitate: What happens if they don't address it (1 sentence)
Solve: How your product helps, with a proof point
Ask: Soft CTA

    Thematic connection (AIDA):

    Attention: Reference the news/trend
Interest: How it relates to their company specifically
Desire: What your product does in this context
Action: Soft CTA

  4. Personalization layers:

    Layer What Gets Personalized Source
    News reference The specific event and its relevance Step 1 news data
    Company context What their company does, their industry, their situation Step 2 ICP research
    Role context Why THIS person cares about this news Step 4 news_relevance
    Your company fit How your product specifically helps in this scenario Step 3 connection angle

  5. Follow email-drafting skill hard rules. Additionally:

    • Never sensationalize negative news. If the signal is a layoff, breach, or crisis, be empathetic, not opportunistic. "I know this is a challenging time" not "Your layoffs mean you need our tool!"
    • Don't pretend you just happened to see the news. Be direct: "Saw the news about [event]" not "I came across an interesting article."
    • If the news is about a crisis, wait 48-72 hours before reaching out. Immediate outreach during a crisis looks predatory.

Output Contract

email_sequences: [
  {
    contact: { full_name, email, title, company_name, role_type, news_relevance }
    news_context: { headline, event_category, source_url }
    connection: { specific_angle, connection_strength }
    sequence: [
      {
        touch_number: integer
        send_day: integer
        subject: string
        body: string
        framework: string
        personalization_elements: {
          news_reference: string       # How the news was referenced
          company_context: string      # How their company situation was used
          role_context: string         # How their specific role was leveraged
          product_connection: string   # How the product was positioned
        }
        word_count: integer
      }
    ]
  }
]

Human Checkpoint

Present samples grouped by connection strength:

## Sample Outreach for Review

### Direct Connection: Sarah Kim, COO @ Acme Corp
News: Acme acquired BetaCo | Angle: CRM unification | Framework: Signal-Proof-Ask

**Touch 1 — Day 1**
Subject: Unifying Acme + BetaCo systems
> Hi Sarah — saw the BetaCo acquisition. Congrats. The integration
> sprint typically surfaces a CRM unification challenge fast —
> two systems, overlapping data, different workflows.
>
> [Peer company] faced the same thing after their acquisition last year.
> [Product] had both systems unified in 30 days. Happy to share how.
>
> Worth a 15-minute call?

**Touch 2 — Day 3**
> [New angle — data migration complexity, with a specific metric]

**Touch 3 — Day 7**
> [Breakup with offer to share the integration playbook]

---

### Adjacent Connection: Dr. Lee, VP Product @ MedTech Inc
News: EMEA expansion | Angle: Localization needs | Framework: PAS

**Touch 1 — Day 1**
Subject: EMEA expansion + [function] localization
> [full email]

---

Approve these samples? I'll generate the rest in the same style.

Step 6: Handoff to Outreach

Identical to the other signal composites. Package contacts + email sequences for the configured outreach 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: News-triggered
News event: [headline]
Connection strengths: X direct, Y adjacent, Z thematic
Contacts: N people across M companies
Sequence: 3 touches (timing varies by connection strength)

Ready to launch?

Execution Summary

Step Tool Dependency Human Checkpoint Typical Time
0. Config None First run only 5 min (once)
1. Parse & Extract Web fetch (for URLs) or none (for text) Review extracted entities 2-3 min
2. Qualify ICP Web search (for company research) Approve qualified list 2-3 min
3. Connection Angle None (LLM reasoning) Approve angles + strength ratings 3-5 min
4. Find People Configurable (Apollo, LinkedIn, etc.) Approve contact list 2-3 min
5. Draft Emails None (LLM reasoning) Review samples, iterate 5-10 min
6. Handoff Configurable (Smartlead, CSV, etc.) Final launch approval 1 min

Total human review time: ~15-25 minutes

Key Difference from Other Signal Composites

Dimension Structured Signals (Funding, Hiring, etc.) News Signal
Signal type Predefined, narrow Arbitrary, broad — anything can be a trigger
Detection Targeted search (job boards, funding databases) Open-ended (any news source)
Extra step — Step 3: Connection Angle identification. Other composites have obvious connections (funding = money to spend). News requires explicit reasoning about WHY this event matters for your product.
Input modes Companies in → signals out Three modes: News→Companies, Companies→News, People→News
Timing Predictable windows (post-raise, pre-hire) Varies wildly by event type — crisis = 48hr delay, trend = flexible
Sensitivity Generally positive (funding, hiring, growth) Can be negative (layoffs, crises, failures). Requires empathy calibration.

Sensitivity Guidelines

Some news events require careful tone calibration:

Event Type Tone What NOT to Do
Layoffs Empathetic. "I know this is a tough time." Don't say "your layoffs mean you need us!"
Data breach / Security incident Helpful, not salesy. "If you need help with [specific thing]." Don't pile on or blame. Don't reach out same-day.
Earnings miss / Revenue decline Efficiency-focused. "Do more with what you have." Don't reference the miss directly in the subject line.
Executive departure / Fired CEO Skip the drama entirely. Focus on the new leader or the company's direction. Don't mention the departure unless it's public and amicable.
Lawsuit / Legal trouble Generally avoid unless your product directly helps with compliance/legal. Don't reference the lawsuit. It looks ambulance-chasey.
Product failure / Recall Only reach out if you have a direct solution. Don't gloat or compare.

Rule of thumb: If you wouldn't bring it up in a face-to-face conversation at a conference, don't put it in a cold email.

Tips

  • Direct connections are rare but powerful. Most news creates adjacent or thematic connections. When you find a direct one, prioritize it — these convert at 2-3x the rate.
  • Speed matters for direct connections. The first vendor to reference a relevant news event looks informed. The fifth looks like they're running the same playbook.
  • Don't force weak connections. If you can't articulate the angle in one sentence, the connection is too weak. Drop it.
  • News about competitors is gold. If a competitor raises funding, gets acquired, has a security breach, or launches a product — their customers and prospects are suddenly open to conversations.
  • Negative news requires a 48-72 hour cooling period. Reaching out the day of a layoff or breach is predatory. Wait, then lead with empathy.
  • Industry reports and trend pieces make great thematic triggers. "The Gartner report on [category] just dropped — here's what it means for [company]" positions you as thoughtful, not reactive.
  • Combine with other signal composites. News often contains embedded signals: an acquisition article mentions the acquiring company is hiring 50 people (hiring signal), a new CEO is named (leadership change signal), or the company just raised funding (funding signal). Route these to the appropriate specialist composite for better outreach.
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