news-signal-outreach
End-to-end news-triggered signal composite. Takes any piece of news — an article, LinkedIn post, tweet, announcement, event, trend, regulation, product launch, acquisition, layoff, expansion, or any other public event — and evaluates whether the companies or people mentioned are ICP fits. If yes, identifies the connection between the news and your product, finds the right people to contact, and drafts personalized outreach using the news as the hook. Tool-agnostic. Accepts both company-level and person-level news triggers. article, tweet, blog post) or mentions a company/person they "came acro
npx skills add gooseworks-ai/goose-skills --skill news-signal-outreach --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
End-to-end signal composite that takes any public news item and evaluates whether mentioned companies or people are ICP fits. If they are, it identifies the connection between the news and the product, finds appropriate contacts, and drafts personalized outreach using the news as the hook. It is tool-agnostic and supports both company-level and person-level triggers. It auto-triggers when a user shares a URL or mentions a news item and asks about relevance, fit, ICP, or outreach.
How it works
Step 0: Configuration (One-Time Setup)
- Collect ICP-related preferences (company description, pain point, ICP targets, disqualifiers, buyer/champion/user titles), and your company context (outcomes, proof points, relevant news categories).
- Store configuration in a client-specific path.
Step 1: Parse & Extract
- Accept three input modes (news→targets, targets→news, people→news).
- Retrieve news content from a URL, raw text, or structured input; extract entities (companies/people), their roles, the core event, event category, date, and affected industries.
- If the news implies more entities than stated, infer broader affected companies as needed.
- Output a structured extracted_signals set including entity, news, and metadata.
Step 2: Qualify Against ICP
- For each extracted entity, check ICP criteria (industries, size, geographies, disqualifiers) for companies; for people, require relevant buyer/champion/user role alignment.
- If implied entities are surfaced, identify specific companies via web search and qualify them.
- Produce icp_qualified and icp_disqualified lists with reasoning.
Step 3: Identify Connection Angle
- For each ICP-qualified entity, determine the connection between the news event and your product.
- Answer: why this news creates urgency for the product, mapping event categories to urgency patterns (e.g., regulation change, acquisition, market expansion, product launch, competitive pressure, cost cutting, crisis, partnership, earnings/growth, industry trend).
- Use your company context (product_outcomes, proof_points, relevant_news_categories) to craft the relevance bridge.
When to use it
Auto-trigger on any external signal involving news or mentions where the user asks about relevance, fit, ICP, or outreach; no explicit "outreach" word needed.
What it can touch
Capabilities include web-search, contact-finding, and email-drafting. It connects to downstream skills like cold-email-outreach and linkedin-outreach when specified.
Caveats
License: MIT. Tools declared: claude-code, codex, cursor. Follow-up actions are driven by ICP qualification and connection angle; outcomes are not guaranteed and depend on input quality and ICP data.
# 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 ou
- When to Auto-Load
- Input Flexibility
- Step 0: Configuration (One-Time Setup)
- ICP Definition
- Your Company Context
- Signal Detection Config
- Contact Finding & Outreach Config
- Step 1: Parse & Extract
- Input Contract
- Process
- Output Contract
- Human Checkpoint
- Step 2: Qualify Against ICP
- Step 3: Identify Connection Angle
What does the news-signal-outreach skill do?
End-to-end news-triggered signal composite. Takes any piece of news — an article, LinkedIn post, tweet, announcement, event, trend, regulation, product launch, acquisition, layoff, expansion, or any other public event — and evaluates whether the companies or people mentioned are ICP fits. If yes, identifies the connection between the news and your product, finds the right people to contact, and drafts personalized outreach using the news as the hook. Tool-agnostic. Accepts both company-level and person-level news triggers. article, tweet, blog post) or mentions a company/person they "came acro
How do I install it?
Run `npx skills add gooseworks-ai/goose-skills --skill news-signal-outreach --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From gooseworks-ai/goose-skills, a repository with 1,091 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.