funding-signal-outreach

funding-signal-outreach is a skill for Claude Code, Codex from gooseworks-ai/goose-skills. It costs 64 tokens per session (4,674 once invoked), scanned A, original, MIT.

A workflow that finds recent company funding events, checks whether the companies match your target customers, identifies relevant contacts, and drafts outreach.

In plain words
What is it for?
Use it to monitor funded companies, qualify them, find buyers or champions, and prepare personalized emails.
Why use it?
It turns funding announcements into a shortlist of companies that may now have budget and a reason to consider your product.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to monitor funded companies, qualify them, find buyers or champions, and prepare personalized emails.

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Install with agentmods
npx agentmods add skills/gooseworks-ai/goose-skills/funding-signal-outreach
About the project

Goose Skills is a library of workflows and data APIs that lets coding agents handle growth and go-to-market work such as advertising, social media, content, SEO, lead generation, and customer research. It is intended for teams using Claude Code, Cursor, Codex, and similar agents. The catalogue entries are its reusable skills.

gooseworks-ai/goose-skills · 1,202 stars · on GitHub · gooseworks.ai

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add gooseworks-ai/goose-skills --skill funding-signal-outreach
Clone the repo
git clone --depth 1 https://github.com/gooseworks-ai/goose-skills

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for funding-signal-outreach

README.md
[![agentmods](https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/funding-signal-outreach/github.svg)](https://agentmods.dev/skills/gooseworks-ai/goose-skills/funding-signal-outreach)
Your own site
<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/funding-signal-outreach"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/funding-signal-outreach/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for funding-signal-outreach

Your own site · 80×15
<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/funding-signal-outreach"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/funding-signal-outreach.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 64 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,674 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00064 $0.04674
Opus 5 $0.00032 $0.02337
Sonnet 5 $0.00013 $0.00935
Haiku 4.5 $0.00006 $0.00467

Measured 9d ago against content hash b187653c9b49, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

funding-signal-outreach scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/outreach/composites/funding-signal-outreach/SKILL.md · 548 lines

How it starts

The opening of the file, as written. The whole thing — 548 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Funding Signal Outreach

Detects recent funding events across a set of companies, qualifies them against your company's context, finds the right people to reach out to, and drafts personalized emails. The full chain from signal to outreach-ready.

When to Auto-Load

Load this composite when:

  • User says "check if any of these companies raised funding", "funding signal outreach", "reach out to recently funded companies"
  • User has a list of companies and wants to act on funding signals
  • An upstream workflow (TAM Pulse, company monitoring) triggers a funding signal check

Architecture

This composite is tool-agnostic. Each step defines a data contract (what goes in, what comes out). The specific tools that fulfill each step are configured once per client/user, not asked every run.

┌─────────────────────────────────────────────────────────────────┐
│                  FUNDING SIGNAL OUTREACH                        │
│                                                                 │
│  ┌──────────┐   ┌──────────┐   ┌──────────┐   ┌──────────┐    │
│  │  DETECT  │──▶│ QUALIFY  │──▶│  FIND    │──▶│  DRAFT   │    │
│  │ Funding  │   │ & Rank   │   │  People  │   │  Emails  │    │
│  └──────────┘   └──────────┘   └──────────┘   └──────────┘    │
│       │              │              │              │            │
│  Input: companies  + your company  + buyer       + signal      │
│  Tool: web search    context        personas      context      │
│    or apollo         (LLM)         Tool: apollo   (LLM)       │
│    or crunchbase                     or linkedin              │
│    or any                            or clearbit              │
│                                      or any                   │
└─────────────────────────────────────────────────────────────────┘

Step 0: Configuration (One-Time Setup)

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

Company Source Config

Question Options Stored As
Where does your company list come from? CSV file / Salesforce / HubSpot / Supabase / Manual list company_source
What fields identify a company? At minimum: company name + domain. Optional: industry, size, location company_fields

Read the full file on GitHub · 548 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 9d ago First seen · 548 lines · 64 tokens per session scan A b187653c9b49

Subscribe to this mod's changes

funding-signal-outreach is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,202 stars, last pushed 11d ago), licensed MIT. It adds 64 tokens to every session and 4,674 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.