Agent skill

Funding Signal Monitor

by gooseworks-ai in gooseworks-ai/goose-skills

Monitor web sources for Series A-C funding announcements. An agent skill from gooseworks-ai/goose-skills.

MITAuto-check passedProductivity & Automation

Install Funding Signal Monitor

skills CLI
$ npx skills add gooseworks-ai/goose-skills --skill funding-signal-monitor -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install gooseworks-ai/goose-skills funding-signal-monitor --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/monitoring/composites/funding-signal-monitor .claude/skills/funding-signal-monitor && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
funding-signal-monitor
GitHub stars
1.2k
Used in
1 other repo
Token cost
~2.3k tokens
SKILL.md length
949 words
Files
3 (incl. scripts)
Skills in repo
273
Repo updated
First seen
Licence
MIT

At a glance

Monitor web sources for Series A-C funding announcements. An agent skill from gooseworks-ai/goose-skills.

  • Works in 6 steps: Dependencies → Apify API Token (for Twitter/Reddit… → Configuration → …
  • Tasks that involve Web search
  • SKILL.md covers Why This Works, Cost, Setup and Usage, plus 5 more sections
  • Runs Python scripts from its folder; calls python3 and pip3; needs APIFY_API_TOKEN

What it does

Funding Signal Monitor is an agent skill from gooseworks-ai/goose-skills. Monitor web sources for Series A-C funding announcements. Aggregates signals from TechCrunch, Crunchbase (via web search), Twitter, Hacker News, and LinkedIn. Filters by stage, amount, and industry. Returns qualified recently-funded companies ready for outreach.

Its SKILL.md is about 2.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files, including scripts (for example `scripts/search_funding.py` and `skill.meta.json`).

It sits in Productivity & Automation, covering Web search and Web scraping. It works with X (Twitter), LinkedIn, Reddit and Apify. The repository describes itself as: Library of Growth & GTM skills + data APIs for Claude Code, Codex, Cursor to run ads, social, content, lead gen, seo and data scraping. The licence is MIT.

When your agent uses it

  • Tasks that involve Web search
  • Tasks that involve Web scraping

Example prompts

  • “/funding-signal-monitor”

Requirements

  • Python 3
  • A credential in APIFY_API_TOKEN

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Dependencies
  2. Apify API Token (for Twitter/Reddit scrapers)
  3. Configuration
  4. Multi-Source Search
  5. Consolidation & Qualification
  6. Output

What it can do on your machine

Read from SKILL.md and the folder at commit c650c6d. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python3
    • pip3

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • hn.algolia.com
    • console.apify.com
    • crunchbase.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • APIFY_API_TOKEN

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Funding Signal Monitor loads about 2.3k tokens when it runs. Until then it costs about 71 tokens; SKILL.md has 949 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~71
When it runs · the whole SKILL.md, loaded when a task matches
~2.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from gooseworks-ai/goose-skills at commit c650c6d, republished under its MIT licence (© gooseworks-ai). 949 words, ~2,268 tokens.

Download SKILL.mdSave it as .claude/skills/funding-signal-monitor/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
funding-signal-monitor
description
Monitor web sources for Series A-C funding announcements. Aggregates signals from TechCrunch, Crunchbase (via web search), Twitter, Hacker News, and LinkedIn. Filters by stage, amount, and industry. Returns qualified recently-funded companies ready for outreach.
version
1.0.0
tags
lead-generation

Funding Signal Monitor

Detect recently-funded startups as buying signals. When a company raises a round, they have fresh capital, aggressive growth plans, and urgent needs for tools and services. This skill finds those companies across multiple sources, qualifies them, and outputs a ranked list ready for outreach.

Why This Works

When a company announces funding, they've:

  • Received capital earmarked for growth (hiring, tooling, infrastructure)
  • Committed to investors on aggressive milestones
  • Entered a 12-18 month sprint to hit next-stage metrics
  • Begun evaluating vendors immediately (the "post-raise buying window" is 1-3 months)

Series A-C companies are the sweet spot: enough money to buy, small enough to move fast.

Cost

ComponentCost
Web Search (WebSearch tool)Free
Hacker News (Algolia API)Free
Twitter scraper (Apify)~$0.05-0.10 per run
Reddit scraper (Apify)~$0.05-0.10 per run

Typical run: $0.10-0.20 total. Web Search + HN are free and provide the bulk of results.

Setup

1. Dependencies
bash
pip3 install requests
2. Apify API Token (for Twitter/Reddit scrapers)
bash
export APIFY_API_TOKEN="apify_api_YOUR_TOKEN_HERE"

Not required if you only want Web Search + HN results.

Usage

Phase 1: Configuration

Accept parameters from the user:

ParameterRequiredDefaultDescription
target-stagesYes—Comma-separated: "Series A, Series B, Series C"
target-industriesNoallFilter: "SaaS, AI, fintech, healthtech"
min-amountNononeMinimum raise amount (e.g., "$5M")
lookback-daysNo7How far back to search
output-pathNostdoutWhere to save the markdown report

Run these searches in parallel to maximize coverage:

A) Web Search (WebSearch tool)

Run 4-6 queries using the WebSearch tool. Vary the phrasing to catch different announcement styles:

  • "Series A announced this week 2026"
  • "Series B funding round 2026"
  • "startup raised Series A"
  • "seed funding announcement startup"
  • "[industry] startup funding" (if industry filter specified)
  • "raised $" AND "Series" AND "2026"

For each result, extract:

  • Company name
  • Amount raised
  • Stage (Seed, A, B, C, etc.)
  • Date of announcement
  • Lead investors
B) Twitter Search (twitter-mention-tracker)
bash
python3 skills/twitter-mention-tracker/scripts/search_twitter.py \
  --query "\"excited to announce\" AND (\"raised\" OR \"Series A\" OR \"Series B\" OR \"funding\")" \
  --since <7-days-ago> --until <today> --max-tweets 50 --output json

Funding announcements often break on Twitter first. Founders post "excited to announce" or "thrilled to share" when rounds close.

C) Hacker News (funding-signal-monitor helper script)
bash
python3 skills/funding-signal-monitor/scripts/search_funding.py \
  --stages "Series A,Series B" --days 7 --min-points 5 --output json

Or use the hacker-news-scraper directly:

bash
python3 skills/hacker-news-scraper/scripts/search_hn.py \
  --query "raised funding Series" --days 7 --output json
D) Reddit Search (reddit-post-finder)
bash
python3 skills/reddit-post-finder/scripts/search_reddit.py \
  --subreddit "startups,SaaS,technology" \
  --keywords "raised,Series A,Series B,funding round" \
  --days 7 --sort hot --output json
Phase 3: Consolidation & Qualification

After collecting results from all sources:

  1. Deduplicate across sources. Same company appearing in multiple sources = higher confidence signal.

  2. For each company, assess:

    CriterionHow to Evaluate
    StageSeed, A, B, C, or later — must match target-stages
    Amount raisedParse from announcement — filter by min-amount if specified
    IndustryInfer from company description — filter if target-industries specified
    Cloud likelihoodTech/SaaS/AI companies = high; traditional industries = lower
    Team size estimateSeries A = 10-30, Series B = 30-100, Series C = 100-300
    RecencyMore recent = more urgent buying window
  3. Score each company:

    • +3 points: Appears in multiple sources
    • +2 points: Stage matches target exactly
    • +2 points: Industry matches target
    • +1 point: High cloud likelihood (tech/SaaS/AI)
    • +1 point: Announced within last 3 days
    • -1 point: Stage is outside target range
    • -2 points: Non-tech industry (unless specifically targeted)
  4. Rank by score descending.

Phase 4: Output

Produce a ranked report with the following columns:

ColumnDescription
RankScore-based ranking
CompanyCompany name
AmountAmount raised
StageFunding stage
DateAnnouncement date
InvestorsLead investors
IndustryCompany's industry/vertical
Source(s)Where the signal was found (web, Twitter, HN, Reddit)
Cloud LikelihoodHigh / Medium / Low
Outreach AngleSuggested approach based on stage and industry

Outreach angle templates:

  • "Scale fast with fresh capital" — Best for Series A. They're building the team and need tools to move fast before the money runs out.
  • "Operationalize before the next round" — Best for Series B. They need to professionalize processes before Series C diligence.
  • "Enterprise-ready at scale" — Best for Series C. They're going upmarket and need enterprise-grade tooling.

Save to the specified output path as markdown, or print to stdout.

Optionally export to Google Sheet using the google-sheets-write capability.

Show full SKILL.md (329 more words)Show less

Helper Script

A standalone Python script is included for searching Hacker News specifically for funding signals:

bash
# Search HN for Series A and B announcements in last 7 days
python3 skills/funding-signal-monitor/scripts/search_funding.py \
  --stages "Series A,Series B" --days 7 --output json

# Filter to high-engagement posts only
python3 skills/funding-signal-monitor/scripts/search_funding.py \
  --stages "Series A,Series B,Series C" --days 14 --min-points 10 --output text

# Search all stages with industry keyword
python3 skills/funding-signal-monitor/scripts/search_funding.py \
  --stages "Series A" --days 7 --keywords "AI,fintech" --output json

AI Agent Integration

When using this skill as an agent, the typical flow is:

  1. User specifies target stages, optional industry filter, optional min amount
  2. Agent runs multi-source search (Phase 2) in parallel
  3. Agent consolidates and scores results (Phase 3)
  4. Agent presents ranked list with outreach angles
  5. User selects companies to pursue
  6. Agent chains to company-contact-finder to find decision-makers
  7. Agent chains to cold-email-outreach to launch outreach

Example prompt:

"Find companies that raised Series A or B in the last week. Focus on SaaS and AI companies. We sell developer tools."

The agent should:

  • Run all source searches
  • Consolidate and score
  • Present the top 10-15 companies with reasoning
  • Suggest next steps (find contacts, launch outreach)

The agent should NOT:

  • Do any outreach without user confirmation
  • Skip the scoring/qualification step
  • Rely on a single source (multi-source coverage is the point)

Tips

  • Run weekly for best coverage. Funding announcements have a ~1 week news cycle.
  • Combine with company-contact-finder to get CTO/VP Eng contacts at funded companies.
  • Chain into cold-email-outreach for automated outreach with funding-specific angles.
  • Track hits in contact-cache to avoid duplicate outreach across weeks.
  • Web Search is your best source — it aggregates TechCrunch, Crunchbase, VentureBeat, etc. Twitter and HN provide supplementary signals and early detection.
  • Multi-source appearances are the strongest signal. A company that shows up on TechCrunch AND Hacker News AND Twitter is a higher-quality lead.

Troubleshooting

"No results found"
  • Broaden your stages (add Seed or Series C)
  • Extend lookback to 14 or 30 days
  • Remove industry filter
  • Check that scraper dependencies are installed
"Too many results"
  • Add an industry filter
  • Increase min-amount
  • Reduce lookback days
  • Focus on Series B+ (fewer but larger rounds)
"Twitter scraper failing"
  • Check APIFY_API_TOKEN is set
  • Fall back to Web Search + HN only (still effective)
  • Twitter is supplementary — the skill works without it

© gooseworks-ai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files (scripts) in skills/monitoring/composites/funding-signal-monitor of gooseworks-ai/goose-skills.

  • SKILL.md
  • scripts/search_funding.py
  • skill.meta.json

Open the folder on GitHubat commit c650c6d

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in gooseworks-ai/goose-skills, which our catalogue first saw on October 7, 2026.

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Questions about Funding Signal Monitor

What does Funding Signal Monitor do?

Monitor web sources for Series A-C funding announcements. An agent skill from gooseworks-ai/goose-skills. Funding Signal Monitor is an agent skill from gooseworks-ai/goose-skills. Monitor web sources for Series A-C funding announcements.

When should I use Funding Signal Monitor?

Funding Signal Monitor fits situations like: tasks that involve Web search; tasks that involve Web scraping.

How do I install Funding Signal Monitor in Claude Code?

Run `npx skills add gooseworks-ai/goose-skills --skill funding-signal-monitor -a claude-code`. Or copy the skill folder (skills/monitoring/composites/funding-signal-monitor in gooseworks-ai/goose-skills) into .claude/skills/funding-signal-monitor in your project. Claude Code loads it when a task matches its description.

How do I install Funding Signal Monitor in Codex?

Run `npx skills add gooseworks-ai/goose-skills --skill funding-signal-monitor -a codex`. Or copy the skill folder (skills/monitoring/composites/funding-signal-monitor in gooseworks-ai/goose-skills) into .agents/skills/funding-signal-monitor in your project. Codex loads it when a task matches its description.

Can I use Funding Signal Monitor in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add gooseworks-ai/goose-skills --skill funding-signal-monitor -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/funding-signal-monitor, .gemini/skills/funding-signal-monitor, .github/skills/funding-signal-monitor and .opencode/skills/funding-signal-monitor in your project.

What does Funding Signal Monitor need to run?

Going by SKILL.md and its folder, Funding Signal Monitor needs Python for the scripts in its folder, the command-line tools its instructions call (python3 and pip3) and credentials named APIFY_API_TOKEN. Our summary lists: Python 3; A credential in APIFY_API_TOKEN.

Does Funding Signal Monitor access the network?

SKILL.md names 3 domains. As links in the text: hn.algolia.com, console.apify.com and crunchbase.com. This is read from the text; nothing was executed.

Is Funding Signal Monitor safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Funding Signal Monitor use?

Funding Signal Monitor is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Funding Signal Monitor use?

About 2.3k tokens (SKILL.md is roughly 9.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Funding Signal Monitor?

Skills that share tags, products or a category with Funding Signal Monitor: Agent Reach (Panniantong/Agent-Reach, 95k stars), Deepapi (davidondrej/skills, 4.1k stars), Coffee Chat (LeoYeAI/openclaw-master-skills, 2.2k stars) and Bright Data MCP (brightdata/skills, 264 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Funding Signal Monitor?

gooseworks-ai (a GitHub organization) maintains it in gooseworks-ai/goose-skills, which has 1,240 GitHub stars. The repository holds 273 skills in this directory. The repository was last updated on October 8, 2026.

Source: gooseworks-ai/goose-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.