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.
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.
npx skills add gooseworks-ai/goose-skills --skill github-repo-signalsgit clone --depth 1 https://github.com/gooseworks-ai/goose-skillsWrote 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.
[](https://agentmods.dev/skills/gooseworks-ai/goose-skills/github-repo-signals)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/github-repo-signals"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/github-repo-signals/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.
<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/github-repo-signals"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/github-repo-signals.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00050 | $0.02419 |
| Opus 5 | $0.00025 | $0.01210 |
| Sonnet 5 | $0.00010 | $0.00484 |
| Haiku 4.5 | $0.00005 | $0.00242 |
Grade A, and why
github-repo-signals 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.
How it starts
The opening of the file, as written. The whole thing — 222 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GitHub Repository Signals
Extract high-intent leads from one or more GitHub repositories by analyzing every type of user interaction. This skill uses only free GitHub API data — no enrichment credits are spent.
When to Use
- User wants to find leads from open-source GitHub repositories
- User wants to identify people who interact with competitor or category repos
- User wants cross-repo interaction analysis to find high-intent prospects
- User asks for GitHub-based lead generation without paid enrichment
- User says their ICP, target audience, or buyers are developers, engineers, or technical people who are active on GitHub
- User describes prospects who use open-source tools, contribute to open source, or build with specific technologies — and those technologies have public GitHub repos
- User wants to find leads in a technical space (e.g., "real-time communication", "AI agents", "infrastructure") where the community congregates around GitHub repositories
Note: If the user describes their ICP as GitHub-active but hasn't identified specific repositories yet, this skill still applies. In that case, ask the user which repositories their ICP is likely to interact with, or help them identify relevant repos based on the technology/space they describe.
Prerequisites
ghCLI authenticated (gh auth statusto verify)- Python 3.9+ with
PyYAMLinstalled - Working directory: the project root containing this skill
Inputs to Collect from User
Before running, ask the user for:
- Repositories (required): One or more GitHub repository URLs or
owner/repostrings - User limit (required): How many top users to include in the output. Explain that more users = longer runtime due to GitHub profile fetching (~5,000 profiles/hour). Suggest 500 as a good starting point for testing.
Execution Steps
Step 1: Verify Environment
gh auth status
Step 2: Run the Tool
python3 ${CLAUDE_SKILL_DIR}/scripts/gh_repo_signals.py \
--repos "owner1/repo1,owner2/repo2" \
--limit <USER_LIMIT> \
--output ${CLAUDE_SKILL_DIR}/../.tmp/repo_signals.csv
What ships with it
7 files 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.
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.
- 9d ago First seen · 222 lines · 50 tokens per session scan A 175f9b4e48ac
github-repo-signals is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,202 stars, last pushed 11d ago), licensed MIT. It adds 50 tokens to every session and 2,419 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.
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