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 lead-discoverygit 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/lead-discovery)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/lead-discovery"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/lead-discovery/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/lead-discovery"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/lead-discovery.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 | $0.00041 | $0.01851 |
| Opus 5 | $0.00020 | $0.00925 |
| Sonnet 5 | $0.00008 | $0.00370 |
| Haiku 4.5 | $0.00004 | $0.00185 |
Grade A, and why
lead-discovery 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 8d 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 — 175 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Lead Discovery — Orchestrator
This is the entry point for all lead generation requests. Before any signal skill runs, this skill ensures the agent has enough business context to configure every downstream skill correctly.
When to Use
- User asks to "find leads", "generate leads", "do outbound", "find prospects", or any variation
- User mentions lead generation without specifying a particular signal source
- User asks to run a specific signal skill but the agent has no business context yet
- Always run this skill first — before github-repo-signals, job-signals, community-signals, competitor-signals, or event-signals
What This Skill Does
- Learns about the user's business (via website or questions)
- Identifies competitors, ICP, and relevant technologies
- Generates the shared context object that all signal skills need
- Recommends which signal sources to run and in what order
- Hands off to individual signal skills with inputs pre-filled
Phase 1: Gather Business Context
If the user provides a website URL
Scrape the website (homepage, pricing page, about page, docs if available) and extract:
- Product description — one-liner of what the product does
- Category — the market category (e.g., observability, API platform, CI/CD, CRM)
- Target buyer — who the product is sold to (developers, DevOps, marketers, etc.)
- Key features — the 3-5 main capabilities
- Technology keywords — the technical terms associated with this product and space
- Pricing model — free tier, usage-based, seat-based, enterprise (helps qualify leads)
- Competitors mentioned or implied — from comparison pages, "alternative to" language, integrations
After extracting, present a summary to the user and ask them to confirm or correct.
If the user does NOT have a website
Ask these questions one conversational block at a time. Do NOT dump all questions at once.
Block 1 — The Basics:
- What does your product do? (one sentence)
- Who is your ideal buyer? (role, company size, industry)
- What problem does it solve?
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.
- 8d ago First seen · 175 lines · 41 tokens per session scan A 08b6e6c9292d
lead-discovery is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,201 stars, last pushed 10d ago), licensed MIT. It adds 41 tokens to every session and 1,851 once invoked, about $0.0002 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.
Other skills, from other repositories
excalidraw-ai
Create professional Excalidraw diagrams by generating JSON directly. This skill provides the Excalidraw JSON schema reference and professional icon libraries for AI agents to autonomously create diagrams without templates.
linting
Python linting with Ruff - an extremely fast linter written in Rust. Use when: (1) Standardizing code quality, (2) Fixing style warnings, (3) Enforcing rules in CI, (4) Replacing flake8/isort/pyupgrade/autoflake, (5) Configuring lint rules and suppressions.
error-handling
Python error handling patterns for FastAPI, Pydantic, and asyncio. Follows "Let it crash" philosophy - raise exceptions, catch at boundaries. Covers HTTPException, global exception handlers, validation errors, background task failures. Use when: (1) Designing API error responses, (2) Handling RequestValidationError…
logfire
Structured observability with Pydantic Logfire and OpenTelemetry. Use when: (1) Adding traces/logs to Python APIs, (2) Instrumenting FastAPI, HTTPX, SQLAlchemy, or LLMs, (3) Setting up service metadata, (4) Configuring sampling or scrubbing sensitive data, (5) Testing observability code.
commit-message
Analyze git changes and generate conventional commit messages. Supports batch commits for multiple unrelated changes. Use when: (1) Creating git commits, (2) Reviewing staged changes, (3) Splitting large changesets into logical commits.
python-backend
Python backend development expertise for FastAPI, security patterns, database operations, Upstash integrations, and code quality. Use when: (1) Building REST APIs with FastAPI, (2) Implementing JWT/OAuth2 authentication, (3) Setting up SQLAlchemy/async databases, (4) Integrating Redis/Upstash caching, (5) Refactoring…