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 community-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/community-signals)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/community-signals"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/community-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/community-signals"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/community-signals.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Data Exfiltration · line 29 Code or instructions that leak agent conversation context to external services, potentially exposing sensitive user interactions.Fix: Remove any code that sends prompts, responses, or session data externally. Preserve user privacy; never exfiltrate conversation content.
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 | $0.00047 | $0.03302 |
| Opus 5 | $0.00023 | $0.01651 |
| Sonnet 5 | $0.00009 | $0.00660 |
| Haiku 4.5 | $0.00005 | $0.00330 |
Grade A, and why
community-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 — 332 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Community Signals
Extract high-intent leads from developer community forums by detecting buying signals in public discussions. Currently supports Hacker News and Reddit.
When to Use
- User wants to find leads from developer communities or forums
- User wants to identify people publicly expressing pain with competitors
- User wants to find people asking "what tool should I use for X"
- User mentions Hacker News, Reddit, Stack Overflow, or developer forums as lead sources
- User describes prospects who discuss tools, complain about solutions, or ask for recommendations in public forums
- User wants to find developers who built DIY/hacky solutions for problems the user's product solves
Prerequisites
- Python 3.9+ with
requestsand optionallypython-dotenv - Apify API token in
.env(for Reddit scraping) - No auth needed for Hacker News (free Algolia API)
- Working directory: the project root containing this skill
Phase 1: Collect Context
Step 1: Gather Product & ICP Information
Ask the user for the following. Do NOT proceed without this — the entire query generation depends on it.
"To find the right leads from developer communities, I need to understand:
- What does your product do? (one-liner)
- Who are your competitors? (list the main ones)
- What specific problems does your product solve? (the pain points)
- Who is your ideal buyer? (role, company type, tech stack)
- Any specific technologies or keywords associated with your space?"
If the user has already provided this context (e.g., from running the github-repo-signals skill), use that — don't ask again.
Phase 2: Generate Search Queries
Step 2: Generate Queries Across 9 Categories
Based on the user's product info, generate 3-5 search queries per category. These are the fixed categories — do not skip any:
Category 1: Alternative Seeking (intent score: 9) People actively looking to switch tools.
- Pattern: "[competitor] alternative", "alternative to [competitor]", "looking for [product type]"
- Example: "twilio alternative", "alternative to agora", "looking for video SDK"
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.
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 · 332 lines · 47 tokens per session scan A 2b03f89ffdfe
community-signals is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,202 stars, last pushed 11d ago), licensed MIT. It adds 47 tokens to every session and 3,302 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.
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…
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.
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…