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 signal-scannergit 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/signal-scanner)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/signal-scanner"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/signal-scanner/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/signal-scanner"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/signal-scanner.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
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 →
- medium Excessive Agency · line 97 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00088 | $0.01431 |
| Opus 5 | $0.00044 | $0.00715 |
| Sonnet 5 | $0.00018 | $0.00286 |
| Haiku 4.5 | $0.00009 | $0.00143 |
Grade A, and why
signal-scanner 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 — 139 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Signal Scanner
Scheduled scanner that detects buying signals on TAM companies and watchlist personas, writes them to the signals table, and sets up downstream activation.
When to Use
- After TAM Builder has populated companies and personas
- As a recurring scan (daily/weekly) to detect timing-based outreach triggers
- When you need to move from static lists to intent-driven outreach
Prerequisites
SUPABASE_URL+SUPABASE_SERVICE_ROLE_KEYin.envAPIFY_TOKENin.env(for Phase 2 signals)ANTHROPIC_API_KEYin.env(optional, for LLM content analysis)- TAM companies populated via
tam-builder - Watchlist personas created for Tier 1-2 companies
Signal Types
| Priority | Signal | Level | Source | Cost |
|---|---|---|---|---|
| P0 | Headcount growth (>10% in 90d) | Company | Data diffs | Free |
| P0 | Tech stack changes | Company | Data diffs | Free |
| P0 | Funding round | Company | Data diffs | Free |
| P0 | Job posting for relevant roles | Company | Apify linkedin-job-search | ~$0.001/job |
| P1 | Leadership job change | Person | Apify linkedin-profile-scraper | ~$3/1k |
| P1 | LinkedIn content analysis | Person | Apify linkedin-profile-posts + LLM | ~$2/1k + LLM |
| P1 | LinkedIn profile updates | Person | Apify linkedin-profile-scraper | ~$3/1k |
| P2 | New C-suite hire | Company | Derived from person scans | Free |
Config Format
See configs/example.json for full schema. Key sections:
client_name— which client's TAM to scansignals.*— enable/disable each signal type with thresholdsscan_scope— filter by tier, status, lead_status
Database Write Policy
CRITICAL: Never write signals or update lead statuses without explicit user approval.
The signal scanner writes to multiple tables: signals (insert), enrichment_log (insert), companies (patch snapshots), and people (patch lead_status). These writes affect downstream outreach decisions — bad signals lead to bad outreach timing.
What ships with it
4 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 · 139 lines · 88 tokens per session scan A 6c62dfd13068
signal-scanner is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,202 stars, last pushed 11d ago), licensed MIT. It adds 88 tokens to every session and 1,431 once invoked, about $0.0004 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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