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 customer-story-buildergit 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/customer-story-builder)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/customer-story-builder"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/customer-story-builder/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/customer-story-builder"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/customer-story-builder.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 | $0.00088 | $0.01724 |
| Opus 5 | $0.00044 | $0.00862 |
| Sonnet 5 | $0.00018 | $0.00345 |
| Haiku 4.5 | $0.00009 | $0.00172 |
Grade A, and why
customer-story-builder 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 — 242 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Customer Story Builder
Turn raw customer signal into a polished case study — plus every derivative format you need. One input (messy transcript or quote), all outputs (case study, one-pager, social snippet, deck slide).
Core principle: The best customer stories already exist in your support tickets, Slack channels, and call recordings. You just need to extract and structure them.
When to Use
- "Turn this customer interview into a case study"
- "We have a great Slack quote from [customer] — help me build a story around it"
- "Write a case study for [customer]"
- "I need social proof assets from [customer win]"
- "Package this customer result for the sales team"
Phase 0: Intake
Customer Context
- Customer name — Can we use their name publicly? (Named vs. anonymous)
- Company description — Industry, size, stage (1 sentence)
- Customer role/title — Who's the champion?
- How long have they been a customer?
Raw Input (provide any/all)
- Interview transcript — Full or partial transcript from a customer call
- Slack/email quotes — Specific messages where they praised the product
- Survey responses — NPS comments, CSAT feedback
- Support ticket excerpts — Before/after of a problem solved
- Review excerpts — G2, Capterra, Trustpilot quotes
- Metrics — Any numbers: time saved, revenue impact, efficiency gains, before/after
Story Angle
- Primary use case — What were they using the product for?
- Key transformation — What changed? (The "before → after" in one sentence)
- Output formats needed — Full case study, one-pager, social snippet, sales slide, or all?
Phase 1: Extract Story Elements
From the raw inputs, identify and extract:
The Problem (Before)
- What was the customer's situation before using the product?
- What specific pain were they experiencing?
- What had they tried before? (Manual process, competitor, nothing)
- How bad was it? (Quantify if possible — hours wasted, money lost, deals missed)
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 · 242 lines · 88 tokens per session scan A e65db0989a43
customer-story-builder 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 1,724 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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