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-win-back-sequencergit 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-win-back-sequencer)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/customer-win-back-sequencer"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/customer-win-back-sequencer/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-win-back-sequencer"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/customer-win-back-sequencer.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 | $0.00066 | $0.02801 |
| Opus 5 | $0.00033 | $0.01401 |
| Sonnet 5 | $0.00013 | $0.00560 |
| Haiku 4.5 | $0.00007 | $0.00280 |
Grade A, and why
customer-win-back-sequencer 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 — 354 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Customer Win-Back Sequencer
Not every churned customer is gone forever. This skill identifies which ones to pursue, finds the right re-engagement angle, and generates a personalized win-back sequence. Timing and relevance are everything — no generic "we miss you" emails.
Built for: Startups that have churned customers sitting in a spreadsheet with no plan to re-engage them. The best win-back campaigns are triggered by change — this skill monitors for those changes and strikes when the timing is right.
When to Use
- "Which churned customers should we try to win back?"
- "Build a win-back campaign for [customer]"
- "Research our churned accounts for re-engagement opportunities"
- "Generate win-back emails for customers who left because of [reason]"
- "Run the win-back scan on our churn list"
Phase 0: Intake
Churned Account Data
- Churn list — CSV/sheet with: company name, domain, contact email, contact LinkedIn URL (if available), churn date, MRR at churn, churn reason (if known)
- Time since churn filter — Min/max months since churn to consider (default: 3-18 months. Too recent = too soon. Too old = too stale.)
- Minimum value — Only pursue accounts above $X MRR? (Focus effort on worthwhile wins)
Product Context
- Major product updates since churn — What's new? (Features, pricing changes, integrations, performance improvements)
- Churn reasons addressed — Which past churn reasons have you actually fixed?
- Current offer — Any win-back incentive? (Discount, extended trial, concierge onboarding, free migration)
Sequence Preferences
- Sender — Who should the emails come from? (Founder, CSM, account exec)
- Channel — Email only, or email + LinkedIn?
- Sequence length — How many touches? (Default: 3-4 over 3-4 weeks)
Phase 1: Churned Account Research
For each account in the churn list, research what's changed:
1A: Company Changes
Search: "[company name]" funding OR raised OR "series" OR acquisition 2025 2026
Search: "[company name]" hiring OR "we're hiring" OR "growing team"
Search: "[company name]" launch OR "new product" OR expansion OR pivot
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 · 354 lines · 66 tokens per session scan A 503235e532e9
customer-win-back-sequencer is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,202 stars, last pushed 11d ago), licensed MIT. It adds 66 tokens to every session and 2,801 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.
Other skills, from other repositories
sequence-architect
Design a 4-6 touch outreach cadence with channels, timing, tonal arc, and branching. Use when starting a new sequence from scratch given an ICP and offer.
icebreaker
Draft 3 distinct opening messages for a specific prospect, anchored on a why-now and a why-this-person. Use when sending a high-leverage first touch where personalization is worth the time.
sequence-doctor
Audit an outreach sequence and produce a ranked fix list. Use when reply rates are low, when you want a second opinion before sending, or when rewriting a sequence from scratch.
jargon-bingo
Score sales calls, emails, or posts on a 5x5 bingo card of cliches. Mostly for fun; quietly useful for spotting jargon-heavy patterns.
prospect-twin
Build a believable persona simulation of a prospect from their LinkedIn profile, then practice outreach against them. Use when preparing for a high-stakes outreach or call, when iterating on openers, or when testing whether a pitch lands.
cringe-translator
Translate sales-jargon-laden LinkedIn messages into what the sender actually means. Mostly for fun; occasionally educational about why a message tanked.