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 battlecard-generatorgit 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/battlecard-generator)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/battlecard-generator"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/battlecard-generator/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/battlecard-generator"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/battlecard-generator.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 | $0.00074 | $0.02326 |
| Opus 5 | $0.00037 | $0.01163 |
| Sonnet 5 | $0.00015 | $0.00465 |
| Haiku 4.5 | $0.00007 | $0.00233 |
Grade A, and why
battlecard-generator 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 — 294 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Battlecard Generator
Research a competitor from every public angle — website, reviews, ads, social, pricing — and produce a structured sales battlecard. The output is what a rep opens 5 minutes before a competitive deal.
Built for: PMMs building competitive programs without a dedicated competitive intel team. The battlecard should be opinionated, not a neutral feature comparison.
When to Use
- "Build a battlecard against [competitor]"
- "We keep losing deals to [competitor] — help me understand why"
- "What are [competitor]'s weaknesses we can exploit?"
- "Prep the sales team for competitive deals against [competitor]"
- "Research [competitor] and give me competitive positioning"
Phase 0: Intake
- Your product name + URL
- Competitor name + URL — One competitor per battlecard (focused > broad)
- Deal context — Where do you compete? (same ICP, upmarket/downmarket, different use case?)
- Known win/loss signals — Any patterns from deals you've won or lost against them?
- Sales team size — Are reps technical or business-focused? (affects language level)
- Existing positioning — Your one-line positioning vs this competitor (if any)
Phase 1: Competitor Research
1A: Website & Messaging Analysis
Fetch: [competitor] homepage, pricing page, about page, product page
Search: "[competitor]" "we help" OR "the only" OR "unlike"
Search: "[competitor]" case study OR customer story
Extract:
- Hero claim — their primary positioning
- Category — what category do they place themselves in?
- Target audience — who do they say they serve?
- Key features emphasized — what do they lead with?
- Social proof — customer logos, metrics, quotes
- Pricing structure — plans, pricing model, enterprise vs self-serve
1B: Review Intelligence
Search: "[competitor]" site:g2.com OR site:capterra.com
Search: "[competitor]" reviews "switched from" OR "moved to"
From reviews, extract:
- Top 5 praised features (their moat — don't compete here directly)
- Top 5 complaints (your attack angles)
- Switching signals — why do customers leave?
- ICP patterns — what roles/company sizes review them?
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 · 294 lines · 74 tokens per session scan A 4714f1cef47f
battlecard-generator is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,202 stars, last pushed 11d ago), licensed MIT. It adds 74 tokens to every session and 2,326 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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