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 content-repurposinggit 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/content-repurposing)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/content-repurposing"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/content-repurposing/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/content-repurposing"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/content-repurposing.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 | $0.00756 |
| Opus 5 | $0.00033 | $0.00378 |
| Sonnet 5 | $0.00013 | $0.00151 |
| Haiku 4.5 | $0.00007 | $0.00076 |
Grade A, and why
content-repurposing 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 — 50 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Content Repurposing
Convert source material into original, voice-matched content for the channels the user actually needs.
Inputs
- Source URLs, transcripts, posts, research reports, or owned source material.
- Target audience, brand or company voice, platforms, formats, objective, and desired quantity.
- Optional Brand Core, voice guide, campaign, offer, CTA, and publishing window.
Workflow
- Confirm which sources may be transformed and whether they are owned, quoted, or used only as inspiration.
- For a video source, use
transcript-intelligenceto obtain or analyze its transcript. Do not download, transcode, or edit the video, and do not introduce an FFmpeg dependency. If the user already supplied a transcript or captions, work from them directly. If only a URL is available and the current environment cannot call the transcript provider, ask for the transcript or captions; do not improvise a terminal-only media workflow. - Use
outlier-post-finderorcreator-profile-teardownwhen the user wants to repurpose a winning pattern rather than a single source. - Extract content atoms: hooks, stories, claims, proof, frameworks, questions, examples, objections, visuals, and calls to action. Retain the source and support for every factual atom.
- Choose only formats that fit the audience and objective. Define the platform constraint, distinct angle, and intended action for each output.
- Rewrite from the user's perspective and voice. Use
create-linkedin-contentandcreate-x-contentfor final platform tuning when those formats are requested. - Produce ready-to-edit drafts, not a list of generic ideas. Vary the structure by platform and include attribution notes where a source materially influenced the output.
- Self-check for voice, factual support, repetition, plagiarism risk, platform fit, and whether each derivative stands on its own.
Runtime paths
- Transcript or text supplied: fully terminal-free. Repurpose the supplied material directly.
- Public video URL with GooseWorks MCP: use
transcript-intelligence, then call the managed provider through the livecall_data_providerschema. No terminal or separate provider key is required. - Public video URL with GooseWorks CLI: use
transcript-intelligenceand its declared provider dependency, then continue with the transcript. - Public video URL without either provider path: explain that the transcript cannot be fetched in this environment and request pasted captions or a transcript.
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 · 50 lines · 66 tokens per session scan A 9c516607ce58
content-repurposing 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 756 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.
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