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 extract-source-samplegit 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/extract-source-sample)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/extract-source-sample"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/extract-source-sample/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/extract-source-sample"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/extract-source-sample.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 | $0.00160 | $0.06539 |
| Opus 5 | $0.00080 | $0.03270 |
| Sonnet 5 | $0.00032 | $0.01308 |
| Haiku 4.5 | $0.00016 | $0.00654 |
Grade A, and why
extract-source-sample 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 12d 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 — 512 lines — stays where its author put it; the contents beside it link to each section on GitHub.
extract-source-sample
This is an agent-executed skill. There are no Python scripts. The agent reads the run folder, builds the JSON, and stamps catalog links by hand. The content-goose run folders aren't always cleanly structured (some have empty production/ JSON, some carry everything in working/) — an agent adapts, a script would brittle out.
When to use
- "Extract the source-sample.json for
<run>." - "Get the upload-sample JSON for this ad so I can remix it."
- "Prep
<run>for remix."
Do NOT use to:
- Rewrite the script for a new brand (that's a separate agent step that consumes this skill's output).
- Render the remix (that's the existing
remix-adskill). - Upload an ad to the library (that's
upload-ad-sample).
Inputs
| Input | Required | Notes |
|---|---|---|
run-dir |
yes | Absolute path to a content-goose ad-run folder (e.g. clients/ladder/ad-runs/run-02-podcast-skit). |
out |
no | Where to write the JSON. Default: <run-dir>/remix/source-sample.json. |
That's the entire interface.
What the agent must do
1. Read the run
Open each file if it exists; tolerate missing files (most production/*.json
in older runs are empty stubs — fall back to working/):
working/script.json— primary source of truth for scenes, voices, set.production/asset-manifest.json—assets[]with roleactive_masterpoints at the master mp4; per-assetprovider+metadata.modelproduce the atom-skill rows.HOW_TO_MAKE_THIS_VIDEO.md— gets dumped verbatim intohow_to.video-project.json— fallback for title / format when script.json doesn't carry them.finals/*.mp4— fallback for the master mp4 if asset-manifest is empty.working/characters/*.png— anchor portraits per character.working/*.py— driver scripts (render_vo.py,render_variants.py,render_clips.py,stitch.py,build_end_card.py, etc.). These are the source's runnable code; the remix consumer ports them. Capture inproduction_scripts[](step 2 below).
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.
- 12d ago First seen · 512 lines · 160 tokens per session scan A 65d734bac920
extract-source-sample is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,202 stars, last pushed 11d ago), licensed MIT. It adds 160 tokens to every session and 6,539 once invoked, about $0.0008 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 2026-08-30.
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