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 video-production-harnessgit 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/video-production-harness)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/video-production-harness"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/video-production-harness/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/video-production-harness"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/video-production-harness.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00048 | $0.01301 |
| Opus 5.5 | $0.00019 | $0.00520 |
| Sonnet 5.5 | $0.00010 | $0.00260 |
| Haiku 4.5 | $0.00005 | $0.00130 |
Grade A, and why
video-production-harness 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 today.
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 — 44 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Non-negotiables (every state, every host)
- Copy the reference. Study the whole reference, picture and sound. Keep its people, action, camera, hook, length (±25%) and its shot and setup counts (±30%, from
scripts/reference_shots.py); list every change inchanges_from_reference, shown first at the script approval. - Match its sound and pace. Speech, music and effects follow the reference. Silence only when the reference is silent or the owner asks for it. Never a sine tone, a drone or an empty track.
scripts/reference_rate.pymeasures the reference's words per second (never estimate it); a voice more than 10% slower is regenerated or sped up (at most 1.25×) before lip-sync and picture timing. - People and demos stay people and demos. Plan a person, hands or a live demonstration in the reference as one; never plan a text card, dots or a still in its place. One that ends up on screen anyway is an
askfinding you recommend fixing. - The script comes from write-video-ad-script. Fetch it with catalog_fetch; never write the script inline.
- Use the real product. Its real photos, its real size and its usage steps. A still packshot gets the real photo through
overlay. A pack held or used on screen is made withopenai/gpt-image-2/edit, the real product photo as reference image 1, front label facing the camera. A blank, unprinted or label-less brand pack is must_fix. Every use step and product state (dry contents, cooked dish, strip on the nose) comes from a real brand photo sent as a reference. - Look real. Phone capture, deep focus, no bokeh, a lived-in room, no retouching or beauty filter, clothes that fit the activity. Every photoreal still and every motion prompt, B-roll included, carries the one light and realism block in libraries, look and rubric: light from a window or lamp, normal exposure, no HDR, matte skin, no over-sharpening, the first frame's light kept.
- Review the final against the reference, listened or measured, never claimed.
scripts/qc_evidence.pymust exit 0 on the exact final bytes (it fails only a broken file); then mark the video complete and read the host's final check. - Two owner approvals, both in this chat: the script and the ingredients. In
review_mode: autoone yes to the full total covers both (see check severity). Agent checks add no other approval step. - Quality warns, never blocks. Every finding is
must_fix(always redone, never delivered),ask(shown with the price of the fix; inreview_mode: autoyou decide) orfine(kept). Only money, consent, safety and broken files stop the run. A redo is paid from the approved total when it fits, otherwise after a new yes to its price; never drop a scene. See check severity.
Reading rule. Read one file per call. If the output says it was truncated, re-read that file in parts before acting.
What ships with it
53 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.
- auto-fix-from-review-notes.md 6.5 KB
- auto-refine.md 5.3 KB
- brainstorm.md 3.2 KB
- capabilities.md 14 KB
- create-clips.md 15 KB
- create-design-brief.md 13 KB
- create-storyboard.md 8.8 KB
- edit-clip.md 6.4 KB
- edit-video.md 13 KB
- lock-character.md 11 KB
- lock-script.md 8.7 KB
- orchestrator.md 11 KB
- polish.md 9.2 KB
- preflight-audit.md 6.2 KB
- promote.md 3.2 KB
- references/check-severity.md 9.6 KB
- references/creative-contract-and-review.md 11 KB
- references/creator-grammar.md 6.7 KB
- references/editorial-review.md 6.3 KB
- references/genres/ai-creator-ugc.md 2.7 KB
- references/genres/chat-ui.md 1.5 KB
- references/genres/hypermotion.md 1.4 KB
- references/genres/motion-graphics.md 1.5 KB
- references/genres/narrator-montage.md 3.1 KB
- references/genres/product-demo.md 2.0 KB
- references/hook-compatibility.md 2.8 KB
- references/libraries-look-and-rubric.md 5.5 KB
- references/product-scale-and-world.md 4.2 KB
- references/quality-tier.md 8.4 KB
- references/recipe-format.md 6.5 KB
- references/review-script-shortform.md 12 KB
- references/shot-routing.md 15 KB
- references/specialist-handoff.md 3.9 KB
- references/speech-and-route-qualification.md 9.4 KB
- release.json 22 KB
- review-video.md 15 KB
- scripts/assemble_sources.py 5.7 KB runs code
- scripts/assemble.py 11 KB runs code
- scripts/assembly_timeline.py 11 KB runs code
- scripts/check_recipe.py 9.6 KB runs code
- scripts/final_grade.py 2.9 KB runs code
- scripts/final_measure_parts.py 15 KB runs code
- scripts/final_measure.py 14 KB runs code
- scripts/pack_recipe.py 11 KB runs code
- scripts/qc_evidence.py 15 KB runs code
- scripts/reference_rate.py 4.3 KB runs code
- scripts/reference_shots.py 13 KB runs code
- scripts/timeline_rules.py 11 KB runs code
- skill.meta.json 683 B
- states/edit-to-delivery.md 8.2 KB
- states/setup-and-script.md 10 KB
- states/storyboard-and-clips.md 7.8 KB
- wrap-session.md 5.9 KB
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.
- today Changed · +1 lines e4ba4bd5d201
- yesterday Changed · +9 lines 3e659d9dc200
- 12d ago Changed · +14 lines e12896636e68
- 15d ago First seen · 20 lines · 48 tokens per session scan A aba66dd45ba5
video-production-harness is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,254 stars, last pushed today), licensed MIT. It adds 48 tokens to every session and 1,301 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-10-03.
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