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 mix-mastergit 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/mix-master)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/mix-master"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/mix-master/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/mix-master"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/mix-master.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 | $0.00090 | $0.01623 |
| Opus 5 | $0.00045 | $0.00812 |
| Sonnet 5 | $0.00018 | $0.00325 |
| Haiku 4.5 | $0.00009 | $0.00162 |
Grade A, and why
mix-master 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 — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
mix-master
Purpose
The v03 Ironman LEARNINGS describe 6+ tuning rounds because no default mix protocol existed: round 1 music too loud, round 2 VO too quiet, round 3 Whisper mis-transcribed, round 4 sidechain ratio finally right, round 5 music ended too early, round 6 climax sat at the same loudness as the rest.
This atom encodes the protocol that landed and ships it as the default. Operators tune by flag; they don't redesign the chain.
Inputs
--video <path>— video file with no audio (or whose audio gets dropped) (required)--vo <path,path,...>— comma-separated list of per-scene VO mp3s in scene order (required)--vo-starts <ms,ms,...>— start time in milliseconds for each VO clip on the timeline (required, same length as--vo)--music <path>— music bed file (required)--sfx <path,path,...>— optional comma-separated SFX list--sfx-starts <ms,ms,...>— optional SFX start times--output <path>— output mp4 (required)--total-duration <s>— target total duration in seconds (required — video AND music are sync-fit to this)--vo-boost-line <N>— 1-indexed VO clip that is the climax; gets +20% additional volume (default unset)--vo-volume <float>— per-clip VO volume multiplier (default3.0)--vo-mix-volume <float>— extra mix-bus volume on VO (default2.0)--music-volume <float>— base music volume (default0.13)--music-swell-volume <float>— peak music volume during apad/swell (default0.21)--sidechain-ratio <float>— sidechain ratio, capped at 20 by FFmpeg (default20)--sidechain-threshold <float>— sidechain threshold (default0.01)--target-i <lufs>— VO loudnorm integrated target (default-16)--target-tp <dbtp>— VO loudnorm true-peak (default-1.5)--target-lra <lu>— VO loudnorm LRA (default11)
Workflow
- Validate inputs —
--voand--vo-startsmust be same length;--sfxand--sfx-startsmust be same length; ffmpeg + ffprobe must be on PATH. - Probe music duration (
ffprobe). If music shorter than--total-duration, appendapad=pad_dur=2.0then trim back to target. If music longer, trim withafade=outover last 1.5s. - Build a
filter_complex_scriptfile (long chains exceed shell arg limits):- Per-VO:
[N:a]loudnorm=I=...:TP=...:LRA=...,adelay=<start>|<start>,volume=<vo_volume>[voN]; - Climax VO (if
--vo-boost-line N): volume becomesvo_volume * 1.2. - Mix all VO:
amix=inputs=K:duration=longest,volume=<vo_mix_volume>[vo_pre]; - Split VO for sidechain key:
[vo_pre]asplit=2[vo_final][vo_sc]; - Music base:
apad=pad_dur=2.0,atrim=0:<TOTAL>,afade=t=out:st=<TOTAL-1.5>:d=1.5,volume=<music_volume>[music_base]; - Sidechain:
[music_base][vo_sc]sidechaincompress=threshold=<thresh>:ratio=<ratio>:attack=20:release=600[music_ducked]; - SFX (optional): per-SFX
adelay+volumethen mix into[sfx_bus]. - Final:
amix=inputs=<2 or 3>:duration=longest[a_out].
- Per-VO:
- Run a single ffmpeg invocation that consumes video, all VO, music, all SFX, applies the filter chain, and outputs the mixed mp4 (video re-encoded only if duration trim needed; otherwise
-c:v copy). - Run a verification probe: ffprobe loudness summary on
[a_out], writemanifest.jsonandverification.md.
What ships with it
2 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.
- 9d ago First seen · 87 lines · 90 tokens per session scan A 1a20a6d8acb6
mix-master is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,202 stars, last pushed 11d ago), licensed MIT. It adds 90 tokens to every session and 1,623 once invoked, about $0.0005 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
excalidraw-ai
Create professional Excalidraw diagrams by generating JSON directly. This skill provides the Excalidraw JSON schema reference and professional icon libraries for AI agents to autonomously create diagrams without templates.
error-handling
Python error handling patterns for FastAPI, Pydantic, and asyncio. Follows "Let it crash" philosophy - raise exceptions, catch at boundaries. Covers HTTPException, global exception handlers, validation errors, background task failures. Use when: (1) Designing API error responses, (2) Handling RequestValidationError…
linting
Python linting with Ruff - an extremely fast linter written in Rust. Use when: (1) Standardizing code quality, (2) Fixing style warnings, (3) Enforcing rules in CI, (4) Replacing flake8/isort/pyupgrade/autoflake, (5) Configuring lint rules and suppressions.
logfire
Structured observability with Pydantic Logfire and OpenTelemetry. Use when: (1) Adding traces/logs to Python APIs, (2) Instrumenting FastAPI, HTTPX, SQLAlchemy, or LLMs, (3) Setting up service metadata, (4) Configuring sampling or scrubbing sensitive data, (5) Testing observability code.
commit-message
Analyze git changes and generate conventional commit messages. Supports batch commits for multiple unrelated changes. Use when: (1) Creating git commits, (2) Reviewing staged changes, (3) Splitting large changesets into logical commits.
python-backend
Python backend development expertise for FastAPI, security patterns, database operations, Upstash integrations, and code quality. Use when: (1) Building REST APIs with FastAPI, (2) Implementing JWT/OAuth2 authentication, (3) Setting up SQLAlchemy/async databases, (4) Integrating Redis/Upstash caching, (5) Refactoring…