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 stitch-videos-ffmpeggit 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/stitch-videos-ffmpeg)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/stitch-videos-ffmpeg"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/stitch-videos-ffmpeg/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/stitch-videos-ffmpeg"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/stitch-videos-ffmpeg.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 | $0.00029 | $0.00465 |
| Opus 5 | $0.00015 | $0.00233 |
| Sonnet 5 | $0.00006 | $0.00093 |
| Haiku 4.5 | $0.00003 | $0.00047 |
Grade A, and why
stitch-videos-ffmpeg 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 — 53 lines — stays where its author put it; the contents beside it link to each section on GitHub.
stitch-videos-ffmpeg
Purpose
Stitch video segments with ffmpeg concat, xfade, overlay, audio mux, and export settings.
Implementation status: refactored from existing repository skills. The workflow consolidates behavior that previously lived across larger skills.
Sources: ad-studio, voiceover-product-ad, ugc-product-video, voiceless-music-transformation-reel, product-stopmotion-ad.
Extraction notes: assembly and composite scripts.
Inputs
- A clear user brief or source asset path.
- Brand, product, audience, platform, and approval constraints when relevant.
- Required credentials or provider access for any external service used by this skill.
- Output directory or test-run directory where artifacts should be saved.
Workflow
- Read the brief and confirm all required inputs are present.
- Load any referenced files in this skill folder only when they are needed.
- Run the provider, script, or planning workflow described by this skill.
- Save outputs under the requested output folder or
skills/test-runs/<timestamp>/<skill-name>/during tests. - Write or update a
manifest.jsonfor executable runs with status, provider, outputs, warnings, and errors.
Output
- Primary artifact or written plan requested by the skill.
manifest.jsonfor executable runs.verification.mdor a short verification summary that names the checks performed.- Any generated source assets, intermediate files, or final exports in the run folder.
Quality Checks
- Required files exist and paths in the manifest are valid.
- Output matches the requested format, platform, duration, dimensions, or text structure.
- Brand claims, captions, on-screen text, and CTAs follow the provided brand rules.
- Provider failures, skipped integrations, and human-review needs are explicit.
Failure Modes
- Missing credentials, provider access, or source files.
- Output does not match requested dimensions, duration, structure, or brand constraints.
- Generated media contains artifacts, unreadable text, unsafe claims, or caption collisions.
- Scaffolded skills cannot run production workflows until implementation details are added.
What ships with it
11 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.
- scripts/composite_final.py 3.5 KB runs code
- scripts/composite_final.sh 295 B runs code
- scripts/composite.py 4.8 KB runs code
- scripts/normalize_clip.sh 668 B runs code
- scripts/voiceless/composite.py 4.5 KB runs code
- skill.meta.json 272 B
- tests/expected-output.md 300 B
- tests/human-test.md 853 B
- tests/sample-input.md 568 B
- tests/smoke-test.md 589 B
- tests/verifier.md 284 B
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 · 53 lines · 29 tokens per session scan A 1874b053a9b8
stitch-videos-ffmpeg is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,202 stars, last pushed 11d ago), licensed MIT. It adds 29 tokens to every session and 465 once invoked, about $0.0001 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…