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 agentmods add skills/hkuds/openspace/ffmpeg-encoder-checknpx skills add HKUDS/OpenSpace --skill ffmpeg-encoder-checkgit clone --depth 1 https://github.com/HKUDS/OpenSpaceWhat 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 | $0.00019 | $0.00884 |
| Opus 5 | $0.00010 | $0.00442 |
| Sonnet 5 | $0.00004 | $0.00177 |
| Haiku 4.5 | $0.00002 | $0.00088 |
Grade A, and why
ffmpeg-encoder-check 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 2d 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 — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.
FFmpeg Encoder Availability Check
Purpose
Before writing video encoding scripts, always verify which H.264 encoders are available in your FFmpeg installation. This prevents failures from library version mismatches, particularly with libopenh264.
Instructions
Step 1: Probe Available Encoders
Run the following command to check available H.264 encoders:
ffmpeg -encoders 2>/dev/null | grep h264
This shows which H.264 encoders are compiled into your FFmpeg build.
Step 2: Interpret Results
Common encoder options you may see:
| Encoder | Description | Recommendation |
|---|---|---|
libx264 |
Software H.264 encoder | Preferred - widely compatible |
libopenh264 |
OpenH264 software encoder | Use with caution - often has library version mismatches |
h264_nvenc |
NVIDIA hardware encoder | Good if NVIDIA GPU available |
h264_videotoolbox |
macOS hardware encoder | Good on macOS |
h264_vaapi |
Intel VAAPI hardware encoder | Good on Linux with Intel GPU |
h264_qsv |
Intel QuickSync encoder | Good on Windows/Linux with Intel GPU |
Step 3: Choose Encoding Strategy
For same-resolution sources (no re-encoding needed):
# Best option - pass-through without quality loss
ffmpeg -i input.mp4 -c:v copy -c:a copy output.mp4
If libx264 is available:
# Reliable software encoding
ffmpeg -i input.mp4 -c:v libx264 -preset medium -crf 23 -c:a aac output.mp4
If only libopenh264 is available:
# Use with caution - may have library issues
ffmpeg -i input.mp4 -c:v libopenh264 -c:a aac output.mp4
Step 4: Validate Before Batch Processing
Always test your encoding command on a small sample file before processing multiple videos or long footage.
Best Practices
- Default to
-c:v copywhen source and target resolutions match - no quality loss, fastest processing - Prefer libx264 over libopenh264 for software encoding - more stable, better compatibility
- Check encoder availability at script startup, not during execution - fail fast with clear error
- Cache encoder check results if running multiple encoding operations in the same session
- Provide fallback options in automated scripts - try copy first, then libx264, then fail gracefully
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.
- 2d ago First seen · 108 lines · 19 tokens per session scan A 9af2b87c75db
ffmpeg-encoder-check is a skill published in the GitHub repository HKUDS/OpenSpace (7,486 stars, last pushed 20d ago), licensed MIT. It adds 19 tokens to every session and 884 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 2026-08-30.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
agent-host-chat-contributions
Build and review cross-cutting agent-host chat behavior through lifecycle contributions. Use when adding turn lifecycle side effects, prompt or context injection, restored-history transformation, protocol-action observation, or when reviewing changes that add code to AgentSideEffects or AgentService.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.