OpenSpace is a skill-management layer for AI agents that stores, retrieves, evaluates, shares, and improves reusable workflows. It is intended for people using multiple coding agents who want skills to be reused and refined based on task outcomes. The catalogue provides 200 skills for use with OpenSpace and the agents it supports.
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 HKUDS/OpenSpace --skill ffmpeg-graceful-degradationgit clone --depth 1 https://github.com/HKUDS/OpenSpaceWrote 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/hkuds/openspace/ffmpeg-graceful-degradation)<a href="https://agentmods.dev/skills/hkuds/openspace/ffmpeg-graceful-degradation"><img src="https://agentmods.dev/badge/skills/hkuds/openspace/ffmpeg-graceful-degradation/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/hkuds/openspace/ffmpeg-graceful-degradation"><img src="https://agentmods.dev/badge/skills/hkuds/openspace/ffmpeg-graceful-degradation.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 | $0.00021 | $0.01552 |
| Opus 5 | $0.00010 | $0.00776 |
| Sonnet 5 | $0.00004 | $0.00310 |
| Haiku 4.5 | $0.00002 | $0.00155 |
Grade A, and why
ffmpeg-graceful-degradation scanned grade A with 1 finding 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.
Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
result = subprocess.run( How it starts
The opening of the file, as written. The whole thing — 199 lines — stays where its author put it; the contents beside it link to each section on GitHub.
FFmpeg Graceful Degradation
When processing videos with ffmpeg, encoding failures are common due to codec availability, library version mismatches, or system configuration issues. This skill provides a systematic fallback strategy to ensure video processing completes successfully.
Overview
The pattern involves: (1) probing encoder availability upfront, (2) testing on a short clip before batch processing, (3) progressive fallback through copy mode, alternative codecs, and finally moviepy, (4) using moviepy as a reliable bundled alternative.
Step 1: Probe Encoder Availability
Before any encoding work, check what encoders are available:
ffmpeg -encoders | grep -E "libx264|libopenh264|mpeg4"
Expected output shows which encoders are present:
libx264- Preferred H.264 encoder (may be missing)libopenh264- Alternative H.264 (often has library issues)mpeg4- Universal fallback (always available)
Step 2: Test Encoding on Single Short Clip
Never start batch processing without validation. Extract and test a short segment:
# Extract 5-second test clip
ffmpeg -y -i input.mp4 -ss 0 -t 5 -c copy test_clip.mp4
# Attempt encode with preferred codec
ffmpeg -y -i test_clip.mp4 -c:v libx264 -preset fast test_output.mp4
Check the exit code and output for errors. Common failures:
libopenh264.so: wrong ELF classEncoder libx264 not found- Library version mismatches
Step 3: Progressive Fallback Strategy
If the preferred encoder fails, try these fallbacks in order:
Fallback A: Copy Mode (No Re-encoding)
ffmpeg -y -i input.mp4 -c:v copy -c:a copy output.mp4
Fast, lossless, but doesn't change codec/format.
Fallback B: MPEG4 Codec
ffmpeg -y -i input.mp4 -c:v mpeg4 -q:v 3 -c:a copy output.mp4
Universal compatibility, larger file sizes, always available.
Fallback C: Install MoviePy (Bundles Working FFmpeg)
pip install moviepy
Then use Python instead of raw ffmpeg:
from moviepy.editor import VideoFileClip, concatenate_videoclips
# Single clip processing
clip = VideoFileClip("input.mp4")
clip.write_videofile("output.mp4", codec="libx264")
# Concatenate multiple clips
clips = [VideoFileClip(f) for f in clip_files]
final = concatenate_videoclips(clips)
final.write_videofile("output.mp4", codec="libx264")
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.
- 9d ago First seen · 199 lines · 21 tokens per session scan A 164e7bb45804
ffmpeg-graceful-degradation is a skill published in the GitHub repository HKUDS/OpenSpace (7,544 stars, last pushed 27d ago), licensed MIT. It adds 21 tokens to every session and 1,552 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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