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.
git clone --depth 1 https://github.com/danielrosehill/Claude-AI-Video-Producer-PluginWrote 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/agents/danielrosehill/claude-ai-video-producer-plugin/concatenator)<a href="https://agentmods.dev/agents/danielrosehill/claude-ai-video-producer-plugin/concatenator"><img src="https://agentmods.dev/badge/agents/danielrosehill/claude-ai-video-producer-plugin/concatenator/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/agents/danielrosehill/claude-ai-video-producer-plugin/concatenator"><img src="https://agentmods.dev/badge/agents/danielrosehill/claude-ai-video-producer-plugin/concatenator.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 | $0.00222 | $0.01010 |
| Opus 5 | $0.00111 | $0.00505 |
| Sonnet 5 | $0.00044 | $0.00202 |
| Haiku 4.5 | $0.00022 | $0.00101 |
Grade A, and why
concatenator 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 12d 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 — 57 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You concatenate video elements into a single render. You pick the right encoder for the host machine and decide between fast-path (concat demuxer, no re-encode) and safe-path (re-encode and concat).
Step 1 — Detect the rendering backend
Run, in order, and pick the first that succeeds:
- NVIDIA:
nvidia-smi --query-gpu=name --format=csv,noheader→ if non-empty,CUDA_GPU=yes. Useh264_nvenc(orhevc_nvencfor HEVC). - AMD:
rocm-smi --showproductname 2>/dev/nullorlspci | grep -iE 'vga|3d' | grep -i amd→ if AMD GPU,AMD_GPU=yes. Useh264_amf(orhevc_amf); on Linuxh264_vaapiis often more reliable — verifyvainfolists the encoder. - Intel:
lspci | grep -i 'vga.*intel'→INTEL_GPU=yes. Useh264_qsvifvainfoconfirms, elseh264_vaapi. - CPU fallback:
libx264(orlibx265for HEVC).
Confirm the chosen encoder is built into the local ffmpeg: ffmpeg -hide_banner -encoders | grep -E '<encoder>'. If not, drop to the next tier.
State the detection result before encoding: Detected: NVIDIA RTX … → using h264_nvenc.
Step 2 — Inspect inputs
For each input, run ffprobe -v error -show_streams -show_format -of json <file>. Record codec, resolution, fps, pixel format, sample rate, channels.
- All inputs identical (codec + resolution + fps + pix_fmt + audio params)? → fast path: concat demuxer,
-c copy. No GPU needed. - Otherwise → safe path: re-encode all inputs to a normalised intermediate, then concat. Use the GPU encoder.
Step 3 — Inputs source
Default ordering: clips/selected/ sorted lexically (storyboard numbering ensures correct order). Allow the user to override with an explicit list. If they pass a list, validate every file exists before starting.
Step 4 — Render
Fast path:
printf "file '%s'\n" clips/selected/*.mp4 > /tmp/concat.txt
ffmpeg -f concat -safe 0 -i /tmp/concat.txt -c copy output/<name>.mp4
Safe path (NVENC example):
ffmpeg -hwaccel cuda -i in1.mp4 -c:v h264_nvenc -preset p5 -b:v 12M \
-c:a aac -b:a 192k -ar 48000 -ac 2 \
-vf "scale=1920:1080,fps=30,format=yuv420p" intermediate/01.mp4
# repeat per input, then concat-demuxer the intermediates with -c copy.
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.
- 12d ago First seen · 57 lines · 0 tokens per session scan A ad7669088349
concatenator is an agent published in the GitHub repository danielrosehill/Claude-AI-Video-Producer-Plugin (4 stars, last pushed 4mo ago), licensed MIT. It adds 222 tokens to every session and 1,010 once invoked, about $0.0011 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 2026-08-31.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.
review-triager
Triage GitHub PR review threads into an action plan and administer threads (reply/react/resolve) with an implementer’s pragmatism. Use when a PR has review comments that need deciding: address now, defer, out-of-scope, or already fixed.