Agent Builder
shareAI-lab/learn-claude-code
Design and build AI agents for any domain. An agent skill from shareAI-lab/learn-claude-code.
Generate, benchmark, validate, and troubleshoot LongCat-Video-Avatar 1.5 videos with draw-things-cli.
$ npx skills add drawthingsai/draw-things-community --skill run-longcat-avatar-video -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install drawthingsai/draw-things-community run-longcat-avatar-video --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/drawthingsai/draw-things-community.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/run-longcat-avatar-video .claude/skills/run-longcat-avatar-video && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "run-longcat-avatar-video" agent skill from https://github.com/drawthingsai/draw-things-community/tree/main/.agents/skills/run-longcat-avatar-video into .claude/skills/run-longcat-avatar-video/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "run-longcat-avatar-video", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/drawthingsai/draw-things-community/tree/main/.agents/skills/run-longcat-avatar-videoType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add drawthingsai/draw-things-community --skill run-longcat-avatar-video -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install drawthingsai/draw-things-community run-longcat-avatar-video --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/drawthingsai/draw-things-community.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.agents/skills/run-longcat-avatar-video .agents/skills/run-longcat-avatar-video && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "run-longcat-avatar-video" agent skill from https://github.com/drawthingsai/draw-things-community/tree/main/.agents/skills/run-longcat-avatar-video into .agents/skills/run-longcat-avatar-video/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "run-longcat-avatar-video", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add drawthingsai/draw-things-community --skill run-longcat-avatar-video -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install drawthingsai/draw-things-community run-longcat-avatar-video --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/drawthingsai/draw-things-community.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.agents/skills/run-longcat-avatar-video .cursor/skills/run-longcat-avatar-video && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "run-longcat-avatar-video" agent skill from https://github.com/drawthingsai/draw-things-community/tree/main/.agents/skills/run-longcat-avatar-video into .cursor/skills/run-longcat-avatar-video/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "run-longcat-avatar-video", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/drawthingsai/draw-things-community.git --path .agents/skills/run-longcat-avatar-video--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add drawthingsai/draw-things-community --skill run-longcat-avatar-video -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install drawthingsai/draw-things-community run-longcat-avatar-video --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/drawthingsai/draw-things-community.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.agents/skills/run-longcat-avatar-video .gemini/skills/run-longcat-avatar-video && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "run-longcat-avatar-video" agent skill from https://github.com/drawthingsai/draw-things-community/tree/main/.agents/skills/run-longcat-avatar-video into .gemini/skills/run-longcat-avatar-video/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "run-longcat-avatar-video", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install drawthingsai/draw-things-community run-longcat-avatar-videoInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add drawthingsai/draw-things-community --skill run-longcat-avatar-video -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/drawthingsai/draw-things-community.git skills-src && mkdir -p .github/skills && cp -r skills-src/.agents/skills/run-longcat-avatar-video .github/skills/run-longcat-avatar-video && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "run-longcat-avatar-video" agent skill from https://github.com/drawthingsai/draw-things-community/tree/main/.agents/skills/run-longcat-avatar-video into .github/skills/run-longcat-avatar-video/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "run-longcat-avatar-video", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add drawthingsai/draw-things-community --skill run-longcat-avatar-video -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install drawthingsai/draw-things-community run-longcat-avatar-video --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/drawthingsai/draw-things-community.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.agents/skills/run-longcat-avatar-video .opencode/skills/run-longcat-avatar-video && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "run-longcat-avatar-video" agent skill from https://github.com/drawthingsai/draw-things-community/tree/main/.agents/skills/run-longcat-avatar-video into .opencode/skills/run-longcat-avatar-video/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "run-longcat-avatar-video", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
run-longcat-avatar-videoGenerate, benchmark, validate, and troubleshoot LongCat-Video-Avatar 1.5 videos with draw-things-cli.
Run Longcat Avatar Video is an agent skill from drawthingsai/draw-things-community. Generate, benchmark, validate, and troubleshoot LongCat-Video-Avatar 1.5 videos with draw-things-cli. Use when Codex needs to prepare LongCat q8p or i8x checkpoints, drive an avatar from a reference image and audio file, generate a fixed 4k+1 frame clip, run local segmented AVC for long audio with the canonical 93/13 configuration, compare i8x against q8p on Apple silicon, estimate full-video runtime, or verify LongCat MP4 frame count, duration, codec, and audio muxing.
Its SKILL.md is about 2.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files (for example `agents/openai.yaml`).
It sits in AI & LLM Engineering. The repository describes itself as: The community repository for the Draw Things app. The licence is GPL-3.0.
Read from SKILL.md and the folder at commit 4357b8d. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Shell commands in SKILL.md call:
bazelxcrunFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Run Longcat Avatar Video loads about 2.8k tokens when it runs. Until then it costs about 125 tokens; SKILL.md has 1,106 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from drawthingsai/draw-things-community at commit 4357b8d, republished under its GPL-3.0 licence (© drawthingsai). 1,106 words, ~2,839 tokens.
.claude/skills/run-longcat-avatar-video/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.Use the local draw-things-cli generate command for LongCat-Video-Avatar 1.5. Keep i8x and
q8p comparisons identical except for the model checkpoint and output path.
Work from the Draw Things repository root. Build once, then reuse the same optimized binary for all runs in one comparison:
bazel build --compilation_mode=opt //Apps:DrawThingsCLI
CLI=bazel-bin/Apps/DrawThingsCLI
"$CLI" generate --helpPrefer the app model directory on macOS unless the user provides another one:
MODELS_DIR="${DRAWTHINGS_MODELS_DIR:-$HOME/Library/Containers/com.liuliu.draw-things/Data/Documents/Models}"Do not switch builds, power modes, resolutions, segment sizes, or preview settings during a benchmark.
Choose one of these DiT checkpoints:
longcat_video_avatar_1.5_dmd_i8x.ckpt: 8-bit S model; prefer it for throughput on supported
Apple silicon.longcat_video_avatar_1.5_dmd_q8p.ckpt: q8p baseline and fallback; use it for matched quality
and performance comparisons.Ensure the selected model and its registered dependencies:
"$CLI" models ensure \
--models-dir "$MODELS_DIR" \
--model longcat_video_avatar_1.5_dmd_i8x.ckptLongCat also needs these files in MODELS_DIR:
umt5_xxl_encoder_q8p.ckpt
wan_v2.1_video_vae_f16.ckpt
whisper_large_v3_f16.ckptThe current model dependency list covers UMT5 and the Wan VAE. Verify Whisper separately because
--audio-encoder-file defaults to whisper_large_v3_f16.ckpt, but it is not a registered LongCat
model dependency:
test -f "$MODELS_DIR/whisper_large_v3_f16.ckpt"Pass --audio-encoder-file NAME.ckpt only when using a different compatible Whisper checkpoint.
Keep model audio separate from ControlNet hints. The reusable input and model-specific encoders live
in Libraries/AudioConverter; LocalImageGenerator only consumes finished conditioning. LongCat
audio follows this internal path:
AudioInput -> LongCatAudioConditioningEncoder -> LongCatAudioFeatures
-> LongCatAudioConditioning -> AudioConditioning.longCatAudioInput owns decoded PCM and builds the waveform used for output audio muxing. Run the Whisper
encoder once to produce LongCatAudioFeatures; derive one LongCatAudioConditioning for a normal
generation or one per AVC segment. AudioConditioning is the model-dispatch boundary where a future
LTX audio-conditioning case can be added. Do not represent model audio as ControlHintType.audio or
route it through ControlModel.
Use both a reference image and driving audio. The image is aspect-scaled and center-cropped to the requested output size. Width and height must be multiples of 64.
Confirm that AVFoundation can decode the audio before starting a long run:
afinfo "$AUDIO"Reject an input that reports zero packets or zero duration even if its file size is nonzero. Use a
valid CAF, MP3, M4A, or WAV container that afinfo and AVAudioFile can decode; do not fix a bad
container by renaming its extension.
The DMD checkpoint is step-distilled. Use these baseline values unless the task explicitly changes them:
steps = 8
cfg = 1
shift = 7
fps = 25LongCat temporal counts must be 4k + 1. Canonical values are 93 generated frames and 13 AVC
condition frames. Values such as 77 and 109 are valid experiments, but do not mix them into a
canonical 93/13 comparison.
Use AVC for audio longer than one generated clip. AVC is local-only and currently supports only
LongCat-Video-Avatar 1.5. Its output duration follows the audio; do not pass --frames with
--avc.
Use 93/13 explicitly even though they are the current defaults:
IMAGE=/path/to/reference.png
AUDIO=/path/to/driving-audio.caf
OUTPUT=/path/to/longcat_avc_i8x.mp4
PROMPT='A person speaks naturally to the camera with stable posture and synchronized mouth motion.'
"$CLI" generate --avc \
--models-dir "$MODELS_DIR" \
--model longcat_video_avatar_1.5_dmd_i8x.ckpt \
--image "$IMAGE" \
--audio "$AUDIO" \
--prompt "$PROMPT" \
--steps 8 --cfg 1 \
--segment-frames 93 --cond-frames 13 \
--width 448 --height 320 \
--seed 42 \
--config-json '{"shift":7}' \
--no-download-missing \
--disable-preview \
--video-format h264 \
--output "$OUTPUT"With 93/13, each later segment contributes 80 new frames. For an audio target of T frames:
stride = 93 - 13 = 80
segments = T <= 93 ? 1 : ceil((T - 93) / 80) + 1
sampling steps = segments * 8The CLI computes Whisper features for the generated span, reuses the last 13 decoded frames as the next segment's clean condition, drops overlap frames, trims to the audio target, and muxes the input speech into the output container.
For the full approximately 82-second reference workload, keep 93/13 and change only the audio, resolution, and output:
"$CLI" generate --avc \
--models-dir "$MODELS_DIR" \
--model longcat_video_avatar_1.5_dmd_i8x.ckpt \
--image "$IMAGE" --audio /path/to/man.mp3 \
--prompt "$PROMPT" \
--steps 8 --cfg 1 \
--segment-frames 93 --cond-frames 13 \
--width 832 --height 512 \
--seed 42 --config-json '{"shift":7}' \
--no-download-missing --disable-preview \
--video-format h264 \
--output /path/to/man_832x512_i8x.mp4Omit --avc, --segment-frames, and --cond-frames. Set --frames 93 explicitly:
"$CLI" generate \
--models-dir "$MODELS_DIR" \
--model longcat_video_avatar_1.5_dmd_i8x.ckpt \
--image "$IMAGE" \
--audio "$AUDIO" \
--prompt "$PROMPT" \
--steps 8 --cfg 1 \
--frames 93 \
--width 448 --height 320 \
--seed 42 \
--config-json '{"shift":7}' \
--no-download-missing \
--disable-preview \
--video-format h264 \
--output /path/to/longcat_93f_i8x.mp4At 25 fps, 93 frames produce 3.72 seconds. The exported audio is trimmed or padded to the same duration.
Run comparisons serially on the same machine. Keep all of these identical:
Change only:
--model longcat_video_avatar_1.5_dmd_i8x.ckpt
--model longcat_video_avatar_1.5_dmd_q8p.ckptWrap each command with /usr/bin/time -p. Record both the CLI's generation/sampling summary and
the external real time:
/usr/bin/time -p "$CLI" generate ...Do not call model-loading, sampling-step, generation, and wall-clock speedups interchangeable. Report each metric by name.
The following results were measured on 2026-07-13 on an Apple M5 Max with 48 GB memory. Both models
used 448x320, 8 steps, CFG 1, shift 7, seed 42, H.264, disabled preview, and the same image, prompt,
and valid 7.988-second audio. Use these as reference data, not a universal performance guarantee.
| Workload | Model | Sampling steps | Generation | Median / step | Wall time |
|---|---|---|---|---|---|
| AVC 93/13, 200 output frames | i8x | 24 | 368.45 s | 10.69 s | 421.77 s |
| AVC 93/13, 200 output frames | q8p | 24 | 497.04 s | 14.92 s | 550.07 s |
| No AVC, 93 output frames | i8x | 8 | 86.59 s | 7.98 s | 134.70 s |
| No AVC, 93 output frames | q8p | 8 | 136.40 s | 13.23 s | 184.26 s |
Observed speedups from that matched run:
An earlier 832x512, approximately 82-second, AVC 93/13 workload was roughly 9 hours with q8p and
3 hours or more with i8x. Treat that as a historical high-resolution observation, not a canonical
3x claim, because it was not captured with the same benchmark ledger as the table above. Re-run
both checkpoints with matched commands before publishing a 3x result.
Check existence, size, codecs, dimensions, and duration:
ls -lh "$OUTPUT"
mdls \
-name kMDItemCodecs \
-name kMDItemDurationSeconds \
-name kMDItemPixelWidth \
-name kMDItemPixelHeight \
"$OUTPUT"On macOS, count actual video samples with AVFoundation when ffprobe is unavailable:
VIDEO="$OUTPUT" xcrun swift -e '
import AVFoundation
import Foundation
let asset = AVURLAsset(url: URL(fileURLWithPath: ProcessInfo.processInfo.environment["VIDEO"]!))
let track = asset.tracks(withMediaType: .video).first!
let reader = try AVAssetReader(asset: asset)
let output = AVAssetReaderTrackOutput(track: track, outputSettings: nil)
reader.add(output)
reader.startReading()
var samples = 0
while let buffer = output.copyNextSampleBuffer() {
samples += CMSampleBufferGetNumSamples(buffer)
}
print("video_samples=\(samples) status=\(reader.status.rawValue)")
'Expect 93 samples for --frames 93. For AVC, expect the audio-derived target; a valid 7.988-second
input at 25 fps produces 200 samples and an 8.00-second MP4.
Inspect visual continuity around each AVC boundary. With 93/13, the first output boundary is near frame 93, and later boundaries advance by 80 frames. Compare i8x and q8p boundary frames before making a quality claim.
--frames must be 4k + 1: use 93 for the canonical single-shot run.--segment-frames or --cond-frames validation fails: use 93/13; both values must be 4k + 1,
and segment frames must exceed condition frames.--frames cannot be used with --avc: remove --frames; AVC duration follows audio.--avc currently supports only local generation: remove --remote and --cloud-compute.MODELS_DIR or pass
--audio-encoder-file with its filename.448x320, close competing GPU workloads,
then retry 832x512. Do not change segment size during an i8x/q8p comparison.--zero-audio-features is a hidden pipeline diagnostic. Never use it for a quality result.Keep the current stable LongCat behavior unless debugging model internals. In particular, do not change masked-reference attention or continuation policy merely to improve a benchmark number.
© drawthingsai, GPL-3.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 1 other file in .agents/skills/run-longcat-avatar-video of drawthingsai/draw-things-community.
Open the folder on GitHubat commit 4357b8d
Run Longcat Avatar Video next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Run Longcat Avatar Video this skilldrawthingsai/draw-things-community | 584 | — | ~2.8k | Automated safety check: Pass | GPL-3.0 | |
| Agent BuildershareAI-lab/learn-claude-code | 78k | 4 repos | ~1.2k | Automated safety check: Pass | MIT | |
| Add Uint Supportpytorch/pytorch | 104k | 2 repos | ~2.3k | Automated safety check: Pass | Custom licence | |
| LLM Benchmarking with lm-evaluation-harnessOrchestra-Research/AI-Research-SKILLs | 13k | 8 repos | ~3k | Automated safety check: Pass | MIT | |
| Segment Anything Model GuideOrchestra-Research/AI-Research-SKILLs | 13k | 8 repos | ~3.3k | Automated safety check: Pass | MIT | |
| 1passwordtrpc-group/trpc-agent-go | 1.9k | 14 repos | ~656 | Automated safety check: Pass | Apache-2.0 |
shareAI-lab/learn-claude-code
Design and build AI agents for any domain. An agent skill from shareAI-lab/learn-claude-code.
pytorch/pytorch
Add unsigned integer (uint) type support to PyTorch operators by updating ATDISPATCH macros.
Orchestra-Research/AI-Research-SKILLs
Runs lm-evaluation-harness to benchmark language models on academic suites such as MMLU, GSM8K and HumanEval, compare models and track training checkpoints.
Orchestra-Research/AI-Research-SKILLs
Guide to using Meta's Segment Anything Model for zero-shot image segmentation with point, box or mask prompts, or automatic mask generation.
trpc-group/trpc-agent-go
Set up and use 1Password CLI (op). An agent skill from trpc-group/trpc-agent-go.
jarrodwatts/claude-code-config
Transforms workflow to use Manus-style persistent markdown files for planning, progress tracking, and knowledge storage.
drawthingsai/draw-things-community
Set up and verify a new Draw Things CPU proxy and Envoy server using the scripts in Scripts/ServerManagement/CPUScript.
drawthingsai/draw-things-community
Use and troubleshoot an already installed Homebrew draw-things-cli for model discovery, authentication, local, cloud, or remote image generation, basic image-to-image and video generation, output…
drawthingsai/draw-things-community
Initialize a Draw Things GPU server with GPUScript, including script sync, Docker/CUDA/NVIDIA runtime setup, 7T data disk mounting, mergerfs, and end-to-end GPU verification.
drawthingsai/draw-things-community
Add a new image or video generative model to the Draw Things app / CLI with a compile-first, end-to-end workflow across SwiftDiffusion, tokenizer plumbing, text encoder, fixed encoder, UNet / DiT…
drawthingsai/draw-things-community
Validate Draw Things LoRA training end to end with draw-things-cli, including tiny-dataset training, loss and scaler checks, checkpoint sanity, and base-versus-LoRA generation comparison.
drawthingsai/draw-things-community
Add or tighten Draw Things LoRA trainer support for generative models available in the Draw Things app / CLI, covering LoRA builders, trainer dispatch, tokenizer and fixed-encoder wiring, checkpoint…
Categories
Generate, benchmark, validate, and troubleshoot LongCat-Video-Avatar 1.5 videos with draw-things-cli. Run Longcat Avatar Video is an agent skill from drawthingsai/draw-things-community.5 videos with draw-things-cli.
Run Longcat Avatar Video fits situations like: Codex needs to prepare LongCat q8p; I8x checkpoints; drive an avatar from a reference image and audio file; generate a fixed 4k+1 frame clip.
Run `npx skills add drawthingsai/draw-things-community --skill run-longcat-avatar-video -a claude-code`. Or copy the skill folder (.agents/skills/run-longcat-avatar-video in drawthingsai/draw-things-community) into .claude/skills/run-longcat-avatar-video in your project. Claude Code loads it when a task matches its description.
Run `npx skills add drawthingsai/draw-things-community --skill run-longcat-avatar-video -a codex`. Or copy the skill folder (.agents/skills/run-longcat-avatar-video in drawthingsai/draw-things-community) into .agents/skills/run-longcat-avatar-video in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add drawthingsai/draw-things-community --skill run-longcat-avatar-video -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/run-longcat-avatar-video, .gemini/skills/run-longcat-avatar-video, .github/skills/run-longcat-avatar-video and .opencode/skills/run-longcat-avatar-video in your project.
Going by SKILL.md and its folder, Run Longcat Avatar Video needs the command-line tools its instructions call (bazel and xcrun).
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Run Longcat Avatar Video is published under the GPL-3.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 2.8k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Run Longcat Avatar Video: Agent Builder (shareAI-lab/learn-claude-code, 78k stars), Add Uint Support (pytorch/pytorch, 104k stars), LLM Benchmarking with lm-evaluation-harness (Orchestra-Research/AI-Research-SKILLs, 13k stars) and Segment Anything Model Guide (Orchestra-Research/AI-Research-SKILLs, 13k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
drawthingsai (a GitHub organization) maintains it in drawthingsai/draw-things-community, which has 584 GitHub stars. The repository holds 8 skills in this directory. The repository was last updated on October 9, 2026.
Source: drawthingsai/draw-things-community on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.