Install the "vss-deploy" agent skill from https://github.com/open-edge-platform/edge-ai-libraries/tree/main/sample-applications/video-search-and-summarization/.github/skills/vss-deploy into .claude/skills/vss-deploy/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vss-deploy", 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.
Type 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.
skills CLI
$ npx skills add open-edge-platform/edge-ai-libraries --skill vss-deploy -a codex
Project install goes to .agents/skills/; add -g for ~/.codex/skills/.
Install the "vss-deploy" agent skill from https://github.com/open-edge-platform/edge-ai-libraries/tree/main/sample-applications/video-search-and-summarization/.github/skills/vss-deploy into .agents/skills/vss-deploy/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vss-deploy", 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.
skills CLI
$ npx skills add open-edge-platform/edge-ai-libraries --skill vss-deploy -a cursor
Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.
Install the "vss-deploy" agent skill from https://github.com/open-edge-platform/edge-ai-libraries/tree/main/sample-applications/video-search-and-summarization/.github/skills/vss-deploy into .cursor/skills/vss-deploy/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vss-deploy", 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.
--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
skills CLI
$ npx skills add open-edge-platform/edge-ai-libraries --skill vss-deploy -a gemini-cli
Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.
Install the "vss-deploy" agent skill from https://github.com/open-edge-platform/edge-ai-libraries/tree/main/sample-applications/video-search-and-summarization/.github/skills/vss-deploy into .gemini/skills/vss-deploy/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vss-deploy", 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.
Installs 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).
skills CLI
$ npx skills add open-edge-platform/edge-ai-libraries --skill vss-deploy -a github-copilot
Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.
Install the "vss-deploy" agent skill from https://github.com/open-edge-platform/edge-ai-libraries/tree/main/sample-applications/video-search-and-summarization/.github/skills/vss-deploy into .github/skills/vss-deploy/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vss-deploy", 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.
skills CLI
$ npx skills add open-edge-platform/edge-ai-libraries --skill vss-deploy -a opencode
OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
Install the "vss-deploy" agent skill from https://github.com/open-edge-platform/edge-ai-libraries/tree/main/sample-applications/video-search-and-summarization/.github/skills/vss-deploy into .opencode/skills/vss-deploy/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vss-deploy", 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.
Facts
Skill name
vss-deploy
GitHub stars
169
Token cost
~4.1k tokens
SKILL.md length
1,629 words
Files
17 (incl. scripts, references)
Skills in repo
29
Repo updated
First seen
Licence
Apache-2.0
At a glance
Deploys and manages VSS through setup.sh and its Docker Compose overlays.
Works in 3 steps: Walk up from the current directory… → Ask git for the enclosing repository… → Reuse a checkout a previous bootstrap…
Local lifecycle tasks such as configuration
SKILL.md covers Answer contract when the host…, Mandatory bootstrap and…, Environment setup (run first) and Mode routing, plus 8 more sections
Runs Shell scripts from its folder; calls docker, bash and git; needs MINIO_ROOT_PASSWORD and POSTGRES_PASSWORD
What it does
Vss Deploy is an agent skill from open-edge-platform/edge-ai-libraries. Deploys and manages VSS through setup.sh and its Docker Compose overlays. Use this skill for local lifecycle tasks such as configuration, startup, mode changes, inspection, shutdown, data cleanup, and health checks. It supports summary, search, dual, and unified modes with GPU and vLLM variants.
Its SKILL.md is about 4.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 21 other files, including scripts and reference files (for example `benchmark/benchmark.json`, `benchmark/benchmark.md` and `evals/evals.json`).
It sits in DevOps & Cloud, covering Containers and LLM inference and serving. It works with Docker and vLLM. The repository describes itself as: Libraries, microservices, tools, and other reference software, supporting development of performance-optimized Edge AI applications. The licence is Apache-2.0.
When your agent uses it
Local lifecycle tasks such as configuration
Tasks that involve Containers
Tasks that involve LLM inference and serving
Example prompts
“Use the vss-deploy skill to deploy and manages VSS through setup.sh and its Docker Compose overlays”
“/vss-deploy”
Requirements
A Bash shell
Docker
Workflow steps
3 steps, taken from the first numbered list in SKILL.md.
1Walk up from the current directory looking for a VSS app root - a
2Ask git for the enclosing repository (git rev-parse --show-toplevel) and
3Reuse a checkout a previous bootstrap already placed in
What it can do on your machine
Read from SKILL.md and the folder at commit 3084578. It shows what the files ask for, not the result of running them.
Tool permissions
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.
Runs code
Ships 2 files in scripts/ (Shell, from the files we listed), which the agent can run.
Shell commands in SKILL.md call:
docker
bash
git
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use docker and git, which can reach the network depending on how they are called.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names these keys or tokens, usually read from environment variables:
MINIO_ROOT_PASSWORD
POSTGRES_PASSWORD
RABBITMQ_PASSWORD
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Vss Deploy loads about 4.1k tokens when it runs, and up to ~9.7k if it reads all its reference files. Until then it costs about 77 tokens; SKILL.md has 1,629 words of instructions outside code blocks.
Always· name and description, kept in context so the agent knows when to use it
~77
When it runs· the whole SKILL.md, loaded when a task matches
~4.1k
With references· SKILL.md plus every file in references/, read only if the agent opens them
~9.7k
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.
Safety
Auto-check passed
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); the scripts in this folder are not scanned.
Download SKILL.mdSave it as .claude/skills/vss-deploy/SKILL.md (or your agent's skills folder). This skill also uses 16 other files; get the full folder from GitHub.
name
vss-deploy
description
Deploys and manages VSS through setup.sh and its Docker Compose overlays. Use this skill for local lifecycle tasks such as configuration, startup, mode changes, inspection, shutdown, data cleanup, and health checks. It supports summary, search, dual, and unified modes with GPU and vLLM variants.
Deploy, switch, inspect, and tear down VSS with setup.sh. Use this skill only
for sample-applications/video-search-and-summarization. Ground every answer in
the repository files - especially setup.sh and docker/compose.*.yaml - and do
not invent flags, services, ports, or variables. Run the commands yourself and
relay the output; do not hand the deploy command to the user (the lone
exception is --setenv, see below).
Answer contract when the host is not reachable
The user may be planning ahead, or Docker / the VSS source may be unavailable
here. In that case do not stall and do not invent output. Answer with the
exact command sequence instead: the bootstrap step, the config/secrets step, the
setup.sh invocation with its flags, the health wait, and the resulting URLs -
plus which command the user must run themselves and why. State plainly that the
commands were not executed. Never end the answer by asking whether to run them.
Mandatory bootstrap and credential contract
Every deployment answer must run or, after a host blocker, show and report this
exact setup shape:
Report the resolved APP_ROOT, whether it was reused without cloning, and the
bootstrap's no-hit fallback (shallow --depth 1, single-branch, sparse checkout
of only the VSS app from main). The canonical files are vss.config and the
external $VSS_CREDENTIALS_FILE; do not substitute stale filenames such as
vss.config.env, vss.secrets.env, or an in-checkout credentials file.
Before any real deploy, config render, stop, or model-download path, run a
bounded Docker host preflight such as docker info >/dev/null 2>&1. If it fails,
stop immediately: do not invoke setup.sh, do not wait for model download, and
do not retry a missing daemon. Report the observed host blocker and provide the
exact deferred sourced command sequence required by the answer contract.
Environment setup (run first)
This skill drives the Video Search & Summarization app through its real source
files, so the VSS application must be present and you must run commands from its
app root. Do this before anything else, and it works whether or not the VSS
source is already in your workspace.
Run the bundled bootstrap. It resolves the app root in this order and prints it
as the only line on stdout:
Walk up from the current directory looking for a VSS app root - a
directory carrying all three markers setup.sh, docker/, and
pipeline-manager/.
Ask git for the enclosing repository (git rev-parse --show-toplevel) and
check whether it holds sample-applications/video-search-and-summarization,
or is itself a VSS app root. This is what makes your own clone - or a fork -
work unchanged.
Reuse a checkout a previous bootstrap already placed in
${XDG_CACHE_HOME:-$HOME/.cache}/vss-src/edge-ai-libraries.
If any of those hit, that checkout is reused and NO clone is performed. Only
when all three miss does it clone - and then only a shallow (--depth 1),
single-branch, sparse checkout of just
sample-applications/video-search-and-summarization from main:
bash
# SKILL_DIR is THIS skill's own directory (shown to you when the skill loads);
# in-repo it is .github/skills/vss-deploy. Works the same if the skill is installed standalone.
SKILL_DIR=".github/skills/vss-deploy"
APP_ROOT="$(bash "$SKILL_DIR/scripts/vss-bootstrap.sh")"
cd "$APP_ROOT"
Every command below assumes the working directory is this APP_ROOT. To pull
from a fork/branch or reuse a specific checkout dir, override VSS_REPO_URL,
VSS_REPO_BRANCH, or VSS_CLONE_DIR before running it. The bootstrap refuses
to overwrite an existing non-VSS clone destination.
Mode routing
User says
Mode flag
UI URL
"summary" / "summarize videos" / "summary only"
--summary
http://<host-ip>:12345/
"search" / "search my videos" / "search only"
--search
http://<host-ip>:12345/
"both" / "dual" / "side by side" / "two UIs"
--summary --search (alias --dual)
…/summary/ and …/search/
"unified" / "one UI" / "search over summaries" / "all"
--summary-and-search (alias --unified, --all)
http://<host-ip>:12345/
If the user is ambiguous, ask which mode; do not default silently.
Quick deployment flow
Work from the app root:
bash
cd sample-applications/video-search-and-summarization
Provide config + credentials.setup.sh reads everything from the shell
environment and aborts on the first missing required var. The repository now
provides .env.example as a general application template, but this skill
keeps config and generated credentials split so credentials never enter a
committed file:
Credentials are generated at runtime outside the checkout, at
${XDG_CONFIG_HOME:-$HOME/.config}/vss/vss.credentials, by
scripts/gen-secrets.sh (strong random values,
created once and reused so data volumes stay valid).
Generate credentials once, then source both files in the same shell:
bash
export VSS_CREDENTIALS_FILE="${XDG_CONFIG_HOME:-$HOME/.config}/vss/vss.credentials"
./.github/skills/vss-deploy/scripts/gen-secrets.sh # creates it if absent
source .github/skills/vss-deploy/vss.config
source "$VSS_CREDENTIALS_FILE"
Common to every mode: MINIO_ROOT_USER, MINIO_ROOT_PASSWORD,
POSTGRES_USER, POSTGRES_PASSWORD, RABBITMQ_USER, RABBITMQ_PASSWORD.
Mode-specific model vars (VLM_MODEL_NAME, ENABLED_WHISPER_MODELS,
OD_MODEL_NAME for summary; MULTIMODAL_EMBEDDING_MODEL for search/dual;
TEXT_EMBEDDING_MODEL for unified) ship with defaults in vss.config -
see references/env-vars.md for the full table.
To inject your own credentials (vault/CI) instead of random ones, export them
before running gen-secrets.sh - it reuses any credential already set.
Dry-run first when unsure - append config to render the resolved
Compose configuration without starting containers, then review before the real deploy:
Deploy - run it yourself.setup.sh must be sourced (it uses return
and exports env while building the Compose command), but it does not need the
user's interactive shell: deploy uses docker compose up -d (detached), so
containers keep running after the subshell exits. First bring any prior stack
down so a stale/wrong-mode deployment can't collide, then deploy - run the whole
chain in one bash -c invocation:
bash
bash -c '
source setup.sh --stop # clear any running stack first
export VSS_CREDENTIALS_FILE="${XDG_CONFIG_HOME:-$HOME/.config}/vss/vss.credentials"
./.github/skills/vss-deploy/scripts/gen-secrets.sh # creates it if absent
source .github/skills/vss-deploy/vss.config
source "$VSS_CREDENTIALS_FILE"
source setup.sh --summary # the chosen mode
'
Run this in the background (run_in_background: true) or with a long
timeout. Before Compose starts, setup.sh launches a transient
vss-model-download container on loopback port 8640 when the selected OD
artifact or an OVMS VLM/split LLM artifact is missing. It submits REST jobs, waits up to
MODEL_DOWNLOAD_JOB_TIMEOUT per job (default 5400 seconds), writes failed
service logs to ov_models/model-download-*.log, removes the transient
container, and only then runs docker compose up -d.
Only exception:--setenv exists solely to leave env vars in the user's
interactive shell for later manual use - a subshell can't do that, so for
that verb only, give the user the !-prefixed command to run themselves:
bash
APP_HOST_PORT=18080 source setup.sh --setenv # port override optional
--setenv takes no mode flag. setup.sh rejects any two-argument form
other than <mode> config / config <mode>, so --setenv --summary-and-search
fails with "Invalid argument combination". It returns before the mode dispatch,
so it exports the mode-agnostic vars (credentials, APP_HOST_PORT, registry and
device settings) - not the mode-derived ones such as VS_INDEX_NAME or
EMBEDDING_MODEL_NAME. Export those by hand if the user needs full parity for
manual docker compose calls.
If the user asks for both a deployment and persistent variables for later
manual Compose commands, satisfy both parts separately: first run the real
deployment flow with the selected mode and overrides (for example,
APP_HOST_PORT=18080 source setup.sh --summary-and-search in the deployment
subshell), then give the user the interactive
APP_HOST_PORT=18080 source setup.sh --setenv command plus any required
mode-derived exports. --setenv prepares their future shell; it is not a
substitute for the requested deployment.
Wait for health, then print URLs. Keep the probe in the invoking
shell so this skill does not ship an executable network helper:
bash
VSS_BASE="http://${HOST_IP:-localhost}:${APP_HOST_PORT:-12345}"
deadline=$(( $(date +%s) + 300 ))
until curl -sf --max-time 5 "$VSS_BASE/manager/health" >/dev/null; do
if [ "$(date +%s)" -ge "$deadline" ]; then
echo "VSS health check timed out: $VSS_BASE/manager/health" >&2
docker compose ps
exit 1
fi
sleep 5
done
echo "UI: $VSS_BASE/"
echo "Pipeline Manager: $VSS_BASE/manager/docs"
Show full SKILL.md (547 more words)Show less
Mode aliases and config-only inspection
setup.sh normalizes --summary --search and --search --summary to --dual;
--summary-and-search, --search-and-summary, and --all to --unified;
config to --dual config; config --summary to --summary config; and
--down to --stop. Use config mode to verify the resolved Compose without
starting containers:
For vLLM, setup.sh adds docker/compose.vllm.yaml, starts vllm-cpu-service
(profile vllm) on host port 8200, and uses VLM_MODEL_NAME for both
captioning and final summary (use VLM_MODEL_NAME="Qwen/Qwen2.5-VL-3B-Instruct").
Experimental ENABLE_VLLM_GPU=true instead adds
docker/compose.vllm.xpu.yaml, selects profile vllm-xpu, and disables OVMS.
For OVMS GPU, setup.sh adds
docker/compose.gpu_ovms.yaml and switches ovms-service to
openvino/model_server:2026.1-gpu.
The skill's fresh bash -c deployment flow prevents derived OVMS storage names
from leaking between runs. If switching from OVMS to vLLM manually in the same
interactive shell, first run
unset VLM_STORAGE_MODEL_NAME LLM_STORAGE_MODEL_NAME; otherwise Compose can
reuse an OVMS storage alias that vLLM does not serve.
Lifecycle: bring down or reset
Run these yourself via bash -c 'source setup.sh …'. --stop, --down,
--clean-data, and config-only inspection (<mode> config) skip the required
environment validation entirely, so they are mode-agnostic and need no config
or credentials sourced first.
bash
source setup.sh --stop # stop/remove containers across all VSS overlays/profiles
source setup.sh --down # alias for --stop
source setup.sh --clean-data # also removes the VSS application data volumes
source setup.sh --help # full help
--clean-data removes the user-data volumes only: docker_minio_data,
docker_pg_data, docker_vdms-db, docker_milvus-db, docker_milvus-etcd,
docker_audio_analyzer_data, and docker_data-prep (volumes absent in the
current mode are skipped). The model-cache volumes -
docker_dataprep-yolox-models, docker_ov-models, docker_vllm_model_cache -
and the host-backed ov_models/ directory are deliberately preserved, so a
--clean-data never forces a costly model re-download.
Default ports & URLs
HOST_IP is auto-detected by setup.sh; APP_HOST_PORT defaults to 12345.
Surface
URL
UI (summary / search / unified)
http://<HOST_IP>:<APP_HOST_PORT>/
UI (dual mode)
…/summary/ and …/search/
Pipeline Manager API + docs
…/manager/docs, health …/manager/health
Data Prep docs (search modes)
http://<HOST_IP>:7890/docs
Embedding server docs (search modes)
http://<HOST_IP>:9777/docs
Troubleshooting ("why won't vss come up")
ERROR: <VAR> is not set → missing shell env var; re-source vss.config
plus $VSS_CREDENTIALS_FILE (step 2).
Invalid VECTORDB_BACKEND → set VECTORDB_BACKEND to vdms
or milvus.
Health never goes green → docker compose ps for crashed containers, then
docker compose logs <service>. The heavy ones are model servers (ovms,
vlm-ov/vllm, embedding).
Wrong/partial stack already running → source setup.sh --stop then redeploy.
Setup fails before Compose starts → inspect the reported
ov_models/model-download-*.log; if loopback port 8640 is occupied, set
MODEL_DOWNLOAD_HOST_PORT to a free port and rerun.
For anything past these basics - model-server crashes, OVMS token/cache/GPU
errors, host model-cache or model-download permission failures, search returning no results,
NPU/OpenGL issues - hand off to the installed vss-troubleshoot skill by name
and the canonical guide at docs/user-guide/troubleshooting.md.
Final answer audit trail
Tool arguments may not be visible to the user or evaluator. The final answer
must therefore report the bootstrap result: resolved APP_ROOT, the change to
that directory, and whether an existing checkout was reused without cloning.
Also state that a total bootstrap miss falls back to a shallow (--depth 1),
single-branch, sparse checkout of only the VSS app from main.
Name every requested setup.sh operation exactly, including overrides and mode
flags, and distinguish commands that completed from commands blocked by the
host. For a blocked deployment, still provide the exact sourced deploy command,
health endpoint, and resulting URL. For --clean-data, say that it performs the
stop/removal itself, list the affected user-data volumes, and explicitly state
that model-cache volumes and the host-backed ov_models/ directory are
preserved.
SKILL.md and 16 other files (scripts, references) in sample-applications/video-search-and-summarization/.github/skills/vss-deploy of open-edge-platform/edge-ai-libraries.
Vss Deploy 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.
Vss Deploy compared with similar skills
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Deploys and manages VSS through setup.sh and its Docker Compose overlays. Vss Deploy is an agent skill from open-edge-platform/edge-ai-libraries.sh and its Docker Compose overlays.
When should I use Vss Deploy?
Vss Deploy fits situations like: local lifecycle tasks such as configuration; tasks that involve Containers; tasks that involve LLM inference and serving.
How do I install Vss Deploy in Claude Code?
Run `npx skills add open-edge-platform/edge-ai-libraries --skill vss-deploy -a claude-code`. Or copy the skill folder (sample-applications/video-search-and-summarization/.github/skills/vss-deploy in open-edge-platform/edge-ai-libraries) into .claude/skills/vss-deploy in your project. Claude Code loads it when a task matches its description.
How do I install Vss Deploy in Codex?
Run `npx skills add open-edge-platform/edge-ai-libraries --skill vss-deploy -a codex`. Or copy the skill folder (sample-applications/video-search-and-summarization/.github/skills/vss-deploy in open-edge-platform/edge-ai-libraries) into .agents/skills/vss-deploy in your project. Codex loads it when a task matches its description.
Can I use Vss Deploy in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add open-edge-platform/edge-ai-libraries --skill vss-deploy -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/vss-deploy, .gemini/skills/vss-deploy, .github/skills/vss-deploy and .opencode/skills/vss-deploy in your project.
What does Vss Deploy need to run?
Going by SKILL.md and its folder, Vss Deploy needs a shell for the scripts in its folder, the command-line tools its instructions call (docker, bash and git) and credentials named MINIO_ROOT_PASSWORD, POSTGRES_PASSWORD and RABBITMQ_PASSWORD. Our summary lists: A Bash shell; Docker.
Does Vss Deploy access the network?
SKILL.md contains no URLs. Its commands use docker and git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Is Vss Deploy safe to install?
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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Vss Deploy use?
Vss Deploy is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Vss Deploy use?
About 4.1k tokens (SKILL.md is roughly 17k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 5.5k tokens, read only when the agent opens those files.
What are the alternatives to Vss Deploy?
Skills that share tags, products or a category with Vss Deploy: Hyperloom Setup (AMD-AGI/Hyperloom, 217 stars), Vllm Deploy Docker (vllm-project/vllm-skills, 103 stars), Serving LLMs On Epyc (amd/skills, 398 stars) and Upgrade Deps (areal-project/AReaL, 5.8k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Vss Deploy?
open-edge-platform (a GitHub organization) maintains it in open-edge-platform/edge-ai-libraries, which has 169 GitHub stars. The repository holds 29 skills in this directory. The repository was last updated on October 8, 2026.