Agentdock User Guide
uvwt/agentdock
当用户询问 AgentDock 是什么、如何使用、配置在哪里、不同平台或安装方式怎样修改配置并生效、如何重启或验证配置、如何发现并配置 Codex/Claude/Grok 等 Coding Agent 的 ACP,以及常见运行问题时使用;覆盖 macOS Desktop、Windows Desktop、Linux 服务、Docker 和直接运行二进制,不用于源码开发与贡献流程。
Diagnose a running or failing video-search-and-summarization deployment.
$ npx skills add open-edge-platform/edge-ai-libraries --skill vss-troubleshoot -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install open-edge-platform/edge-ai-libraries vss-troubleshoot --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/open-edge-platform/edge-ai-libraries.git skills-src && mkdir -p .claude/skills && cp -r skills-src/sample-applications/video-search-and-summarization/.github/skills/vss-troubleshoot .claude/skills/vss-troubleshoot && 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 "vss-troubleshoot" agent skill from https://github.com/open-edge-platform/edge-ai-libraries/tree/main/sample-applications/video-search-and-summarization/.github/skills/vss-troubleshoot into .claude/skills/vss-troubleshoot/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vss-troubleshoot", 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/open-edge-platform/edge-ai-libraries/tree/main/sample-applications/video-search-and-summarization/.github/skills/vss-troubleshootType 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 open-edge-platform/edge-ai-libraries --skill vss-troubleshoot -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install open-edge-platform/edge-ai-libraries vss-troubleshoot --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/open-edge-platform/edge-ai-libraries.git skills-src && mkdir -p .agents/skills && cp -r skills-src/sample-applications/video-search-and-summarization/.github/skills/vss-troubleshoot .agents/skills/vss-troubleshoot && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "vss-troubleshoot" agent skill from https://github.com/open-edge-platform/edge-ai-libraries/tree/main/sample-applications/video-search-and-summarization/.github/skills/vss-troubleshoot into .agents/skills/vss-troubleshoot/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vss-troubleshoot", 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 open-edge-platform/edge-ai-libraries --skill vss-troubleshoot -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install open-edge-platform/edge-ai-libraries vss-troubleshoot --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/open-edge-platform/edge-ai-libraries.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/sample-applications/video-search-and-summarization/.github/skills/vss-troubleshoot .cursor/skills/vss-troubleshoot && 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 "vss-troubleshoot" agent skill from https://github.com/open-edge-platform/edge-ai-libraries/tree/main/sample-applications/video-search-and-summarization/.github/skills/vss-troubleshoot into .cursor/skills/vss-troubleshoot/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vss-troubleshoot", 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/open-edge-platform/edge-ai-libraries.git --path sample-applications/video-search-and-summarization/.github/skills/vss-troubleshoot--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 open-edge-platform/edge-ai-libraries --skill vss-troubleshoot -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install open-edge-platform/edge-ai-libraries vss-troubleshoot --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/open-edge-platform/edge-ai-libraries.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/sample-applications/video-search-and-summarization/.github/skills/vss-troubleshoot .gemini/skills/vss-troubleshoot && 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 "vss-troubleshoot" agent skill from https://github.com/open-edge-platform/edge-ai-libraries/tree/main/sample-applications/video-search-and-summarization/.github/skills/vss-troubleshoot into .gemini/skills/vss-troubleshoot/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vss-troubleshoot", 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 open-edge-platform/edge-ai-libraries vss-troubleshootInstalls 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 open-edge-platform/edge-ai-libraries --skill vss-troubleshoot -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/open-edge-platform/edge-ai-libraries.git skills-src && mkdir -p .github/skills && cp -r skills-src/sample-applications/video-search-and-summarization/.github/skills/vss-troubleshoot .github/skills/vss-troubleshoot && 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 "vss-troubleshoot" agent skill from https://github.com/open-edge-platform/edge-ai-libraries/tree/main/sample-applications/video-search-and-summarization/.github/skills/vss-troubleshoot into .github/skills/vss-troubleshoot/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vss-troubleshoot", 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 open-edge-platform/edge-ai-libraries --skill vss-troubleshoot -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install open-edge-platform/edge-ai-libraries vss-troubleshoot --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/open-edge-platform/edge-ai-libraries.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/sample-applications/video-search-and-summarization/.github/skills/vss-troubleshoot .opencode/skills/vss-troubleshoot && 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 "vss-troubleshoot" agent skill from https://github.com/open-edge-platform/edge-ai-libraries/tree/main/sample-applications/video-search-and-summarization/.github/skills/vss-troubleshoot into .opencode/skills/vss-troubleshoot/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vss-troubleshoot", 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.
vss-troubleshootDiagnose a running or failing video-search-and-summarization deployment.
Vss Troubleshoot is an agent skill from open-edge-platform/edge-ai-libraries. Diagnose a running or failing video-search-and-summarization deployment. Probes Pipeline Manager health and feature/config endpoints to detect whether the backend is up and which mode is live, then runs structured cross-service triage grounded in setup.sh, Docker Compose files, health routes, and OVMS config. Use when users say "is vss up", "what mode is running", "check vss health", "debug vss", "VSS isn't working", "OVMS won't start", "no summary appears", "search returns nothing", containers are crash-looping…
Its SKILL.md is about 3.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 18 other files, including scripts and reference files (for example `BENCHMARK.md`, `evals/evals.json` and `evals/trigger-evals.json`).
It sits in DevOps & Cloud, covering Containers and Summarization. It works with Docker. 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.
8 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 0ed0479. 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.
Ships 2 files in scripts/ (Shell), which the agent can run.
Shell commands in SKILL.md call:
curljqdockerbashFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md. Its commands use curl and docker, which can reach the network depending on how they are called.
From URLs in SKILL.md, links to its own repository left out.
Names these keys or tokens, usually read from environment variables:
HUGGINGFACE_TOKENRABBITMQ_PASSWORDMINIO_ROOT_PASSWORDFrom names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Vss Troubleshoot loads about 3.3k tokens when it runs, and up to ~4.5k if it reads all its reference files. Until then it costs about 151 tokens; SKILL.md has 1,335 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); the scripts in this folder are not scanned.
The full file from open-edge-platform/edge-ai-libraries at commit 0ed0479, republished under its Apache-2.0 licence (© open-edge-platform). 1,335 words, ~3,345 tokens.
.claude/skills/vss-troubleshoot/SKILL.md (or your agent's skills folder). This skill also uses 14 other files; get the full folder from GitHub.Use this skill to diagnose a broken Video Search & Summarization deployment without guessing. The app is started with source setup.sh --summary, --search, --summary --search/--dual, or --summary-and-search/--unified; stopped with source setup.sh --down; user data reset with source setup.sh --clean-data.
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 first tries to find an existing VSS checkout -
walking up from the current directory and inspecting the enclosing git repo - and
reuses it without ever re-cloning. Only when no checkout is found does it do a
shallow, single-branch, sparse checkout of just
sample-applications/video-search-and-summarization from main. It prints the
resolved app root on stdout:
# SKILL_DIR is THIS skill's own directory (shown to you when the skill loads);
# in-repo it is .github/skills/vss-troubleshoot. Works the same if the skill is installed standalone.
SKILL_DIR=".github/skills/vss-troubleshoot"
APP_ROOT="$(bash "$SKILL_DIR/scripts/vss-bootstrap.sh")"
cd "$APP_ROOT"Every command below assumes the working directory is this APP_ROOT. To pull
from a fork/branch or reuse a specific checkout dir, override VSS_REPO_URL,
VSS_REPO_BRANCH, or VSS_CLONE_DIR before running it.
Run the read-only collector:
"$SKILL_DIR/scripts/triage.sh"It prints Docker Compose/container status, tails recent logs, curls key health endpoints, checks documented host ports, and reports GPU/NPU device visibility. This matters because most failures are dependency chains: pipeline-manager depends on storage/database/search/summary services, and UI symptoms often originate in OVMS, vLLM, EVAM, VDMS, MinIO, RabbitMQ, or Postgres.
If Docker Compose cannot resolve services, run from the app root and compare with setup's own generated config:
source setup.sh --summary config # or --search config / --summary-and-search configBefore diving into the decision tree, confirm whether the backend is even up and
which mode is live. Set HOST=http://${HOST_IP:-localhost}:${APP_HOST_PORT:-12345}
and run each command yourself, then relay the result. If nothing is deployed,
hand off to the vss-deploy skill at .github/skills/vss-deploy/SKILL.md.
# 1. Is the Pipeline Manager reachable?
curl -sf --max-time 5 "$HOST/manager/health" && echo " ← Pipeline Manager healthy" \
|| echo "UNREACHABLE - backend down or wrong HOST_IP/APP_HOST_PORT"
# 2. Which capabilities/mode are live, and the resolved config
curl -s "$HOST/manager/app/features" | jq . # search/summary flags
curl -s "$HOST/manager/app/config" | jq . # resolved system config
# 3. Subsystem probes
curl -s "$HOST/metrics-manager/health" # optional live metrics service
curl -s "$HOST/manager/audio/models" | jq . # whisper models (summary modes)
curl -s "$HOST/manager/pipeline/evam" | jq . # EVAM pipeline statusapp/features returns string flags, not booleans -
{"summary":"FEATURE_ON","search":"FEATURE_OFF"} - so test against the string
(e.g. jq -e '.search=="FEATURE_ON"'). Use it to decide which workflow applies:
vss-search-index needs search==FEATURE_ON; vss-summarize-video needs
summary==FEATURE_ON. A backend that 404s on /manager/health while the model
servers (ovms-service, vllm-cpu-service, embedding server) are still loading
is usually starting, not broken - wait and re-probe.
Check these exact services first: nginx, pipeline-manager, postgres-service, minio-service, ovms-service, vllm-cpu-service, vllm-xpu-service, video-ingestion, audio-analyzer, rabbitmq-service, video-search, vdms-vector-db, multimodal-dataprep, vector-retriever, multimodal-embedding-serving, and optional metrics-manager.
Why: Compose depends_on gates many services on health. For example, summary mode needs ovms-service or vllm-cpu-service, video-ingestion, rabbitmq-service, and audio-analyzer; search mode needs multimodal-dataprep, vector-retriever, and multimodal-embedding-serving healthy.
Actions:
triage.sh; later services often fail only because they waited for it.setup.sh: MinIO, Postgres, RabbitMQ credentials; VLM_MODEL_NAME, ENABLED_WHISPER_MODELS, OD_MODEL_NAME for summary; MULTIMODAL_EMBEDDING_MODEL for search; TEXT_EMBEDDING_MODEL for unified mode.source setup.sh --clean-data (this deletes Docker volumes listed by setup, including MinIO/Postgres/VDMS/data-prep data).Default host ports from setup.sh/Compose:
12345; pipeline-manager 3001; search-ms 78908300/9300; vLLM 8200; EVAM 8090; audio 89995672/15672/18834001/4002; Postgres 5432; VDMS 55555; multimodal-dataprep 6016; vector-retriever 6008; embedding service 9777; telemetry 92738640 is loopback-only and transient while setup.sh
downloads missing summary-path models; it should not remain running afterward.Why: Compose publishes these host ports. If another process owns one, the container may fail to bind or the UI may talk to the wrong service.
Actions:
triage.sh to identify listeners.source setup.sh ....http://localhost:3001/health for pipeline-manager and http://localhost:7890/health for video-search when applicable.Inspect ovms-service logs and ov_models/ovms/config.json. Converted models
live under
ov_models/ovms/openvino_models/<device>/<precision>/<source-model>; setup
registers storage-aware names such as
Qwen_Qwen2.5-VL-3B-Instruct_CPU_int8.
Why: pipeline-manager sends VLM/LLM requests to http://ovms-service/v3 when ENABLE_VLLM is false. If OVMS is unhealthy, summary jobs can remain Ready or In Progress.
Likely fixes:
ov_models/ content unless the user accepts re-downloading every model.PM_SUMMARIZATION_MAX_COMPLETION_TOKENS below the default 4000, or use a model with a larger context window.CL_OUT_OF_RESOURCES or cache at 100%: split VLM/LLM across CPU/GPU, use smaller/quantized models, or tune OVMS_CACHE_SIZE_GB cautiously.VLM_TARGET_DEVICE=CPU or another supported device.The model-download container runs before Compose only when an OD artifact or an
OVMS VLM/split LLM artifact is missing. Inspect the error's
ov_models/model-download-*.log, or docker logs vss-model-download while the
job is still running. Verify MODEL_DOWNLOAD_IMAGE, proxy settings, optional
Hugging Face token, selected model/device/precision, free disk space, and that
MODEL_DOWNLOAD_HOST_PORT (default 8640) is available. Increase
MODEL_DOWNLOAD_JOB_TIMEOUT from its 5400 second default only when a valid
download/conversion legitimately needs longer.
When ENABLE_VLLM=true, setup adds compose.vllm.yaml, starts
vllm-cpu-service on host port 8200, and points VLM/LLM APIs to
http://vllm-cpu-service:8000/v1. Experimental ENABLE_VLLM_GPU=true instead
adds compose.vllm.xpu.yaml, starts vllm-xpu-service on the same default host
port, and points both APIs to http://vllm-xpu-service:8000/v1.
Why: In vLLM mode OVMS is not the active inference backend. Debugging OVMS logs will not explain vLLM request failures.
Actions: check the active vLLM service's /health endpoint and logs for model
download/context/cache problems. Verify VLM_MODEL_NAME,
HUGGINGFACE_TOKEN, and VLLM_MAX_MODEL_LEN; for CPU also inspect
VLLM_CPU_KVCACHE_SPACE, and for XPU inspect device visibility and
VLLM_GPU_MEM.
Check video-ingestion health (http://localhost:8090/pipelines) and logs, then rabbitmq-service and minio-service health/logs.
Why: summary ingestion uses DLStreamer Pipeline Server/EVAM to process video, publishes over RabbitMQ MQTT port 1883, and stores media through MinIO. If any of those fail, no chunks reach downstream summarization.
Actions:
OD_MODEL_NAME is a generic YOLO id supported by the model-download
Ultralytics plugin and that the IR exists under
ov_models/object-detection/ultralytics/public/<model>/FP32/.EVAM_DEVICE or detection uses accelerators.RABBITMQ_USER/RABBITMQ_PASSWORD and MinIO credentials match MINIO_ROOT_USER/MINIO_ROOT_PASSWORD.Follow this order: pipeline-manager health → Postgres health → MinIO health → video-ingestion → RabbitMQ → audio-analyzer → inference backend (ovms-service or vllm-cpu-service).
Why: pipeline-manager persists job state in Postgres, uses MinIO for assets, EVAM/RabbitMQ for video events, audio-analyzer for transcripts, and OVMS/vLLM for captions/final summaries.
Actions:
Ready/In Progress stuck states and correlate with OVMS/vLLM logs.VLM_MODEL_NAME; smaller models may have insufficient capacity.libgl1-mesa-dri libgl1-mesa-dev, remove ov_models/ if needed, redeploy, and retest.Check video-search (http://localhost:7890/health), multimodal-dataprep (/v1/dataprep/health on port 6016), vector-retriever (/ready on port 6008), vdms-vector-db (55555, or the Milvus stack when VECTORDB_BACKEND=milvus), multimodal-embedding-serving (9777), MinIO, and whether videos were actually ingested.
Why: search requires embeddings generated by multimodal-dataprep, stored in the active vector database under VS_INDEX_NAME (video_frame_embeddings for search, video_summary_embeddings for unified). video-search does not query the vector DB itself — it delegates all similarity search to vector-retriever, which embeds the query via multimodal-embedding-serving and reads the vector DB, then video-search aggregates the returned frames into ranked videos.
Actions:
MULTIMODAL_EMBEDDING_MODEL or TEXT_EMBEDDING_MODEL changed, old vectors may have incompatible dimensions. Re-ingest, or reset data with source setup.sh --clean-data and rerun the correct setup mode.vector-retriever container is healthy and matches the backend: vector-retriever-vdms for VDMS, vector-retriever-milvus for Milvus (selected by VECTORDB_BACKEND). A mismatch, or a metric/index mismatch (VDB_METRIC_TYPE/VDB_INDEX_TYPE) between multimodal-dataprep and vector-retriever, yields empty results.vector-retriever logs for the embedding call to multimodal-embedding-serving and the vector-DB read; check video-search logs for the delegation call to http://vector-retriever:8000/query.FRAME_INTERVAL, ENABLE_OBJECT_DETECTION, and video diversity affect result quality.The Compose files do not define application log files; use Docker stdout/stderr
via docker logs or triage.sh. Important persistent locations are Docker
volumes docker_minio_data, docker_pg_data, docker_vdms-db,
docker_audio_analyzer_data, docker_data-prep, docker_collector_signals;
the host-backed OVMS repository at ov_models/ovms/; object detection models
under ov_models/object-detection/ultralytics/public/; and failed setup logs at
ov_models/model-download-*.log.
See references/common-failures.md for a compact symptom/cause/fix table.
© open-edge-platform, Apache-2.0. 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 14 other files (scripts, references) in sample-applications/video-search-and-summarization/.github/skills/vss-troubleshoot of open-edge-platform/edge-ai-libraries.
Open the folder on GitHubat commit 0ed0479
Vss Troubleshoot 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 |
|---|---|---|---|---|---|---|
| Vss Troubleshoot this skillopen-edge-platform/edge-ai-libraries | 171 | — | ~3.3k | Automated safety check: Pass | Apache-2.0 | |
| Agentdock User Guideuvwt/agentdock | 1.2k | — | ~1.6k | Automated safety check: Pass | Apache-2.0 | |
| Azure TranslatorMicrosoftDocs/Agent-Skills | 776 | — | ~4.4k | Automated safety check: Pass | CC-BY-4.0 | |
| Iron Proxy Gateway for NanoClawnanocoai/nanoclaw | 31k | — | ~4.6k | Automated safety check: Notes | MIT | |
| GreptimeDB Dev Docker ImageGreptimeTeam/greptimedb | 6.7k | — | ~4k | Automated safety check: Notes | Apache-2.0 | |
| Senior DevOps Toolkitmaslennikov-ig/claude-code-orchestrator-kit | 260 | 6 repos | ~1.1k | Automated safety check: Notes | Custom licence |
uvwt/agentdock
当用户询问 AgentDock 是什么、如何使用、配置在哪里、不同平台或安装方式怎样修改配置并生效、如何重启或验证配置、如何发现并配置 Codex/Claude/Grok 等 Coding Agent 的 ACP,以及常见运行问题时使用;覆盖 macOS Desktop、Windows Desktop、Linux 服务、Docker 和直接运行二进制,不用于源码开发与贡献流程。
MicrosoftDocs/Agent-Skills
Expert knowledge for Azure Translator development including troubleshooting, best practices, decision making, limits & quotas, security, configuration, integrations & coding patterns, and deployment.
nanocoai/nanoclaw
Installs or refreshes Iron Proxy and its Iron Control web console for NanoClaw, with a local Docker setup, database, credentials and a human approval bridge.
GreptimeTeam/greptimedb
Packages a locally built GreptimeDB debug binary into a development-only Docker image for local-cluster testing, with an optional push to a dev registry.
maslennikov-ig/claude-code-orchestrator-kit
Comprehensive DevOps skill for CI/CD, infrastructure automation, containerization, and cloud platforms (AWS, GCP, Azure). Includes pipeline setup…
langbot-app/LangBot
Deploys and configures a LangBot instance with Docker Compose or Kubernetes, covering config.yaml, the Box sandbox runtime, the plugin runtime and the global API key.
open-edge-platform/edge-ai-libraries
Build a new time-series analytics use case on top of the deployed Time Series Analytics microservice — bring it up with Docker Compose (from a repo clone, or by fetching the compose files from…
open-edge-platform/edge-ai-libraries
Scaffolds and wires a new NestJS service/module for the Video Search & Summarization sample app's pipeline-manager using the repo's real conventions.
open-edge-platform/edge-ai-libraries
Deploy Chat Question-and-Answer Core to Kubernetes using Helm (OpenVINO CPU, OpenVINO GPU, or Ollama), including values.yaml configuration, helm install/upgrade, deployment verification, uninstall…
open-edge-platform/edge-ai-libraries
Generates or updates CHANGELOG.md by analyzing git commit history between two branches, tags, or revisions in ANY git repository or folder.
open-edge-platform/edge-ai-libraries
Deploys and manages VSS through setup.sh and its Docker Compose overlays.
open-edge-platform/edge-ai-libraries
A skill your agent uses whenever a developer needs to deploy VSS to Kubernetes, helm install VSS, configure values.yaml for VSS, or run VSS on k8s with GPU/vLLM for the…
Works with
Categories
Diagnose a running or failing video-search-and-summarization deployment. Vss Troubleshoot is an agent skill from open-edge-platform/edge-ai-libraries. Diagnose a running or failing video-search-and-summarization deployment.
Vss Troubleshoot fits situations like: users say is vss up; what mode is running; check vss health; VSS isnt working.
Run `npx skills add open-edge-platform/edge-ai-libraries --skill vss-troubleshoot -a claude-code`. Or copy the skill folder (sample-applications/video-search-and-summarization/.github/skills/vss-troubleshoot in open-edge-platform/edge-ai-libraries) into .claude/skills/vss-troubleshoot in your project. Claude Code loads it when a task matches its description.
Run `npx skills add open-edge-platform/edge-ai-libraries --skill vss-troubleshoot -a codex`. Or copy the skill folder (sample-applications/video-search-and-summarization/.github/skills/vss-troubleshoot in open-edge-platform/edge-ai-libraries) into .agents/skills/vss-troubleshoot 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 open-edge-platform/edge-ai-libraries --skill vss-troubleshoot -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-troubleshoot, .gemini/skills/vss-troubleshoot, .github/skills/vss-troubleshoot and .opencode/skills/vss-troubleshoot in your project.
Going by SKILL.md and its folder, Vss Troubleshoot needs a shell for the scripts in its folder, the command-line tools its instructions call (curl, jq, docker and bash) and credentials named HUGGINGFACE_TOKEN, RABBITMQ_PASSWORD and MINIO_ROOT_PASSWORD. Our summary lists: A Bash shell; Docker; A credential in HUGGINGFACE_TOKEN.
SKILL.md contains no URLs. Its commands use curl and docker, which can reach the network depending on how they are called. 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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
Vss Troubleshoot is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 3.3k tokens (SKILL.md is roughly 13k 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 1.2k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Vss Troubleshoot: Agentdock User Guide (uvwt/agentdock, 1.2k stars), Azure Translator (MicrosoftDocs/Agent-Skills, 776 stars), Iron Proxy Gateway for NanoClaw (nanocoai/nanoclaw, 31k stars) and GreptimeDB Dev Docker Image (GreptimeTeam/greptimedb, 6.7k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
open-edge-platform (a GitHub organization) maintains it in open-edge-platform/edge-ai-libraries, which has 171 GitHub stars. The repository holds 29 skills in this directory. The repository was last updated on October 10, 2026.
Source: open-edge-platform/edge-ai-libraries on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.