video-analytics
- Repo stars 1,423
- License Apache-2.0
- Author updated Live
- Author repo video-search-and-summarization
- Domain
- AI
- Compatible agents
-
- Claude Code
- Cursor
- Cline
- Codex
- Windsurf
- Gemini CLI
- +20
- Trust score
- 94 / 100 · audit passed
- Author / version / license
- @NVIDIA-AI-Blueprints · Apache-2.0
- Token usage
- Lean
- Setup complexity
- Guided setup
- External API key
- Not required
- Operating systems
- Unspecified (assume cross-platform)
- Runtime requirements
- No special requirements
- Permissions
-
- Read-only
- Shell exec
- Write / modify
- Network behavior
- External requests
- Install commands
- 26 variants
Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.
Heads up: 未限定 allowed-tools,默认拥有全部工具权限。
---
name: video-analytics
description: Query video analytics data and metrics from Elastic search via the VA-MCP server (port 9901). Th…
category: ai
runtime: no special runtime
---
# video-analytics output preview
## PART A: Task fit
- Use case: Query video analytics data and metrics from Elastic search via the VA-MCP server (port 9901). This includes incidents, alerts, sensor data, and metrics. Use for any question about violations, alerts, incidents, object counts, speeds, occupancy, or anything that requires looking up recorded events. This is the primary way to answer a question that requires incidents, alerts and other metrics such as people counts and violations..
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “Deployment prerequisite / REQUIRED: Two-Step Pattern (copy this exactly) / Tool Reference” and do not present inference as author intent.
## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “Query video analytics data and metrics from Elastic search via the VA-MCP server (port 9901). This includes incidents, alerts, sensor data, and metrics. Use for any question about violations, alerts, incidents, object counts, speeds, occupancy, or anything that requires looking up recorded events. This is the primary way to answer a question that requires incidents, alerts and other metrics such as people counts and violations.”.
- **02** When the source has headings, the agent prioritizes “Deployment prerequisite / REQUIRED: Two-Step Pattern (copy this exactly) / Tool Reference” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, run shell commands, write/modify files; may access external network resources; usually needs no extra API key.
## Running Rules
- read files, run shell commands, write/modify files; may access external network resources; usually needs no extra API key.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options. The source mentions slash commands such as `/deploy`; use them first when your agent supports command triggers.
Name target files or source material, expected output, forbidden changes, and whether network or shell access is allowed. Permission fingerprint: read files, run shell commands, write/modify files.
Start with a small task and check whether the result follows “Deployment prerequisite / REQUIRED: Two-Step Pattern (copy this exactly) / Tool Reference”. Inspect diffs, logs, previews, or tests before expanding scope.
Confirm the final output includes a concrete result, evidence, and next action. If it stays generic, tighten inputs, boundaries, and acceptance criteria.
---
name: video-analytics
description: Query video analytics data and metrics from Elastic search via the VA-MCP server (port 9901). Th…
category: ai
source: NVIDIA-AI-Blueprints/video-search-and-summarization
---
# video-analytics
## When to use
- Query video analytics data and metrics from Elastic search via the VA-MCP server (port 9901). This includes incidents…
- Use it when the task has clear inputs, repeatable steps, and validation criteria.
## What to provide
- Target material, scope, expected result, and forbidden changes.
- Whether network, commands, file writes, or external services are allowed.
## Execution rules
- Organize steps around “Deployment prerequisite / REQUIRED: Two-Step Pattern (copy this exactly) / Tool Reference” and keep inference separate from source facts.
- read files, run shell commands, write/modify files; may access external network resources; usually needs no extra API key.
- Validate with a small sample before expanding the task.
## Output requirements
- Return the deliverable, key evidence, validation method, and next action.
- Mark missing information as unknown; do not invent commands, platforms, or dependencies. The author source anchors workflow facts; repository files anchor sources and commands; Fluxly only adds fit, limitations, and quality judgment.
skill "video-analytics" {
input -> user goal + target files + boundaries + acceptance criteria
context -> Deployment prerequisite / REQUIRED: Two-Step Pattern (copy this exactly) / Tool Reference
rules -> SKILL.md triggers / order / output contract
runtime -> no special runtime | read files, run shell commands, write/modify files | may access external network resources
guardrails -> usually needs no extra API key + small-sample validation + diff/log review
output -> copyable result + checklist + next iteration
} Video Analytics (VA-MCP)
Queries incidents, alerts, and metrics stored in Elasticsearch via MCP JSON-RPC at port 9901.
ALWAYS run the commands below yourself and relay results to the user. Do NOT guess or describe — actually execute and report back.
Deployment prerequisite
This skill reads from the Elasticsearch/VA-MCP stack brought up by the VSS alerts profile (either verification or real-time mode). Before any query:
Probe the VA-MCP endpoint:
curl -sf --max-time 5 "http://${HOST_IP}:9901/mcp" >/dev/null 2>&1 || \ curl -sf --max-time 5 "http://${HOST_IP}:9901/" >/dev/nullIf the probe fails, ask the user:
"The VSS
alertsprofile isn't running on$HOST_IP(VA-MCP unreachable). Which mode should I deploy —verification(CV) orreal-time(VLM)?"- Answer → hand off to the
/deployskill with-p alerts -m <mode>. Return here once it succeeds. - If the user declines → stop. No incidents/alerts/metrics to query without the alerts stack up.
(If your caller has granted explicit pre-authorization to deploy autonomously — e.g. the request says "pre-authorized to deploy prerequisites", or you are running in a non-interactive evaluation harness with that permission — skip the confirmation and invoke
/deploydirectly. Default the mode toverificationunless the request specifies otherwise.)- Answer → hand off to the
If the probe passes, proceed.
REQUIRED: Two-Step Pattern (copy this exactly)
Every query requires two shell commands run in sequence:
# Step 1: initialize — get session ID from response HEADER
SESSION_ID=$(curl -si -X POST http://localhost:9901/mcp \
-H "Content-Type: application/json" \
-H "Accept: application/json, text/event-stream" \
-d '{"jsonrpc":"2.0","method":"initialize","params":{"protocolVersion":"2024-11-05","capabilities":{},"clientInfo":{"name":"cli","version":"1.0"}},"id":0}' \
| grep -i "mcp-session-id" | awk '{print $2}' | tr -d '\r')
# Step 2: call the tool using the session ID in the header
curl -s -X POST http://localhost:9901/mcp \
-H "Content-Type: application/json" \
-H "Accept: application/json, text/event-stream" \
-H "mcp-session-id: $SESSION_ID" \
-d '{"jsonrpc":"2.0","method":"tools/call","params":{"name":"video_analytics__get_incidents","arguments":{"max_count":10}},"id":1}' \
| grep '^data:' | sed 's/^data: //' | jq -r '.result.content[0].text'
The session ID comes from the response header
mcp-session-id, not the body. Skipping Step 1 always results inBad Request: Missing session ID.
Tool Reference
Replace the -d payload in Step 2 with any of the following.
video_analytics__get_incidents
| Parameter | Type | Description |
|---|---|---|
source |
string | Sensor ID or place name (optional) |
source_type |
string | sensor or place |
start_time |
string | ISO 8601: YYYY-MM-DDTHH:MM:SS.sssZ |
end_time |
string | ISO 8601 |
max_count |
int | Max results (default: 10) |
includes |
list | Extra fields: objectIds, info |
vlm_verdict |
string | confirmed, rejected, or unverified |
# Recent incidents (all sensors)
-d '{"jsonrpc":"2.0","method":"tools/call","params":{"name":"video_analytics__get_incidents","arguments":{"max_count":10}},"id":1}'
# For a specific sensor
-d '{"jsonrpc":"2.0","method":"tools/call","params":{"name":"video_analytics__get_incidents","arguments":{"source":"<sensor-id>","source_type":"sensor","max_count":20}},"id":1}'
# Confirmed (VLM-verified) only
-d '{"jsonrpc":"2.0","method":"tools/call","params":{"name":"video_analytics__get_incidents","arguments":{"vlm_verdict":"confirmed","max_count":10}},"id":1}'
video_analytics__get_incident
-d '{"jsonrpc":"2.0","method":"tools/call","params":{"name":"video_analytics__get_incident","arguments":{"id":"<incident-id>","includes":["objectIds","info"]}},"id":1}'
video_analytics__get_sensor_ids
-d '{"jsonrpc":"2.0","method":"tools/call","params":{"name":"video_analytics__get_sensor_ids","arguments":{}},"id":1}'
video_analytics__get_places
-d '{"jsonrpc":"2.0","method":"tools/call","params":{"name":"video_analytics__get_places","arguments":{}},"id":1}'
video_analytics__get_fov_histogram
-d '{"jsonrpc":"2.0","method":"tools/call","params":{"name":"video_analytics__get_fov_histogram","arguments":{"source":"<sensor-id>","source_type":"sensor","start_time":"<ISO>","end_time":"<ISO>","object_type":"Person","bucket_count":10}},"id":1}'
video_analytics__analyze
analysis_type: max_min_incidents, average_speed, avg_num_people, avg_num_vehicles
-d '{"jsonrpc":"2.0","method":"tools/call","params":{"name":"video_analytics__analyze","arguments":{"source":"<sensor-id>","source_type":"sensor","start_time":"<ISO>","end_time":"<ISO>","analysis_type":"avg_num_people"}},"id":1}'
vst_sensor_list
-d '{"jsonrpc":"2.0","method":"tools/call","params":{"name":"vst_sensor_list","arguments":{}},"id":1}'
Decide Fit First
Design Intent
How To Use It
Boundaries And Review