NotebookLM Research Assistant
PleasePrompto/notebooklm-skill
Lets Claude Code ask questions of your Google NotebookLM notebooks through browser automation and return answers grounded in your uploaded sources.
Analyze and count objects in videos using Google Gemini API (object counting, pedestrian detection, vehicle tracking, and surveillance video analysis).
$ npx skills add benchflow-ai/skillsbench --skill gemini-count-in-video -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install benchflow-ai/skillsbench gemini-count-in-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/benchflow-ai/skillsbench.git skills-src && mkdir -p .claude/skills && cp -r skills-src/tasks-extra/pedestrian-traffic-counting/environment/skills/gemini-count-in-video .claude/skills/gemini-count-in-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 "gemini-count-in-video" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks-extra/pedestrian-traffic-counting/environment/skills/gemini-count-in-video into .claude/skills/gemini-count-in-video/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gemini-count-in-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/benchflow-ai/skillsbench/tree/main/tasks-extra/pedestrian-traffic-counting/environment/skills/gemini-count-in-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 benchflow-ai/skillsbench --skill gemini-count-in-video -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install benchflow-ai/skillsbench gemini-count-in-video --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .agents/skills && cp -r skills-src/tasks-extra/pedestrian-traffic-counting/environment/skills/gemini-count-in-video .agents/skills/gemini-count-in-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 "gemini-count-in-video" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks-extra/pedestrian-traffic-counting/environment/skills/gemini-count-in-video into .agents/skills/gemini-count-in-video/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gemini-count-in-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 benchflow-ai/skillsbench --skill gemini-count-in-video -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install benchflow-ai/skillsbench gemini-count-in-video --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/tasks-extra/pedestrian-traffic-counting/environment/skills/gemini-count-in-video .cursor/skills/gemini-count-in-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 "gemini-count-in-video" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks-extra/pedestrian-traffic-counting/environment/skills/gemini-count-in-video into .cursor/skills/gemini-count-in-video/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gemini-count-in-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/benchflow-ai/skillsbench.git --path tasks-extra/pedestrian-traffic-counting/environment/skills/gemini-count-in-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 benchflow-ai/skillsbench --skill gemini-count-in-video -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install benchflow-ai/skillsbench gemini-count-in-video --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/tasks-extra/pedestrian-traffic-counting/environment/skills/gemini-count-in-video .gemini/skills/gemini-count-in-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 "gemini-count-in-video" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks-extra/pedestrian-traffic-counting/environment/skills/gemini-count-in-video into .gemini/skills/gemini-count-in-video/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gemini-count-in-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 benchflow-ai/skillsbench gemini-count-in-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 benchflow-ai/skillsbench --skill gemini-count-in-video -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .github/skills && cp -r skills-src/tasks-extra/pedestrian-traffic-counting/environment/skills/gemini-count-in-video .github/skills/gemini-count-in-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 "gemini-count-in-video" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks-extra/pedestrian-traffic-counting/environment/skills/gemini-count-in-video into .github/skills/gemini-count-in-video/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gemini-count-in-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 benchflow-ai/skillsbench --skill gemini-count-in-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 benchflow-ai/skillsbench gemini-count-in-video --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/tasks-extra/pedestrian-traffic-counting/environment/skills/gemini-count-in-video .opencode/skills/gemini-count-in-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 "gemini-count-in-video" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks-extra/pedestrian-traffic-counting/environment/skills/gemini-count-in-video into .opencode/skills/gemini-count-in-video/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gemini-count-in-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.
gemini-count-in-videoAnalyze and count objects in videos using Google Gemini API (object counting, pedestrian detection, vehicle tracking, and surveillance video analysis).
Gemini Count In Video is an agent skill from benchflow-ai/skillsbench. Analyze and count objects in videos using Google Gemini API (object counting, pedestrian detection, vehicle tracking, and surveillance video analysis).
Its SKILL.md is about 2.7k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It works with Google Gemini. The repository describes itself as: SkillsBench evaluates how well skills work and how effective agents are at using them. The licence is Apache-2.0.
Read from SKILL.md and the folder at commit 9a1f4dd. 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.
No scripts in the folder and no shell commands in SKILL.md (its code samples are python and json).
From the folder's file list and the shell code blocks in SKILL.md.
Links to these hosts (documentation or services it may open):
ai.google.devaistudio.google.comFrom URLs in SKILL.md, links to its own repository left out.
Names these keys or tokens, usually read from environment variables:
GEMINI_API_KEYFrom names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Gemini Count In Video loads about 2.7k tokens when it runs. Until then it costs about 43 tokens; SKILL.md has 484 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 benchflow-ai/skillsbench at commit 9a1f4dd, republished under its Apache-2.0 licence (© benchflow-ai). 484 words, ~2,698 tokens.
.claude/skills/gemini-count-in-video/SKILL.md (or your agent's skills folder).This skill enables video analysis and object counting using the Google Gemini API, with a focus on counting pedestrians, detecting objects, tracking movement, and analyzing surveillance footage. It supports precise prompting for differentiated counting (e.g., pedestrians vs cyclists vs vehicles).
The following Python libraries are required:
from google import genai
from google.genai import types
import os
import timeFor object counting tasks, structure results as JSON:
{
"success": true,
"video_file": "surveillance_001.mp4",
"model": "gemini-2.0-flash-exp",
"counts": {
"pedestrians": 12,
"cyclists": 3,
"vehicles": 5
},
"notes": "Optional observations about the counting process or edge cases"
}success: Whether the analysis completed successfullyvideo_file: Name of the analyzed video filemodel: Gemini model used for the requestcounts: Object counts by categorynotes: Any clarifications or warnings about the countfrom google import genai
import os
import time
import re
client = genai.Client(api_key=os.getenv("GEMINI_API_KEY"))
# Upload video (File API for >20MB)
myfile = client.files.upload(file="surveillance.mp4")
# Wait for processing
while myfile.state.name == "PROCESSING":
time.sleep(5)
myfile = client.files.get(name=myfile.name)
if myfile.state.name == "FAILED":
raise ValueError("Video processing failed")
# Prompt for counting pedestrians with clear exclusion criteria
prompt = """Count the total number of pedestrians who are WALKING through the scene in this surveillance video.
IMPORTANT RULES:
- ONLY count people who are walking on foot
- DO NOT count people riding bicycles
- DO NOT count people driving cars or other vehicles
- Count each unique pedestrian only once, even if they appear in multiple frames
Provide your answer as a single integer number representing the total count of pedestrians.
Answer with just the number, nothing else.
Your answer should be enclosed in <answer> and </answer> tags, such as <answer>5</answer>.
"""
response = client.models.generate_content(
model="gemini-2.0-flash-exp",
contents=[prompt, myfile],
)
# Parse the response
response_text = response.text.strip()
match = re.search(r"<answer>(\d+)</answer>", response_text)
if match:
count = int(match.group(1))
print(f"Pedestrian count: {count}")
else:
print("Could not parse count from response")from google import genai
import os
import time
import re
def upload_and_wait(client, file_path: str, max_wait_s: int = 300):
"""Upload video and wait for processing."""
myfile = client.files.upload(file=file_path)
waited = 0
while myfile.state.name == "PROCESSING" and waited < max_wait_s:
time.sleep(5)
waited += 5
myfile = client.files.get(name=myfile.name)
if myfile.state.name == "FAILED":
raise ValueError(f"Video processing failed: {myfile.state.name}")
if myfile.state.name == "PROCESSING":
raise TimeoutError(f"Processing timeout after {max_wait_s}s")
return myfile
client = genai.Client(api_key=os.getenv("GEMINI_API_KEY"))
# Process all videos in directory
video_dir = "/app/video"
video_extensions = {".mp4", ".mkv", ".avi", ".mov"}
results = {}
for filename in os.listdir(video_dir):
if any(filename.lower().endswith(ext) for ext in video_extensions):
video_path = os.path.join(video_dir, filename)
print(f"Processing {filename}...")
# Upload and analyze
myfile = upload_and_wait(client, video_path)
response = client.models.generate_content(
model="gemini-2.0-flash-exp",
contents=["Count pedestrians walking through the scene. Answer with just the number.", myfile],
)
# Extract count
count = int(re.search(r'\d+', response.text).group())
results[filename] = count
print(f" Count: {count}")
print(f"\nProcessed {len(results)} videos")
# Results dictionary can now be used for further processing or saving# Count different categories separately
prompt = """Analyze this surveillance video and count:
1. Pedestrians (people walking on foot)
2. Cyclists (people riding bicycles)
3. Vehicles (cars, trucks, motorcycles)
RULES:
- Count each unique individual/vehicle only once
- If someone switches from walking to cycling, count them in their primary mode
- Provide counts as three separate numbers
Format your answer as:
Pedestrians: <number>
Cyclists: <number>
Vehicles: <number>
"""
response = client.models.generate_content(
model="gemini-2.0-flash-exp",
contents=[prompt, myfile],
)
# Parse multiple counts
text = response.text
pedestrians = int(re.search(r'Pedestrians:\s*(\d+)', text).group(1))
cyclists = int(re.search(r'Cyclists:\s*(\d+)', text).group(1))
vehicles = int(re.search(r'Vehicles:\s*(\d+)', text).group(1))# Request structured output with XML-like tags
prompt = """Count the total number of pedestrians walking through the scene.
You should reason and think step by step. Provide your answer as a single integer.
Your answer should be enclosed in <answer> and </answer> tags, such as <answer>5</answer>.
"""
response = client.models.generate_content(
model="gemini-2.0-flash-exp",
contents=[prompt, myfile],
)
# Robust extraction
match = re.search(r"<answer>(\d+)</answer>", response.text)
if match:
count = int(match.group(1))
else:
# Fallback: try to find any number in response
numbers = re.findall(r'\d+', response.text)
count = int(numbers[0]) if numbers else 0<answer>N</answer>) for reliable parsing.import time
def upload_and_wait(client, file_path: str, max_wait_s: int = 300):
"""Upload video and wait for processing with timeout."""
myfile = client.files.upload(file=file_path)
waited = 0
while myfile.state.name == "PROCESSING" and waited < max_wait_s:
time.sleep(5)
waited += 5
myfile = client.files.get(name=myfile.name)
if myfile.state.name == "FAILED":
raise ValueError(f"Video processing failed: {myfile.state.name}")
if myfile.state.name == "PROCESSING":
raise TimeoutError(f"Processing timeout after {max_wait_s}s")
return myfile
def count_with_fallback(client, video_path):
"""Count pedestrians with error handling and fallback."""
try:
myfile = upload_and_wait(client, video_path)
prompt = """Count pedestrians walking through the scene.
Answer with just the number in <answer></answer> tags."""
response = client.models.generate_content(
model="gemini-2.0-flash-exp",
contents=[prompt, myfile],
)
# Try structured parsing first
match = re.search(r"<answer>(\d+)</answer>", response.text)
if match:
return int(match.group(1))
# Fallback to any number found
numbers = re.findall(r'\d+', response.text)
if numbers:
return int(numbers[0])
print(f"Warning: Could not parse count, defaulting to 0")
return 0
except Exception as e:
print(f"Error processing video: {e}")
return 0Common issues:
<answer></answer> for reliable parsing© benchflow-ai, 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
Just SKILL.md in tasks-extra/pedestrian-traffic-counting/environment/skills/gemini-count-in-video of benchflow-ai/skillsbench.
Open the folder on GitHubat commit 9a1f4dd
Gemini Count In 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 |
|---|---|---|---|---|---|---|
| Gemini Count In Video this skillbenchflow-ai/skillsbench | 1.8k | — | ~2.7k | Automated safety check: Pass | Apache-2.0 | |
| NotebookLM Research AssistantPleasePrompto/notebooklm-skill | 7.8k | 14 repos | ~2.4k | Automated safety check: Notes | MIT | |
| Brand and Design Toolkitnextlevelbuilder/ui-ux-pro-max-skill | 135k | 1 repos | ~3.5k | Automated safety check: Pass | MIT | |
| AI Image Generation and Editingzhayujie/CowAgent | 47k | — | ~1.3k | Automated safety check: Pass | MIT | |
| OpenCLI Smart Search Routerjackwener/OpenCLI | 30k | 2 repos | ~753 | Automated safety check: Pass | Apache-2.0 | |
| NotebookLM Automationteng-lin/notebooklm-py | 20k | — | ~4.1k | Automated safety check: Pass | MIT |
PleasePrompto/notebooklm-skill
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nextlevelbuilder/ui-ux-pro-max-skill
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zhayujie/CowAgent
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jackwener/OpenCLI
Routes a search question to the most suitable opencli source, normally one AI site plus up to two specialist sites, with call limits per question and a recap at the end.
teng-lin/notebooklm-py
Installs, authenticates and operates Gemini Notebook (NotebookLM) through the notebooklm-py CLI or its typed async Python API, for notebooks, sources, grounded chat and generated artifacts.
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Works with
Analyze and count objects in videos using Google Gemini API (object counting, pedestrian detection, vehicle tracking, and surveillance video analysis). Gemini Count In Video is an agent skill from benchflow-ai/skillsbench. Analyze and count objects in videos using Google Gemini API (object counting, pedestrian detection, vehicle tracking, and surveillance video analysis).
Run `npx skills add benchflow-ai/skillsbench --skill gemini-count-in-video -a claude-code`. Or copy the skill folder (tasks-extra/pedestrian-traffic-counting/environment/skills/gemini-count-in-video in benchflow-ai/skillsbench) into .claude/skills/gemini-count-in-video in your project. Claude Code loads it when a task matches its description.
Run `npx skills add benchflow-ai/skillsbench --skill gemini-count-in-video -a codex`. Or copy the skill folder (tasks-extra/pedestrian-traffic-counting/environment/skills/gemini-count-in-video in benchflow-ai/skillsbench) into .agents/skills/gemini-count-in-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 benchflow-ai/skillsbench --skill gemini-count-in-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/gemini-count-in-video, .gemini/skills/gemini-count-in-video, .github/skills/gemini-count-in-video and .opencode/skills/gemini-count-in-video in your project.
Going by SKILL.md and its folder, Gemini Count In Video needs credentials named GEMINI_API_KEY. Our summary lists: Python 3; A credential in GEMINI_API_KEY.
SKILL.md names 2 domains. As links in the text: ai.google.dev and aistudio.google.com. 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.
Gemini Count In Video 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 2.7k 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 Gemini Count In Video: NotebookLM Research Assistant (PleasePrompto/notebooklm-skill, 7.8k stars), Brand and Design Toolkit (nextlevelbuilder/ui-ux-pro-max-skill, 135k stars), AI Image Generation and Editing (zhayujie/CowAgent, 47k stars) and OpenCLI Smart Search Router (jackwener/OpenCLI, 30k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
benchflow-ai (a GitHub organization) maintains it in benchflow-ai/skillsbench, which has 1,835 GitHub stars. The repository holds 189 skills in this directory. The repository was last updated on July 23, 2026.
Source: benchflow-ai/skillsbench on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.