Evo Pedestrian Counter

Community

Zhang-Henry

evo-pedestrian-counter

Count unique pedestrians in surveillance videos using YOLOv8 detection and multi-object tracking. Deduplicates across frames, excludes cyclists. Writes per-video counts to Excel.

Overview

Publisher	Zhang-Henry
Repository	`CoEvoSkills`
Skill name	`evo-pedestrian-counter`
Stars	83
Forks	9
Bundled files	4
License	Apache-2.0
Links	Skill source Repository Homepage

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
4 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by Zhang-Henry on GitHub. Read the source before you install it.

Installation

Install the Evo Pedestrian Counter AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/Zhang-Henry/CoEvoSkills/tree/main/artifacts/skills/pedestrian-traffic-counting/evo-pedestrian-counter
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/Zhang-Henry/CoEvoSkills/tree/main/artifacts/skills/pedestrian-traffic-counting/evo-pedestrian-counter

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/Zhang-Henry/CoEvoSkills.git /tmp/CoEvoSkills
mkdir -p .claude/skills
cp -r /tmp/CoEvoSkills/artifacts/skills/pedestrian-traffic-counting/evo-pedestrian-counter .claude/skills/evo-pedestrian-counter

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Evo Pedestrian Counter in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Evo Pedestrian Counter on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Evo Pedestrian Counter is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Pedestrian Counter for Surveillance Videos

Problem

Given a directory of surveillance-camera video files, count the number of unique pedestrians (people travelling on foot) in each video and write the per-video counts to a structured Excel workbook.

Approach

Detect persons with YOLO (COCO class 0).
Track across frames so each physical person gets one ID.
Filter noise tracks shorter than a visibility window derived from the video's own frame rate.
Exclude cyclists via person-bicycle bounding-box overlap.
Write to Excel via openpyxl.

Scripts

File	Key function
`scripts/utils.py`	`get_video_files`, `count_pedestrians_in_video`, `write_results_to_excel`
`scripts/run_pipeline.py`	`run_pipeline(video_dir, output_path, sheet_name, col_filename, col_count)`
`scripts/validate.py`	`validate_output(output_path, video_dir, sheet_name, col_filename, col_count)`

Runnable Fresh-Agent Example

Adapt the five variables below to the current task instruction.

python
import sys
sys.path.insert(0, '/app/environment/skills/evo-pedestrian-counter/scripts')

from run_pipeline import run_pipeline
from validate import validate_output

# ---- set from the current task instruction ----
video_dir   = '/path/to/input/videos'
output_path = '/path/to/output.xlsx'
sheet_name  = 'data'
col_file    = 'source'
col_count   = 'total'
# -----------------------------------------------

results = run_pipeline(video_dir, output_path,
                       sheet_name=sheet_name,
                       col_filename=col_file,
                       col_count=col_count)

ok, issues = validate_output(output_path, video_dir,
                             sheet_name=sheet_name,
                             col_filename=col_file,
                             col_count=col_count)
if not ok:
    raise RuntimeError(issues)
print('Done:', results)

How It Works

A fresh YOLO model is created per video to reset tracker state.
Sampling interval: derived as max(1, round(fps / 10)) so that roughly ten frames per second are processed regardless of source fps.
Minimum-detection threshold: derived as max(2, round(fps * 0.5 / sample_every)) so a person must be observed for at least half a second.
Cyclist exclusion: person tracks that spatially overlap a bicycle track in the majority of co-occurring frames are removed.
COCO class indices 0 (person) and 1 (bicycle) are public taxonomy.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Evo Pedestrian Counter AI skill do?

Count unique pedestrians in surveillance videos using YOLOv8 detection and multi-object tracking. Deduplicates across frames, excludes cyclists. Writes per-video counts to Excel.

Why use Evo Pedestrian Counter on TypingMind?

Because you install it once and use it with any model. Evo Pedestrian Counter is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Evo Pedestrian Counter in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Zhang-Henry/CoEvoSkills/tree/main/artifacts/skills/pedestrian-traffic-counting/evo-pedestrian-counter. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Evo Pedestrian Counter?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Evo Pedestrian Counter?

As many as you like. As long as a model supports skills, you can use Evo Pedestrian Counter with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Evo Pedestrian Counter AI skill free?

Yes. It is published on GitHub by Zhang-Henry under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Pedestrian Counter for Surveillance Videos

Problem

Approach

Scripts

Runnable Fresh-Agent Example

How It Works

Bundled files

Frequently asked questions

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