Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add OpenLAIR/OpenSkill --skill evo-pedestrian-vlm-countinggit clone --depth 1 https://github.com/OpenLAIR/OpenSkillWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/openlair/openskill/evo-pedestrian-vlm-counting)<a href="https://agentmods.dev/skills/openlair/openskill/evo-pedestrian-vlm-counting"><img src="https://agentmods.dev/badge/skills/openlair/openskill/evo-pedestrian-vlm-counting/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/openlair/openskill/evo-pedestrian-vlm-counting"><img src="https://agentmods.dev/badge/skills/openlair/openskill/evo-pedestrian-vlm-counting.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00084 | $0.00813 |
| Opus 5 | $0.00042 | $0.00407 |
| Sonnet 5 | $0.00017 | $0.00163 |
| Haiku 4.5 | $0.00008 | $0.00081 |
Grade A, and why
evo-pedestrian-vlm-counting scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pedestrian VLM Counting
Sends extracted video frames to Vision LLM APIs with carefully engineered prompts for unique pedestrian counting, parses integer results robustly, and writes final counts to a properly formatted Excel file.
When to Use
- Counting unique pedestrians in surveillance video footage
- Analyzing foot traffic across multiple video files
- Generating Excel reports of pedestrian counts per video
- Using vision AI to perform person re-identification across frames
Dependencies
This skill depends on evo-video-frame-extraction for frame extraction. Import frame extraction functions from that skill.
Core Functions
The skill provides these functions in scripts/pedestrian_counting.py:
count_pedestrians_openai(b64_frames: list[str], api_key: str = None) -> int
Sends base64-encoded frames to OpenAI GPT-4o for unique pedestrian counting. Uses structured prompting with chain-of-thought reasoning to improve accuracy. Returns an integer count.
count_pedestrians_gemini(b64_frames: list[str], api_key: str = None) -> int
Sends frames to Google Gemini for unique pedestrian counting. Accepts base64 JPEG strings. Returns an integer count.
parse_count_from_response(response_text: str) -> int
Robustly extracts an integer pedestrian count from LLM response text. Handles JSON responses, plain integers, and natural language responses. Falls back to regex extraction.
write_results_to_excel(results: list[dict], output_path: str) -> None
Writes results to an Excel file with columns "filename" and "number" on a sheet named "results". Uses pandas with openpyxl engine. Enforces integer types.
run_pedestrian_counting_pipeline(video_dir: str, output_path: str, api: str = "openai", interval: float = 3.0, max_frames: int = 10) -> list[dict]
End-to-end pipeline: discovers videos, extracts frames, counts pedestrians via VLM, writes Excel. The api parameter selects "openai" or "gemini".
Usage Example
from scripts.pedestrian_counting import run_pedestrian_counting_pipeline
# Run the full pipeline
results = run_pedestrian_counting_pipeline(
video_dir="/path/to/videos",
output_path="pedestrian_counts.xlsx",
api="openai",
interval=3.0,
max_frames=10
)
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 74 lines · 84 tokens per session scan A 0075af3b8cfc
evo-pedestrian-vlm-counting is a skill published in the GitHub repository OpenLAIR/OpenSkill (90 stars, last pushed 2d ago), licensed Apache-2.0. It adds 84 tokens to every session and 813 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-11.
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