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 J-StaR-Films-Studios/VibeCode-Protocol-Suite --skill high-fidelity-extractiongit clone --depth 1 https://github.com/J-StaR-Films-Studios/VibeCode-Protocol-SuiteWrote 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/j-star-films-studios/vibecode-protocol-suite/high-fidelity-extraction)<a href="https://agentmods.dev/skills/j-star-films-studios/vibecode-protocol-suite/high-fidelity-extraction"><img src="https://agentmods.dev/badge/skills/j-star-films-studios/vibecode-protocol-suite/high-fidelity-extraction/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/j-star-films-studios/vibecode-protocol-suite/high-fidelity-extraction"><img src="https://agentmods.dev/badge/skills/j-star-films-studios/vibecode-protocol-suite/high-fidelity-extraction.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00023 | $0.00769 |
| Opus 5 | $0.00012 | $0.00385 |
| Sonnet 5 | $0.00005 | $0.00154 |
| Haiku 4.5 | $0.00002 | $0.00077 |
Grade A, and why
high-fidelity-extraction 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 7d ago.
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 — 60 lines — stays where its author put it; the contents beside it link to each section on GitHub.
High Fidelity Data Extraction Protocol
This skill enables the agent to extract deep intelligence from social media platforms (Instagram, TikTok, YouTube, etc.) and complex web environments with high precision.
🚀 Core Philosophy: The Extraction Spectrum
When tasked with extraction, always check the prompt for specific limitations. If no limitations are provided, assume Standard Level.
Extraction Capability Matrix
| Feature | Basic (Standard) | Deep (Advanced) | Elite (Full Intelligence) |
|---|---|---|---|
| Captions | Full Text + Hashtags | + Edited timestamps | + OCR from video overlays |
| Comments | Top 3-5 (Top Level) | Top 10 + Threaded replies | Full sentiment & pain point mapping |
| Engagement | Likes / Views count | Engagement Rate % | Share/Save estimates & Viral Velocity |
| Brand Intel | @Mentions in caption | + Link-in-bio analysis | + Competitor comparison vs Meta Ad Library |
| Visuals | Profile Screen/Grid | Individual Post Screenshots | UI/UX Reverse Engineering of Funnel |
| Technical | URL collection | Precise ISO Timestamps | API Pattern Mapping & DB Schema generation |
📊 Tabular Output Format
Unless the user specifies otherwise, all extracted data should be compiled into a Markdown table for maximum scannability.
Standard Template:
| Post Type | Caption Snippet | Top Comments (Synthesized) | Key Metrics | Brand/Tags |
| :--- | :--- | :--- | :--- | :--- |
| Pinned | "How to be..." | Users asking about discipline tips | 30k Likes | @Adidas, #NYC |
| Recent 1 | "Day in life..." | High praise for work/life balance | 15k Views | @CeraVe |
🧠 Smart Filtering Strategy (Instagram Reels)
When the goal involves "high-performing" content or maximizing engagement:
- Reels First: Navigate to the
/reels/tab immediately. View counts are not visible on the main grid but are overlayed on Reel thumbnails. - Baseline Calculation:
- Extract view counts from the first 6-12 visible Reels.
- Calculate the average (mean) view count.
- Filtration:
- Only click/extract posts that exceed this average.
- This ensures we focus valuable browser resources on proven content.
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.
- 7d ago First seen · 60 lines · 23 tokens per session scan A 1ad98b0f193c
high-fidelity-extraction is a skill published in the GitHub repository J-StaR-Films-Studios/VibeCode-Protocol-Suite (24 stars, last pushed today), licensed ISC. It adds 23 tokens to every session and 769 once invoked, about $0.0001 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 2026-09-03.
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