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 iBigQiang/feedgrab --skill analyzergit clone --depth 1 https://github.com/iBigQiang/feedgrabWrote 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/ibigqiang/feedgrab/analyzer)<a href="https://agentmods.dev/skills/ibigqiang/feedgrab/analyzer"><img src="https://agentmods.dev/badge/skills/ibigqiang/feedgrab/analyzer/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/ibigqiang/feedgrab/analyzer"><img src="https://agentmods.dev/badge/skills/ibigqiang/feedgrab/analyzer.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 | $0.00040 | $0.01073 |
| Opus 5.5 | $0.00016 | $0.00429 |
| Sonnet 5.5 | $0.00008 | $0.00215 |
| Haiku 4.5 | $0.00004 | $0.00107 |
Grade A, and why
analyzer 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 1mo 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 — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Content Analyzer Skill
Any content → structured analysis report with actionable insights
Trigger
When user sends content (URL, text, or transcript) with analysis intent:
/analyze [URL]- "Analyze this article"
- "What are the key takeaways?"
- Auto-triggered after video/podcast transcription (from video skill)
Pipeline
Step 1: Get Content
Choose tool based on input type:
| Input | Tool |
|---|---|
| Tweet URL | fetch_tweet or Jina Reader |
| Web URL | WebFetch or Jina Reader |
| Local file | Read file directly |
| Transcript from video skill | Use directly |
Step 2: Multi-Dimensional Analysis
Scan content across these dimensions. Only output dimensions with actual content — skip empty ones.
## 📖 Summary
[1-3 sentence core thesis]
**Source**: [author/publisher] · [date]
**Type**: [tweet/article/video/podcast/report]
---
## 💡 Key Insights
### 🎯 Core Arguments
- **Thesis**: [Main argument or finding]
- **Evidence**: [Supporting data or reasoning]
- **Strength**: [How convincing? What's missing?]
### 🤖 Tools & Methods
- **What**: [Tools, frameworks, or techniques mentioned]
- **How**: [How they're used or applied]
- **Relevance**: [Could you use this?]
### ⚙️ Workflow Ideas
- **Optimization**: [Process improvements mentioned]
- **Automation**: [What could be automated]
- **Integration**: [How to fit into existing workflow]
### 📊 Data & Numbers
- **Key metrics**: [Important numbers mentioned]
- **Trends**: [Patterns in the data]
- **Gaps**: [What data is missing]
### ⚠️ Risks & Warnings
- **Pitfalls**: [Explicitly mentioned risks]
- **Blind spots**: [What the author might be missing]
- **Counter-arguments**: [Alternative perspectives]
### 🔗 Resources
- **Tools/APIs**: [Mentioned tools or data sources]
- **People**: [Worth following or referencing]
- **Further reading**: [Related content]
### 🧠 Mental Model Shifts
- **Before**: [Common assumption]
- **After**: [New understanding from this content]
- **Impact**: [How this changes decisions]
---
## ✅ Action Items
### Quick Wins (< 30 min)
- [ ] [Action 1] — Impact: ★★★★ | Effort: Easy
- [ ] [Action 2] — Impact: ★★★ | Effort: Easy
### Deeper Work (1-3 hours)
- [ ] [Action 3] — Impact: ★★★ | Effort: Medium
- [ ] [Action 4] — Impact: ★★ | Effort: Medium
### Exploration (needs validation)
- [ ] [Action 5] — Impact: ★★★ | Effort: Hard | Nature: Exploratory
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.
- 1mo ago First seen · 137 lines · 40 tokens per session scan A 29571b67becc
analyzer is a skill published in the GitHub repository iBigQiang/feedgrab (619 stars, last pushed yesterday), licensed MIT. It adds 40 tokens to every session and 1,073 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-08-30.
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