Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/AI-Learning-Gems/AI-Learning-Gems.github.ionpx agentmods add rules/ai-learning-gems/ai-learning-gems.github.io/web-source-fetchingWrote 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/rules/ai-learning-gems/ai-learning-gems.github.io/web-source-fetching)<a href="https://agentmods.dev/rules/ai-learning-gems/ai-learning-gems.github.io/web-source-fetching"><img src="https://agentmods.dev/badge/rules/ai-learning-gems/ai-learning-gems.github.io/web-source-fetching/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/rules/ai-learning-gems/ai-learning-gems.github.io/web-source-fetching"><img src="https://agentmods.dev/badge/rules/ai-learning-gems/ai-learning-gems.github.io/web-source-fetching.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 | $0.11056 | $0.11056 |
| Opus 5.5 | $0.04422 | $0.04422 |
| Sonnet 5.5 | $0.02211 | $0.02211 |
| Haiku 4.5 | $0.01106 | $0.01106 |
Grade A, and why
web-source-fetching scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
| Use browser for static pages | Use `curl` or `read_url_content` | How it starts
The opening of the file, as written. The whole thing — 962 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Web Source Fetching Strategies
This document contains rules and strategies for fetching authoritative content from various web sources. It is a living document — add new entries as you discover what works for each website.
Core Principles
- Never rely on internal world knowledge for technical content — every claim must trace to a downloaded source
- Source of truth hierarchy: LaTeX/source code > HTML > PDF > summaries
- Download first, read later — save content locally before processing
- Prefer raw sources — GitHub raw files, arXiv LaTeX, not rendered HTML
search_webis for discovery only — use it to identify sources, not to extract content
Anti-Patterns to Avoid
| ❌ Don't Do This | ✅ Do This Instead |
|---|---|
Use search_web for content |
Use it only for source discovery |
| Fabricate URLs | Only use URLs you've actually fetched |
| Regenerate content from memory | Quote/cite downloaded sources |
| Use browser for static pages | Use curl or read_url_content |
| Link to external URLs for images | Download images locally |
| Conclude a source is incomplete because grep found no matches | List section headings first (grep '^##'), then read relevant sections. A grep false negative is not evidence of missing content. |
Extraction Completeness Verification (MANDATORY for Web Sources)
Readability is necessary but not sufficient. A web extraction can produce a readable, substantial .md file that is missing entire sections due to a soft paywall, JavaScript rendering failure, or extraction timeout. This is especially common with Substack (free preview + paywalled body) and Medium.
After extracting any web article, verify structural completeness:
- List section headings:
grep '^##\|^###\|^####' content.md— the output should show a logical article structure (introduction, body sections, conclusion). - Check for paywall markers:
grep -i 'upgrade to paid\|subscribe to continue\|for paid subscribers\|unlock this post' content.md— if any of these strings appear, the extraction hit a paywall boundary. Re-extract with--profilefor the relevant site (e.g.,--profile substack). - Check the ending: Read the last 20 lines of the file. Does the article end with a conclusion/summary, or does it cut off abruptly with a subscription prompt? An abrupt ending signals truncation.
- Compare against expected content: If you know the article discusses topics X, Y, and Z (from web search summaries or the TEXTBOOK-PLAN), verify that the section headings include all three topics. If topic Z is missing from the headings, the extraction may be incomplete.
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 · 962 lines · 11,056 tokens per session scan A 944e054a8eac
web-source-fetching is a cursor rule published in the GitHub repository AI-Learning-Gems/AI-Learning-Gems.github.io (23 stars, last pushed 4mo ago), licensed MIT. It adds 11,056 tokens to every session, about $0.0442 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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