Borrowing it
Nothing to install: this file belongs to anortham/julie. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/anortham/julie/main/.claude/skills/web-research/SKILL.mdgit clone --depth 1 https://github.com/anortham/julieWrote 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/anortham/julie/web-research)<a href="https://agentmods.dev/skills/anortham/julie/web-research"><img src="https://agentmods.dev/badge/skills/anortham/julie/web-research/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/anortham/julie/web-research"><img src="https://agentmods.dev/badge/skills/anortham/julie/web-research.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 | $0.00067 | $0.01117 |
| Opus 5.5 | $0.00027 | $0.00447 |
| Sonnet 5.5 | $0.00013 | $0.00223 |
| Haiku 4.5 | $0.00007 | $0.00112 |
Grade C, and why
web-research scanned grade C 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 2mo 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.
Recursive force deletehighDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
rm -rf docs/web/ Copies of this mod
1 near-identical copy found in the catalogue:
- web-research — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 129 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Web Research
Fetch web pages, index them locally, and read selectively using Julie's tools. This replaces dumping entire web pages into context (which wastes thousands of tokens) with a workflow that indexes the content and lets you pull out just the sections you need.
Prerequisites
browser39 must be installed. Check with:
which browser39
If missing, tell the user to download the latest binary release from browser39 releases and add it to their PATH.
Workflow
Step 0 (Optional): Discover URLs from a Topic
If the user gave a research topic rather than a specific URL, find candidate pages first:
browser39 search "your research topic" --output json > /tmp/b39-search.json
Parse the links[] array and pick the URLs worth fetching. Skip this step when the user already provided URLs.
Step 1: Fetch and Save
Determine the target file path from the URL. The directory structure mirrors the URL:
docs/web/
docs.rs/axum/latest.md
developer.mozilla.org/Web/API/Fetch_API.md
github.com/tokio-rs/tokio.md
Fetch the page and save directly to the target file. Never print full page content to stdout.
# Fetch (rm ensures no stale results — --output appends, not overwrites)
echo '{"id":"1","action":"fetch","v":1,"seq":1,"url":"THE_URL","options":{"selector":"article","strip_nav":true,"include_links":true}}' > /tmp/b39-cmd.jsonl
rm -f /tmp/b39-out.jsonl
browser39 batch /tmp/b39-cmd.jsonl --output /tmp/b39-out.jsonl
# Extract markdown directly to file (no stdout leak)
mkdir -p docs/web/TARGET_DOMAIN/TARGET_PATH_DIR
python3 -c "
import sys,json
with open('/tmp/b39-out.jsonl') as f:
for line in f:
d=json.loads(line)
md = d.get('markdown','')
if md:
with open('docs/web/TARGET_DOMAIN/TARGET_PATH.md','w') as out:
out.write(md)
print(f'Saved {len(md)} chars to docs/web/TARGET_DOMAIN/TARGET_PATH.md')
"
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.
- 2mo ago First seen · 129 lines · 67 tokens per session scan C ab0e7b58c264
web-research is a skill published in the GitHub repository anortham/julie (6 stars, last pushed 27d ago), licensed MIT. It adds 67 tokens to every session and 1,117 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it C with 1 finding (recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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