efficient-web-research
Helps your agent research the web efficiently. It avoids downloading entire pages unnecessarily, saving time and resources.
What it does
This tool helps the AI search the internet smartly. It avoids downloading entire pages unnecessarily, focusing only on what's needed to answer your question.
When to use it
- ✓When you ask the AI to research a topic.
- ✓When you provide a link to a page or repository.
- ✓When the AI needs to find specific information on a website.
Example of what to ask your AI
“Research how RAFT consensus works.”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
The AI first tries to understand what you asked for and where to look (a link, a code site like GitHub, or a general topic). Then, it fetches information gradually, starting with the simplest parts and only going deeper if needed.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add sickn33/agentic-awesome-skills --skill efficient-web-researchOr download just the file:
curl -L https://raw.githubusercontent.com/sickn33/agentic-awesome-skills/main/plugins/agentic-awesome-skills-claude/skills/efficient-web-research/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
Efficient Web Research Skill
A protocol for accessing web content in the most token-efficient, accurate, and structured way — using the right tool at the right depth, and stopping as soon as the question is answerable.
When to Use
- Use this skill when the task matches this description: Protocol for token-efficient web research. Use when accessing URLs, GitHub repos, or running search queries. Prevents full-page fetching waste.
Core Principle
Fetch the minimum needed to answer. Skim before you dive. Stop when you can answer.
Every unnecessary fetch wastes tokens and adds noise. This skill enforces a layered approach where you escalate fetch depth only when shallower layers fail.
Step 1 — Classify the Input
Before fetching anything, identify what kind of input you received:
| Input Type | Example | Go To |
|---|---|---|
| GitHub repo URL | github.com/user/repo |
GitHub Protocol |
| Specific page URL | docs.python.org/3/library/os |
URL Protocol |
| Topic / query (no URL) | "how does RAFT consensus work" | Search Protocol |
| Multiple URLs | List of links | Multi-URL Protocol |
| PDF / file link | .pdf, .txt, .md URL |
File Protocol |
GitHub Protocol
Use when input is a GitHub URL (repo, file, PR, issue, etc.)
Step 1 — Parse the URL
github.com/{owner}/{repo} → Repo root
github.com/{owner}/{repo}/tree/{branch} → Directory
github.com/{owner}/{repo}/blob/{branch}/{path} → Single file
github.com/{owner}/{repo}/issues/{n} → Issue
github.com/{owner}/{repo}/pull/{n} → Pull request
Step 2 — Use GitHub API (preferred over scraping)
Always prefer the GitHub API. It returns clean JSON — no HTML parsing needed.
# Repo metadata (name, description, language, stars, topics)
GET https://api.github.com/repos/{owner}/{repo}
# File tree (see what files exist — very cheap)
GET https://api.github.com/repos/{owner}/{repo}/git/trees/{ref}?recursive=1
# Single file content (base64 encoded)
GET https://api.github.com/repos/{owner}/{repo}/contents/{path}?ref={ref}
# README only (usually enough to understand the repo)
GET https://api.github.com/repos/{owner}/{repo}/readme
Step 3 — Layered Fetch for Repos
Layer 1 (always do first):
→ Fetch repo metadata + README only
→ Can you answer the user's question now? YES → STOP. NO → continue.
Layer 2 (only if needed):
→ Fetch file tree to understand structure
→ Identify the 1-3 most relevant files based on the question
→ Can you answer now? YES → STOP. NO → continue.
Layer 3 (last resort):
→ Fetch specific relevant files only (never fetch all files)
→ Prioritize: main entry point, config files, key modules
Token Rules for GitHub
- README alone answers ~70% of "what does this repo do" questions — always try it first
- Never fetch more than 3 files in a single research turn
- If a file exceeds ~300 lines, read only the top (imports + class/function signatures)
- Decode base64 content from API before passing to context
URL Protocol
Use when the user gives a specific non-GitHub URL (docs, articles, blogs, etc.)
Step 1 — Assess the URL type
| Site type | Likely works with | Notes |
|---|---|---|
| Static docs / MDN / ReadTheDocs | read_url_content |
Fast, clean, cheap |
| News articles / blogs | read_url_content |
Usually fine |
| SPAs / React/Next.js apps | browser_subagent |
JS-rendered |
| Auth-gated pages | browser_subagent |
Needs login |
| Raw GitHub files (raw.githubusercontent) | read_url_content |
Direct text |
Step 2 — Layered Fetch
Layer 1 — Skim
→ Fetch the URL with read_url_content
→ Read only headings (H1, H2, H3) and first paragraph
→ Does this page contain what the user needs? NO → try a different URL or search. YES → continue.
Layer 2 — Targeted Extract
→ If the page has anchor links (e.g. /docs/page#section), fetch with the anchor
→ Extract only the relevant section (200–500 tokens max)
→ Can you answer? YES → STOP.
Layer 3 — Full Fetch
→ Fetch full page, strip boilerplate (nav, footer, ads, cookie banners, sidebars)
→ Cap at 2000 tokens. Summarize before passing to answer.
Layer 4 — Browser Subagent (last resort only)
→ Use ONLY if read_url_content returns empty, garbled, or JS-placeholder content
…
FAQ
Why doesn't the AI download the whole page?
Downloading everything uses more of the AI's 'memory' (tokens) and can include a lot of useless information. Fetching only what's essential is faster and more efficient.
What are 'tokens'?
Tokens are like small pieces of information that the AI uses to understand and generate text. More information means more tokens are used.
Will the AI always use GitHub?
No, it uses GitHub if you give it a link there. If it's a regular link, it uses a different method. If it's just a topic, it searches the web.
Details
Original description by the author
Protocol for token-efficient web research. Use when accessing URLs, GitHub repos, or running search queries. Prevents full-page fetching waste.
Popularity
- GitHub stars
- 48k
- Ranking position
- #2903
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
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