Efficient Web Research

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.

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:

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

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
  → Instruct subagent: "Navigate to [URL], wait for content to load, extract [specific section]"
  → Do NOT use browser_subagent for static pages — it's expensive

What to Strip from Fetched Pages

Always remove before using fetched content:

• Navigation menus and breadcrumbs
• Cookie banners and GDPR notices
• "Related articles" / "You might also like" blocks
• Footer content (copyright, links)
• Social share buttons
• Ads and sponsored content

Extract and keep:

• Main article / documentation body
• Code blocks
• Tables with data
• Numbered steps or procedures

Search Protocol

Use when the user gives a topic, question, or query — not a specific URL.

Step 1 — Sharpen the Query Before Searching

Do NOT search the raw user query. Transform it first:

Raw: "how to deploy fastapi on aws"
Sharpened: "fastapi AWS deployment tutorial 2024"

Raw: "python async vs threads"
Sharpened: "Python asyncio vs threading performance comparison"

Raw: "best way to structure react project"
Sharpened: "React project folder structure best practices"

Query sharpening rules:

• Add specificity: version numbers, technology names, "tutorial" / "guide" / "comparison"
• Add recency if relevant: current year
• Remove filler words: "how do I", "what is the", "can you explain"
• For code questions: add the language + framework name explicitly

Step 2 — Search and Select

1. Run search_web with the sharpened query
2. Get results (titles + snippets)
3. Scan titles + snippets ONLY — do not fetch yet
4. Pick the TOP 1-2 most relevant results (max 3 in complex cases)
5. Skip results from: forums (if docs exist), aggregator blogs, paywalled sites
6. Prefer: official docs, GitHub repos, well-known tech blogs, academic sources

Step 3 — Fetch Selected Results

Apply the URL Protocol (above) to each selected URL. Process results one at a time — only fetch the second URL if the first didn't answer the question.

Token Rules for Search

• Never read more than 3 URLs per search query
• If the snippet already contains the answer → do NOT fetch the full page, use the snippet
• For factual questions (dates, names, simple facts) → snippet is usually enough
• For procedural questions (how to do X) → fetch 1 relevant page, targeted section only

Multi-URL Protocol

Use when the user provides a list of URLs to compare or summarize.

1. Skim all URLs first (Layer 1 fetch for each)
2. Group by relevance to the user's question
3. Deep-fetch only the most relevant 1-3 URLs
4. Summarize each in 3-5 sentences before combining
5. Never dump raw content from multiple pages — always summarize per-source first

File Protocol

Use when URL points directly to a file (PDF, .txt, .md, .csv, etc.)

• .md / .txt / .csv → read_url_content works directly, read full content
• .pdf → Use browser_subagent or a PDF extraction tool; extract text only
• .json / .yaml → read_url_content, parse structure, summarize schema + key values
• Large files (>500 lines) → Read first 100 lines + last 20 lines + search for relevant sections

Anti-Patterns (Never Do These)

Decision Flowchart (Quick Reference)

Input received
│
├─ GitHub URL?
│   ├─ Fetch README + metadata via API
│   ├─ Answered? → STOP
│   ├─ Need more? → Fetch file tree, pick 1-3 files
│   └─ Still need more? → Fetch specific files only
│
├─ Specific URL?
│   ├─ Try read_url_content → skim headings
│   ├─ Answered? → STOP
│   ├─ Need more? → Targeted section fetch
│   ├─ Still need more? → Full fetch, stripped
│   └─ JS-rendered / broken? → browser_subagent (last resort)
│
├─ Topic/query?
│   ├─ Sharpen query
│   ├─ search_web → scan snippets
│   ├─ Snippet enough? → Answer from snippet, STOP
│   ├─ Need more? → Fetch top 1 result (targeted)
│   └─ Still need more? → Fetch top 2nd result (targeted)
│
└─ List of URLs?
    ├─ Skim all (Layer 1 each)
    ├─ Deep fetch top 1-3 relevant ones
    └─ Summarize per-source, then combine

Output Format Rules

After fetching, structure your response as:

Source: [URL or "Web search for: query"]
Summary: [2-5 sentences of what was found]
Answer: [Direct answer to user's question]
Confidence: [High / Medium / Low — based on source quality]

For multiple sources:

Source 1: ...
Source 2: ...
Combined Answer: ...

Never output:

• Raw HTML fragments
• Full page dumps
• Unattributed information
• More than needed to answer the question

Token Budget Guide

Rule of thumb: If you're about to spend >2000 tokens on a fetch, ask yourself if there's a cheaper path first.

Limitations

• JavaScript Reliance: Standard fetching may not fully render Single Page Applications (SPAs). You must fallback to browser_subagent for these, which is slower and more expensive.
• Paywalls & Protections: This skill cannot bypass CAPTCHAs, bot protections (e.g., strict Cloudflare rules), or hard paywalls.
• GitHub API Limits: Frequent GitHub API requests without authentication may hit rate limits.