efficient-web-research – skill Dữ liệu & Khoa học cho agent AI

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.

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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.

Scope and evidence precedence

The numeric file/URL/token caps below are efficiency defaults, not hard limits. Full-file/bundle audits, legal or security analysis, user-supplied multi-source comparisons and exact quotations require enough original context to support the requested conclusion; do not claim a complete review from excerpts. Search snippets are discovery clues, not sufficient evidence for consequential or ambiguous claims. Read the primary page and cite its exact supporting content. Tool names are illustrative; use the host's actual tools and delegate only when authorized. A URL fragment does not guarantee server-side section extraction.

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.

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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](#github-protocol) |

| Specific page URL | docs.python.org/3/library/os | [URL Protocol](#url-protocol) |

| Topic / query (no URL) | "how does RAFT consensus work" | [Search Protocol](#search-protocol) |

| Multiple URLs | List of links | [Multi-URL Protocol](#multi-url-protocol) |

| PDF / file link | .pdf, .txt, .md URL | [File Protocol](#file-protocol) |

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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

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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
  → 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

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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

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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

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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

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Anti-Patterns (Never Do These)

| Anti-pattern | Why it's bad | Do this instead |

|---|---|---|

| Fetching full page for a simple fact | Wastes 1000s of tokens | Use snippet or targeted anchor |

| Using browser_subagent for static sites | Very expensive | Use read_url_content first |

| Searching with the raw user query | Vague results | Sharpen query first |

| Fetching 5+ search results | Token explosion | Max 3, stop when answered |

| Dumping raw HTML into context | Noisy, wasteful | Always strip to Markdown |

| Fetching "just in case" | Unnecessary tokens | Only fetch what's needed to answer |

| Re-fetching the same URL | Redundant | Cache result in context, reuse |

| Fetching entire GitHub repo | Extremely wasteful | README + targeted files only |

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Decision Flowchart (Quick Reference)

Input re

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