Web Reference Fetcher

Overview

This skill is designed to be called by other skills or workflows. It provides a three-step pipeline:

1. Fetch: Retrieve web content using jina.ai reader API
2. Analyze: Use subagents to extract specific topics and create detailed summaries
3. Save: Store the analyzed content as structured markdown files

When to Use This Skill

Use this skill when:

• Another skill needs to fetch and analyze web documentation
• You have URL(s) and need structured topic extraction
• You need detailed summaries saved to specific file paths
• Working with reference materials that require analysis and organization

Input Format

This skill expects:

1. URL(s): One or more web URLs to fetch
2. Experiment Context: Filename and entry ID to determine output directory
3. Output Path: Standardized path workdir/<filename>_<entry_id>/references/ref_<N>.md

Directory naming convention:

• Extract filename from JSONL path: public_test from data/public_test.jsonl
• Combine with entry ID: public_test_public_test_1
• Create references subdirectory: workdir/public_test_public_test_1/references/
• Save each reference as: ref_1.md, ref_2.md, etc.

Workflow

Step 1: Fetch Web Content

Use the provided script to fetch raw content from URL(s).

Execute:

python3 .claude/skills/web-reference-fetcher/scripts/fetch_url.py <url> --output workdir/<filename>_<entry_id>/references/ref_<N>.md

What it does:

• Fetches content via https://r.jina.ai/<url>
• Returns clean markdown content
• Saves to standardized location

Script options:

• --output <path>: Save fetched content to file (required for standardized workflow)
• --silent: Suppress progress messages

Standardized output location:

• Reference files: workdir/<filename>_<entry_id>/references/ref_<N>.md
• Example: workdir/public_test_public_test_1/references/ref_1.md

Output: The script saves the fetched markdown content to the specified path.

Step 2: Analyze with Subagents

Pass the fetched content to a subagent along with extraction requirements.

Subagent invocation pattern:

Use the Task tool to launch a general-purpose subagent:

Prompt template:
以下のウェブコンテンツから、{トピック名}に関する詳細な情報を抽出してください。

コンテンツ:
---
{fetched_markdown_content}
---

抽出要件:
{extraction_requirements}

以下の形式でmarkdownとして出力してください:

# {トピック名}

## {セクション1}
[詳細な説明、テーブル、仕様など]

## {セクション2}
[詳細な説明、手順、コード例など]

...

Extraction requirements should specify:

• What topics to extract
• What level of detail is needed
• What format to use (tables, lists, code blocks, etc.)
• Any specific sections or keywords to focus on

Step 3: Save Structured Output

Save the subagent's output to the specified path.

File operations:

• Create parent directories if they don't exist
• Save with UTF-8 encoding
• Verify file was written successfully

Complete Example Usage

Example 1: Fetch and Analyze a Single URL

# 1. Fetch content
CONTENT=$(python3 .claude/skills/web-reference-fetcher/scripts/fetch_url.py \
  "https://example.com/docs")

# 2. Pass to subagent via Task tool with:
#    - Fetched content
#    - Topic: "API Authentication Methods"
#    - Extraction: "Extract all authentication methods, parameters, and examples"

# 3. Save subagent output
#    Path: workdir/references/api_auth.md

Example 2: Multiple URLs for Different Topics

# Fetch URL 1
CONTENT1=$(python3 .claude/skills/web-reference-fetcher/scripts/fetch_url.py \
  "https://example.com/spec")

# Analyze with subagent for Topic 1
# Save to: workdir/references/specification.md

# Fetch URL 2
CONTENT2=$(python3 .claude/skills/web-reference-fetcher/scripts/fetch_url.py \
  "https://example.com/tutorial")

# Analyze with subagent for Topic 2
# Save to: workdir/references/tutorial.md

Integration with Other Skills

This skill is designed to be called by other skills. Example integration:

# In another skill (e.g., test-case-handler skill):

## Step 3: Fetch Reference Documentation

For each reference URL in the test case:
1. Invoke web-reference-fetcher skill
2. Pass URL and topic extracted from test case instruction
3. Specify output path: `workdir/references/task_{N}/reference_{M}.md`
4. Use the saved markdown for further processing

Extraction Requirements Examples

For Technical Specifications

抽出要件:
- すべての技術パラメータを表形式で抽出
- 測定手順をステップバイステップで記載
- 計算式と例を含める
- 品質基準と許容範囲を明記

For API Documentation

抽出要件:
- すべてのエンドポイントをリスト化
- リクエスト/レスポンス形式を示す
- 認証方法を詳細に説明
- エラーコードと対処方法を含める

For Tutorials/Procedures

抽出要件:
- 手順を番号付きリストで抽出
- 各手順の詳細な説明を含める
- 必要なツールと前提条件を明記
- トラブルシューティング情報を追加

Error Handling

Fetch Errors

• URL not accessible: Report error, suggest alternatives
• Network issues: Check connectivity and jina.ai availability
• Invalid URL format: Validate URL before attempting fetch

Subagent Errors

• Content too large: Split into chunks and analyze separately
• Unclear extraction requirements: Ask caller to specify details
• Format issues: Validate subagent output before saving

File System Errors

• Permission denied: Check write permissions
• Path not found: Create parent directories automatically
• Disk full: Report error with suggested cleanup

Resources

scripts/

• fetch_url.py: Fetches web content via jina.ai
  • Input: URL string
  • Output: Clean markdown content to stdout
  • Options: --output to save raw content, --silent for quiet mode

Advanced Usage

Batch Processing

For multiple URLs:

for url in "${urls[@]}"; do
  # Fetch each URL
  # Launch subagents in parallel if possible
  # Save to respective paths
done

Custom Output Formatting

Provide detailed formatting instructions to subagents:

フォーマット要件:
- 見出しレベル: H2を最上位とする
- コードブロック: 言語を明示する
- テーブル: Markdown形式、ヘッダー行を含む
- リスト: 階層構造を保持する

Caching Fetched Content

To avoid re-fetching:

# Save raw content first
python3 .claude/skills/web-reference-fetcher/scripts/fetch_url.py \
  "https://example.com/docs" --output /tmp/cached_content.md

# Use cached content for multiple analyses with different extraction requirements

Dependencies

• Python 3.6+
• curl command-line tool
• Internet connectivity
• Access to Task tool for subagent invocation
• Write permissions to output directories

Quality Assurance

Before completing:

• URL was fetched successfully
• Subagent received correct content and extraction requirements
• Output file is well-structured and comprehensive
• File was saved to the correct path
• Caller has been informed of the saved location

Communication Protocol

When called by other skills:

1. Receive: URL(s), extraction requirements, output path(s)
2. Execute: Fetch → Analyze → Save pipeline
3. Return: Success status and saved file path(s)
4. Report errors: Clear error messages with suggested fixes

Self-Contained Design

This skill is self-contained and can be invoked without knowledge of:

• JSONL file structures
• Test case formats
• Specific project conventions

It only requires:

• Valid URL(s)
• Clear extraction requirements
• Valid output path(s)