openclaw/doc-web-assistant

Doc Web Assistant

Purpose

This skill lets OpenClaw treat a documentation website as a local structured knowledge base.

Read When

• Crawling documentation websites into JSON
• Building a local offline doc knowledge base
• Answering questions from documentation content
• Turning documentation steps into terminal command plans
• Preparing safe command execution from doc pages

Runtime Requirements

• Python
• pip

Allowed Tools

• Bash(python:*)
• Bash(py:*)
• Bash(pip:*)

It supports three stages:

1. Use OpenClaw native web_fetch to obtain page content.
2. Import fetched content into local JSON files.
3. Query the local JSON store by natural language.
4. Build an execution plan from matched documentation commands.

The acquisition path is:

1. Use native web_fetch first.
2. Save or pass the fetched content into this skill via import.

This skill is designed for documentation assistant workflows such as:

• Installing software from official docs
• Extracting build commands from long doc pages
• Creating terminal-ready command plans from documentation
• Using one prompt to let OpenClaw read docs before acting

Skill files

• doc_web_assistant.py: importer, crawler, indexer, retriever, planner
• requirements.txt: Python dependencies
• SKILL.md: skill instructions for OpenClaw

Installation

Install the required Python packages:

pip install -r requirements.txt

If OpenClaw runs inside the skill directory, this is enough.

If you need an explicit command with absolute path, use:

python doc_web_assistant.py --help

Core commands

1. Import fetched content into JSON

Preferred path for OpenClaw.

First, use native web_fetch to get the document content from the target URL. Then save that content to a local file such as ./fetched_doc.md or ./fetched_doc.html. Finally import it with:

python doc_web_assistant.py import --input "./fetched_doc.md" --source-url "https://example.com/docs/install" --out "./doc_store"

You may also import HTML directly:

python doc_web_assistant.py import --input "./fetched_doc.html" --source-url "https://example.com/docs/install" --out "./doc_store" --format html

Output files will be created under ./doc_store:

• manifest.json
• chunks.json
• pages/page_0001.json

2. Query imported documentation

python doc_web_assistant.py query --db "./doc_store" --query "如何安装 ARMv9 优化版 llama.cpp" --top-k 5

This returns the most relevant sections and code blocks from the local store.

3. Build a doc-grounded command plan

python doc_web_assistant.py plan --db "./doc_store" --query "根据文档给我安装 KleidiAI 优化版 llama.cpp 的命令" --top-k 5

This returns:

• relevant doc chunks
• extracted commands
• risk classification for each command

Recommended OpenClaw workflow

When the user asks a documentation-driven task, follow this sequence.

Workflow A: First-time doc import with native fetch

Use when the target documentation is not yet available locally.

1. Use OpenClaw native web_fetch to fetch <DOC_URL>.
2. Save the fetched content to a temporary file.
3. Import that file into the local doc store.

python doc_web_assistant.py import --input "./fetched_doc.md" --source-url "<DOC_URL>" --out "./doc_store"

Then inspect results with:

python doc_web_assistant.py query --db "./doc_store" --query "<USER_TASK>" --top-k 5

Workflow B: Answer from docs

Use when the user wants explanation only.

python doc_web_assistant.py query --db "./doc_store" --query "<USER_QUESTION>" --top-k 5

Answer from the matched chunks and cite the source URL when possible.

Workflow C: Plan terminal operations from docs

Use when the user wants executable steps.

python doc_web_assistant.py plan --db "./doc_store" --query "<USER_TASK>" --top-k 5

Then convert returned commands into an ordered plan.

Workflow D: Execute after planning

Use only after a plan is built.

• Execute low risk commands directly after checking environment.
• Review medium risk commands before execution.
• Never auto-execute high risk commands without explicit user confirmation.

Operational policy for OpenClaw

When using this skill, OpenClaw should behave as follows.

If the user says

• "读取这个文档并告诉我怎么做"
• "根据这个网址里的安装文档帮我执行"
• "把这个文档导入成本地 JSON"
• "从这个网页里抽取命令"

Then OpenClaw should

1. Determine whether the task needs import, query, or plan.
2. If the task references a URL and no local store exists, use native web_fetch first.
3. Import the fetched content with python doc_web_assistant.py import ....
4. Use query for explanation tasks.
5. Use plan for terminal-execution tasks.
6. Present or execute commands according to risk level.
7. Prefer commands extracted from documentation instead of inventing commands.
8. If native web_fetch returns incomplete or noisy content, say so explicitly before planning execution.

JSON schema overview

manifest.json

Contains:

• root URL
• generation time
• page count
• page listing

pages/page_xxxx.json

Contains per-page structured content:

• source URL
• title
• summary
• sections
• code blocks
• extracted commands
• crawl depth

chunks.json

Contains flattened retrieval chunks for:

• section text
• code block text
• extracted commands

Example: build a local doc assistant

Native fetch + import

1. Use native web_fetch on:

• https://docs.radxa.com/en/orion-o6/app-development/llama.cpp

2. Save the fetched content to ./radxa_llama_doc.md

3. Import it:

python doc_web_assistant.py import --input "./radxa_llama_doc.md" --source-url "https://docs.radxa.com/en/orion-o6/app-development/llama.cpp" --out "./radxa_llama_docs"

Query

python doc_web_assistant.py query --db "./radxa_llama_docs" --query "KleidiAI 编译参数" --top-k 3

Plan

python doc_web_assistant.py plan --db "./radxa_llama_docs" --query "根据文档生成安装和编译命令" --top-k 5

Safety rules

• Do not assume extracted commands are valid for every OS or architecture.
• Always check whether the doc targets Linux, Windows, ARM64, x86_64, or Docker.
• Treat package installation, build, download, service, and docker commands as at least medium risk.
• Treat delete, format, reboot, permission-change, and destructive system commands as high risk.
• If the documentation is incomplete, say so clearly instead of guessing.

Notes

• This skill works best on documentation pages with clear headings, paragraphs, lists, and code blocks.
• It is intended for documentation websites rather than arbitrary web apps.
• Native web_fetch is the acquisition method for anti-bot or dynamically rendered sites.
• The importer stores structured content for local reuse, which makes later prompts faster.

Troubleshooting

• If pip is missing, install Python with pip first.
• If native web_fetch succeeds but the imported structure is weak, save the fetched content as Markdown and import that Markdown file.
• If the page is heavy on JavaScript rendering, use a pre-rendered documentation URL when possible.
• If query returns weak matches, use a more specific prompt including product name, component name, and action.

Reporting issues

If the skill logic is unclear, update SKILL.md and keep the command examples aligned with the Python script.