Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add ChrisLamDev/hermes-core-skills --skill multi-agent-browser-text-extractiongit clone --depth 1 https://github.com/ChrisLamDev/hermes-core-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/chrislamdev/hermes-core-skills/multi-agent-browser-text-extraction)<a href="https://agentmods.dev/skills/chrislamdev/hermes-core-skills/multi-agent-browser-text-extraction"><img src="https://agentmods.dev/badge/skills/chrislamdev/hermes-core-skills/multi-agent-browser-text-extraction.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00051 | $0.01175 |
| Opus 5 | $0.00026 | $0.00588 |
| Sonnet 5 | $0.00010 | $0.00235 |
| Haiku 4.5 | $0.00005 | $0.00118 |
Grade A, and why
multi-agent-browser-text-extraction scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Multi-Agent Browser Text Extraction
Extract large volumes of original text from JavaScript-heavy Chinese websites (e.g. ddc.shengyen.org 法鼓全集) using parallel delegate_task sub-agents, each with browser toolset.
When to Use
- Target site has JS-heavy tree menu navigation (not static HTML)
- Need to extract 10+ pages of verbatim text from different chapters/books
- Site has bot detection that varies by URL path
- Each extraction requires clicking through tree menus to reach specific chapters
- Content must be 100% verbatim (no paraphrasing)
Prerequisites
delegate_tasktool withbrowsertoolset enabled- Target site URL (usually a single base URL with query-param navigation)
- Mapping of questions/topics to specific book chapters
Workflow
Phase 1: Discovery (single browser session)
- Navigate to the base URL (e.g.
https://ddc.shengyen.org/) - Explore the tree menu structure. Note:
- Which expandable levels exist (e.g. 輯 > 冊 > 章)
- Whether chapters are loaded via
javascript:void(null)links (they usually are) - Whether the URL changes when clicking chapters (it usually doesn't — content loads via AJAX)
- Test
browser_consoleto extract content:document.querySelector('[class*="content"]')?.innerText || document.body.innerText.substring(0, 5000) - Identify any bot detection early. If the site blocks, try different URL paths.
Phase 2: Batch Spawn (parallel extraction)
Divide the chapters into batches of 5-9 questions each. Each batch spawns a delegate_task with toolsets: ["browser"].
Critical passing of context:
- Provide the EXACT chapter names and book numbers (e.g.
05-02 正信的佛教) - Provide output format template
- Set
max_iterations=40-55for 6-9 chapters - Include site navigation instructions in the context
Context template:
HOW TO USE THE SITE:
1. Go to https://ddc.shengyen.org/
2. Click "第五輯 佛教入門類" in left sidebar to expand
3. Click the book (e.g. "05-03 學佛群疑") to expand its chapters
4. Click a chapter name from the expanded sub-menu
5. Use browser_console: document.querySelector('[class*="content"]')?.innerText || ...
6. Copy the EXACT text, verbatim
QUESTIONS TO EXTRACT:
{list of questions with book and chapter mapping}
Output format for EACH question:
## Question: {question}
**Source**: 《書名》聖嚴法師 — Chapter Name
**Original Text**:
> {exact, verbatim, can be long}
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 7d ago First seen · 118 lines · 51 tokens per session scan A acd5dd1ef435
multi-agent-browser-text-extraction is a skill published in the GitHub repository ChrisLamDev/hermes-core-skills (9 stars, last pushed 2mo ago), licensed MIT. It adds 51 tokens to every session and 1,175 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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