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 Illuminated2020/DeepAgents-AutoGLM --skill web-researchgit clone --depth 1 https://github.com/Illuminated2020/DeepAgents-AutoGLMWrote 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/illuminated2020/deepagents-autoglm/web-research)<a href="https://agentmods.dev/skills/illuminated2020/deepagents-autoglm/web-research"><img src="https://agentmods.dev/badge/skills/illuminated2020/deepagents-autoglm/web-research/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/illuminated2020/deepagents-autoglm/web-research"><img src="https://agentmods.dev/badge/skills/illuminated2020/deepagents-autoglm/web-research.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00024 | $0.00976 |
| Opus 5 | $0.00012 | $0.00488 |
| Sonnet 5 | $0.00005 | $0.00195 |
| Haiku 4.5 | $0.00002 | $0.00098 |
Grade A, and why
web-research 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 10d 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.
This is a copy
100% identical to web-research — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Web Research Skill
This skill provides a structured approach to conducting comprehensive web research using the task tool to spawn research subagents. It emphasizes planning, efficient delegation, and systematic synthesis of findings.
When to Use This Skill
Use this skill when you need to:
- Research complex topics requiring multiple information sources
- Gather and synthesize current information from the web
- Conduct comparative analysis across multiple subjects
- Produce well-sourced research reports with clear citations
Research Process
Step 1: Create and Save Research Plan
Before delegating to subagents, you MUST:
-
Create a research folder - Organize all research files in a dedicated folder relative to the current working directory:
mkdir research_[topic_name]This keeps files organized and prevents clutter in the working directory.
-
Analyze the research question - Break it down into distinct, non-overlapping subtopics
-
Write a research plan file - Use the
write_filetool to createresearch_[topic_name]/research_plan.mdcontaining:- The main research question
- 2-5 specific subtopics to investigate
- Expected information from each subtopic
- How results will be synthesized
Planning Guidelines:
- Simple fact-finding: 1-2 subtopics
- Comparative analysis: 1 subtopic per comparison element (max 3)
- Complex investigations: 3-5 subtopics
Step 2: Delegate to Research Subagents
For each subtopic in your plan:
-
Use the
tasktool to spawn a research subagent with:- Clear, specific research question (no acronyms)
- Instructions to write findings to a file:
research_[topic_name]/findings_[subtopic].md - Budget: 3-5 web searches maximum
-
Run up to 3 subagents in parallel for efficient research
Subagent Instructions Template:
Research [SPECIFIC TOPIC]. Use the web_search tool to gather information.
After completing your research, use write_file to save your findings to research_[topic_name]/findings_[subtopic].md.
Include key facts, relevant quotes, and source URLs.
Use 3-5 web searches maximum.
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.
- 10d ago First seen · 103 lines · 24 tokens per session scan A c609f8cd36e3
web-research is a skill published in the GitHub repository Illuminated2020/DeepAgents-AutoGLM (120 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 24 tokens to every session and 976 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to web-research, differing in 0 lines, and is treated as a copy.
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