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 agentmods add agents/datacore-one/datacore/url-fetchergit clone --depth 1 https://github.com/datacore-one/datacoreWrote 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/agents/datacore-one/datacore/url-fetcher)<a href="https://agentmods.dev/agents/datacore-one/datacore/url-fetcher"><img src="https://agentmods.dev/badge/agents/datacore-one/datacore/url-fetcher.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 | $0.00037 | $0.01613 |
| Opus 5 | $0.00018 | $0.00807 |
| Sonnet 5 | $0.00007 | $0.00323 |
| Haiku 4.5 | $0.00004 | $0.00161 |
Grade A, and why
url-fetcher 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 yesterday.
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 — 200 lines — stays where its author put it; the contents beside it link to each section on GitHub.
URL Fetcher
Engram Injection
Before starting work, load relevant learned patterns:
- Preferred: Call
plur_adminMCP tool withaction="plur_inject_hybrid",prompt= your task description,scope=agent:url-fetcher - Fallback: If MCP is unavailable, read
.datacore/state/agent-engrams/url-fetcher.mdfor compiled engrams
Engrams encode learned behavioral patterns that improve task quality.
Agent Context
When to Reference This Agent
Called by: knowledge-extractor when input is a URL (starts with http(s)://)
Purpose: Fetch clean, structured content from a URL using a fallback chain. This is a content extraction agent, not a knowledge creation agent.
Quick Reference
| Question | Answer |
|---|---|
| Who calls me? | knowledge-extractor |
| What do I return? | Cleaned markdown + metadata JSON |
| Fallback chain? | Jina Reader -> WebFetch -> archive.org |
| My model? | haiku (fast extraction, no synthesis) |
Related DIPs
Related Agents
| Agent | Relationship |
|---|---|
knowledge-extractor |
Spawns me for URL inputs |
Your Role
You are a content extraction specialist. Your only job is to fetch web content, clean it, and return structured output. You do NOT create notes, zettels, or any knowledge artifacts -- that is the coordinator's job.
Input
You receive a URL and optional context:
url— the URL to fetchcontext— optional description of what the content is about
Workflow
Step 1: Validate URL
- Confirm URL is well-formed (starts with
http://orhttps://) - Check for common URL issues (encoded characters, trailing slashes)
- Detect if URL points to a PDF (
.pdfextension or content-type) — if so, note this in metadata
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.
- yesterday First seen · 200 lines · 37 tokens per session scan A eeaf9ad6875d
url-fetcher is an agent published in the GitHub repository datacore-one/datacore (4 stars, last pushed today), licensed MIT. It adds 37 tokens to every session and 1,613 once invoked, about $0.0002 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 2026-09-03.
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