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 skills/grainulation/grainulator/fetchnpx skills add grainulation/grainulator --skill fetchgit clone --depth 1 https://github.com/grainulation/grainulatorWhat 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 | $0.00027 | $0.00949 |
| Opus 5 | $0.00014 | $0.00475 |
| Sonnet 5 | $0.00005 | $0.00190 |
| Haiku 4.5 | $0.00003 | $0.00095 |
Grade A, and why
fetch 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 2d 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 — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/fetch -- Ad-hoc URL fetch with smart extraction
Pulls a URL's main content (title, description, body paragraphs) without the HTML boilerplate. Delegates to silo's smart-fetch MCP tool, which strips scripts/styles/nav/footer and targets <main> or <article> regions. Typical reduction: 80-99% vs raw HTML.
Arguments
$ARGUMENTS
Expected: /fetch <url> [--mode auto|concise|full|meta-only] [--no-cache] [--privacy]
--mode auto(default): tries concise extraction, falls back to full if quality degrades--mode concise: caps body at ~2KB--mode full: returns all extracted paragraphs--mode meta-only: only title + description (smallest)--no-cache: skip local cache read, force network fetch--privacy: don't write to cache (use for sensitive URLs)
When to use
- Quick reference: "what does this page say" —
/fetch <url>beats opening a browser - Before witnessing: peek at content before committing to
/witness(which creates a claim) - Third-party pages: docs sites, blog posts, research articles
- Re-reading: cache hits are ~1ms; great for iterating on content you've already fetched
When NOT to use
- Confluence: use
/pull— structured API is better than HTML scraping - DeepWiki: use
/pull deepwiki— it already has a cleaner path - Authenticated pages: smart-fetch doesn't do auth. Use farmer for approval-gated flows.
- PDFs, images, JSON: smart-fetch rejects non-HTML content types with
unsupported-content-type
Instructions
-
Call
mcp__silo__silo_smart-fetchwith the URL and parsed flags. -
If the response
qualityis "failed" (empty body, SPA, link list, HTTP error), tell the user:- What the reported quality was
- Any warnings returned
- Suggest retrying with
--mode fullor rawWebFetchas a fallback
-
Display the extracted content in a readable format:
- Title, description
- Full content (or first N lines if very long)
- Size reduction metric and cache hit/miss status
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.
- 2d ago First seen · 86 lines · 27 tokens per session scan A 99952447400b
fetch is a skill published in the GitHub repository grainulation/grainulator (87 stars, last pushed 4mo ago), licensed MIT. It adds 27 tokens to every session and 949 once invoked, about $0.0001 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 2026-08-30.
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