agentleFS
grainulationgrainulation/grainulator/skills/fetch/SKILL.md

fetch

Skill86 starsChanged 9 days agoMITView on GitHub

Size-efficient URL fetch with semantic extraction. Use for ad-hoc web research when you want content without the raw HTML overhead.

4 other projects use a version of this.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

7 rules. 0 are common, 0 are distinctive, and 7 are very specific to this project.

  • 0

    Common

    Most of the 4 edited versions we read keep these.

  • 0

    Distinctive

    This author's own choices. Worth a look.

  • 7

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    --mode meta-only: only title + description (smallest)

    line 25
  2. Very specific:

    --privacy: don't write to cache (use for sensitive URLs)

    line 27
  3. Very specific:

    Confluence: use /pull — structured API is better than HTML scraping

    line 38
  4. Very specific:

    DeepWiki repositories: use the available grainulator.deepwiki tool with repo: "owner/repo", or inspect repository code.

    line 39
  5. Very specific:

    If they're corroborating a claim: /witness <claim_id> <url> --smart

    line 58
  6. Very specific:

    If they want to save findings: /research "<topic extracted from content>"

    line 59
  7. Very specific:

    If the content was weak/failed: /fetch <url> --mode full or open in a browser

    line 60

“Common” is measured: kept by at least 6 in 10 of the 4 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

/fetch -- Ad-hoc URL fetch with smart extraction

Pulls a URL's main content (title, description, body paragraphs) without the HTML boilerplate. Delegates to Grainulator’s memory_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; retries full only when extraction failed and was truncated
  • --mode concise: caps body at ~2KB
  • --mode full: returns fuller extraction within fetch/body limits; check truncation
  • --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 repositories: use the available grainulator.deepwiki tool with repo: "owner/repo", or inspect repository code; /pull is for documents/Confluence
  • Authenticated pages: smart-fetch doesn't do auth. Use the host's authenticated browser or connector.
  • PDFs, images, JSON: smart-fetch rejects non-HTML content types with unsupported-content-type

Instructions

  1. Call mcp__grainulator__memory_smart_fetch with the URL and parsed flags: --no-cache maps to cache: false; --privacy maps to privacy: true. Combine both to avoid cache reads and writes.
  2. Inspect status, errors, quality, warnings and truncation. Error responses may have no quality. For degraded/failed/truncated extraction, report the limitation and fetch fuller evidence if the task depends on completeness:
    • What the reported quality was
    • Any warnings returned
    • Suggest retrying with --mode full or raw WebFetch as a fallback
  3. 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
  4. Suggest next actions based on what the user seems to be doing:
    • If they're corroborating a claim: /witness <claim_id> <url> --smart
    • If they want to save findings: /research "<topic extracted from content>"
    • If the content was weak/failed: /fetch <url> --mode full or open in a browser

Example output

URL:        https://example.com/article
Quality:    high  |  Cached: no (first fetch)
Title:      "Understanding Smart Fetch"
Size:       142.1 KB -> 2.3 KB (98% reduction)
Mode used:  concise
Elapsed:    340ms

--- Content ---
[first 2KB of extracted main content]

Auto

- <authorized next action>

Manual

- <action requiring the user, or None.>

Anti-rationalization

RationalizationReality
"Smart-fetch lost content"Check the quality field. If "failed", retry with --mode full. If "degraded", the site may be a SPA — content depends on JS execution.
"I should always use full mode"Full is fine for small pages but wasteful on long docs. auto only retries failed, truncated extraction; it does not guarantee completeness.
"Cached content might be stale"Default TTL is 7 days. Use --no-cache for latest, or grainulator memory cache purge <domain> to drop specific entries.

Host access

Use available grainulator MCP tools, passing the active sprint dir explicitly for evidence operations. If a tool is unavailable, use the local grainulator CLI (or node <checkout>/bin/grainulator.js). Read sibling skill files directly when slash commands are unavailable. Resolve template paths relative to this skill’s checkout when CLAUDE_PLUGIN_ROOT is unset. Optional external connectors are not required for local work; use local code, supplied documents, or available web tools. Do not write managed ledger files directly to bypass a missing MCP connection.

Next-step output

For standalone fetch, setup, or read-only orchestration without an evidence sprint, derive next steps from that task. Do not initialize or compile an unrelated ledger just to produce this footer.

When working in an evidence sprint, use the current compiler's next_actions to present exactly two bullet lists labeled Auto and Manual. Auto is work the agent can continue under existing authorization. Manual is only work requiring the user's decision, access, or action. Classify using the current request and constraints; compiler suggestions never grant permission. Continue authorized Auto work without asking again.

Keep 2–3 useful actions total when available, use short concrete labels and commands where useful, and show None. for an empty group. Do not invent work to fill a quota. Never omit next steps merely because compilation is ready or the answer should be brief. Refresh stale compilation first and exclude work the user removed from scope. When the user asks only for next steps, output only these two lists: no findings recap, counts, reasons, or offer to continue.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.