agentleFS
Mathews-TomMathews-Tom/armory/skills/web-fetch/SKILL.md

web-fetch

Skill340 starsChanged 8 months agoMITView on GitHub

Web content fetching via curl and WebFetch when a specific URL is provided. Covers HTTP GET/POST, JSON APIs, HTML, auth, cookies. Triggers on: "fetch this URL", "download HTML", "call this API", "curl this endpoint". NOT for search, use tavily.

  • Reads credentials
  • Sends data out

12 other projects use a version of this.

Adopted by

Most recently taken up by

Used with

Projects with it also use skill-creator.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • SentrySkill

    skill-creator

    Create new agent skills following the Agent Skills specification. Use when asked to "create a skill", "add a new skill", "write a skill", "make a skill", "build a skill", or scaffold a new skill with SKILL.md. Guides through requirements, writing, registration, and verification.

    Also in 4 projects that use this

The file

We split this skill into the rules it gives an agent

7 rules. 0 are common, 1 is distinctive, and 6 are very specific to this project.

  • 0

    Common

    Most of the 9 edited versions we read keep these.

  • 1

    Distinctive

    This author's own choices. Worth a look.

  • 6

    Very specific

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

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

    All web content retrieval uses curl (Bash) or the built-in WebFetch tool.

    line 13
  2. Very specific:

    Default choice: Use WebFetch for general page content.

    line 26
  3. Distinctive:

    Verify a URL is reachable before complex operations:

    line 282
  4. Very specific:

    WebFetch: does not support custom headers, POST bodies, or cookies.

    line 292
  5. Very specific:

    curl: does not render JavaScript.

    line 294
  6. Very specific:

    Never hardcode credentials: Use $ENV_VAR references. If the variable is not set, surface the error immediately.

    line 310
  7. Very specific:

    Follow redirects by default: Always use -L with curl unless you specifically need to inspect the redirect chain.

    line 312

“Common” is measured: kept by at least 6 in 10 of the 9 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

Web Fetch

All web content retrieval uses curl (Bash) or the built-in WebFetch tool. No MCP server needed — Claude Code's native tools cover every Fetch MCP operation with more control.

Quick Reference

Fetch MCP ToolReplacementWhen to Use
fetch_htmlcurl -s URLRaw HTML needed for parsing
fetch_jsoncurl -s URL | jq '.'API responses, structured data
fetch_markdownWebFetchReadable page content (default output is markdown)
fetch_txtcurl -s URL or WebFetchPlain text extraction

Default choice: Use WebFetch for general page content. Use curl when you need headers, authentication, POST bodies, or raw format control.


WebFetch (Built-in Tool)

The WebFetch tool fetches a URL and returns clean markdown content. It handles JavaScript-rendered pages, strips navigation and boilerplate, and returns readable text.

Best for: documentation pages, articles, blog posts, README files — any content where you want readable text rather than raw HTML.

Limitations: no custom headers, no POST bodies, no cookie management. Use curl for those.


curl Patterns

Fetch HTML

curl -sL "https://example.com/page"
FlagPurpose
-sSilent mode — suppress progress meter
-LFollow redirects (3xx)
-o file.htmlSave to file instead of stdout
-IHeaders only (HEAD request)
-iInclude response headers in output

Fetch and extract specific elements with xmllint or python3:

curl -sL "https://example.com" | python3 -c "
from html.parser import HTMLParser
import sys

class TitleParser(HTMLParser):
    def __init__(self):
        super().__init__()
        self.in_title = False
        self.title = ''
    def handle_starttag(self, tag, attrs):
        self.in_title = tag == 'title'
    def handle_data(self, data):
        if self.in_title:
            self.title += data
    def handle_endtag(self, tag):
        if tag == 'title':
            self.in_title = False

p = TitleParser()
p.feed(sys.stdin.read())
print(p.title)
"

Fetch JSON

curl -s "https://api.example.com/v1/data" \
  -H "Accept: application/json" | jq '.'

Filter and reshape JSON responses:

# Extract specific fields
curl -s "https://api.example.com/users" | jq '.[] | {name, email}'

# Filter by condition
curl -s "https://api.example.com/items" | jq '[.[] | select(.status == "active")]'

# Count results
curl -s "https://api.example.com/items" | jq 'length'

# Get nested value
curl -s "https://api.example.com/config" | jq '.database.host'

Fetch Plain Text

# Strip HTML tags for plain text
curl -sL "https://example.com/page" | python3 -c "
import html.parser, sys

class Stripper(html.parser.HTMLParser):
    def __init__(self):
        super().__init__()
        self.text = []
    def handle_data(self, d):
        self.text.append(d)
    def get_text(self):
        return ''.join(self.text)

s = Stripper()
s.feed(sys.stdin.read())
print(s.get_text())
"

Or use WebFetch which returns clean markdown — close enough to plain text for most purposes.


Authenticated Requests

Bearer Token

curl -s "https://api.example.com/data" \
  -H "Authorization: Bearer $API_TOKEN" \
  -H "Content-Type: application/json"

API Key in Header

curl -s "https://api.example.com/data" \
  -H "X-API-Key: $API_KEY"

API Key in Query Parameter

curl -s "https://api.example.com/data?api_key=$API_KEY"

Basic Auth

curl -s -u "username:$PASSWORD" "https://api.example.com/data"

Store credentials in environment variables. Never hardcode tokens or passwords in commands.


POST, PUT, PATCH, DELETE

POST with JSON Body

curl -s -X POST "https://api.example.com/items" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $API_TOKEN" \
  -d '{
    "name": "item-name",
    "value": 42
  }' | jq '.'

POST with Form Data

curl -s -X POST "https://api.example.com/upload" \
  -F "file=@./document.pdf" \
  -F "description=Uploaded via curl"

PUT (Full Update)

curl -s -X PUT "https://api.example.com/items/123" \
  -H "Content-Type: application/json" \
  -d '{"name": "updated-name", "value": 99}' | jq '.'

PATCH (Partial Update)

curl -s -X PATCH "https://api.example.com/items/123" \
  -H "Content-Type: application/json" \
  -d '{"value": 100}' | jq '.'

DELETE

curl -s -X DELETE "https://api.example.com/items/123" \
  -H "Authorization: Bearer $API_TOKEN"

Advanced Patterns

Pagination

PAGE=1
while true; do
  RESPONSE=$(curl -s "https://api.example.com/items?page=$PAGE&per_page=50" \
    -H "Authorization: Bearer $API_TOKEN")
  COUNT=$(echo "$RESPONSE" | jq 'length')
  echo "$RESPONSE" | jq '.[]'
  [ "$COUNT" -lt 50 ] && break
  PAGE=$((PAGE + 1))
done

Timeout and Retry

curl -s --connect-timeout 10 --max-time 30 \
  --retry 3 --retry-delay 2 \
  "https://api.example.com/data"

Response Headers Inspection

curl -sI "https://example.com" | grep -i "content-type"

Save Response with Status Code

HTTP_CODE=$(curl -s -o /tmp/response.json -w "%{http_code}" "https://api.example.com/data")
echo "Status: $HTTP_CODE"
cat /tmp/response.json | jq '.'

Cookie Handling

# Save cookies
curl -s -c /tmp/cookies.txt "https://example.com/login" \
  -d "user=admin&pass=$PASSWORD"

# Reuse cookies
curl -s -b /tmp/cookies.txt "https://example.com/dashboard"

Error Handling

HTTP StatusMeaningResolution
301/302RedirectAdd -L flag to follow
401UnauthorizedCheck token/credentials; verify env var is set
403ForbiddenInsufficient permissions or IP restriction
404Not FoundVerify URL path; resource may be deleted
429Rate LimitedRespect Retry-After header; add delay between requests
500Server ErrorRetry once; if persistent, report upstream
SSL errorCertificate issueDo not use -k (insecure) — fix the root cause
TimeoutNetwork/server slowIncrease --max-time; check connectivity

Verify a URL is reachable before complex operations:

curl -sI -o /dev/null -w "%{http_code}" "https://example.com"

Limitations

  • WebFetch does not support custom headers, POST bodies, or cookies. Use curl for authenticated or stateful requests.
  • curl does not render JavaScript. For JS-heavy SPAs, prefer WebFetch which handles rendered content.
  • Large responses may exceed context limits. Pipe through jq, head, or python3 to extract only needed data before loading into context.
  • Binary content (images, PDFs, archives) should be saved to disk with -o, not piped to stdout.

Calibration Rules

  1. Default to WebFetch for reading web pages. It returns clean markdown, handles JS rendering, and requires no flags. Switch to curl only when you need headers, auth, POST, or raw format control.
  2. Always pipe JSON through jq. Raw JSON in context wastes tokens. Filter to only the fields needed.
  3. Never hardcode credentials. Use $ENV_VAR references. If the variable is not set, surface the error immediately.
  4. Follow redirects by default. Always use -L with curl unless you specifically need to inspect the redirect chain.
  5. Prefer -s (silent) on every curl call. Progress meters add noise to output.

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.