Data Feeds

What it does

When your agent uses it

• The user wants clean JSON from a known platform URL rather than raw HTML
• Tasks that involve Web scraping
• Tasks that involve Schema markup

Example prompts

• “/data-feeds”

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

1. 1JSON parses cleanly: jq . returns 0 (or for --format ndjson, each line parses).
2. 2Record count matches expected. One URL usually = one record, but reviews/posts/comments pipelines return arrays sized by what the platform…
3. 3No top-level error
4. 4No per-record error: for array results, ensure no record has an error field
5. 5Core fields present for the pipeline type (examples)
6. 6On failure: double --timeout and retry once. If still failing, bdata pipelines list to confirm the type name hasn't changed.

What it can do on your machine

Read from SKILL.md and the folder at commit 81f51af. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  Shell commands in SKILL.md call:

  • jq

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  Hosts in commands or code, which the agent is likely to contact:

  • amazon.com
  • linkedin.com
  • instagram.com
  • maps.google.com
  • youtube.com

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names no API keys, tokens, secrets or passwords.

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Data Feeds loads about 2.2k tokens when it runs, and up to ~5.1k if it reads all its reference files. Until then it costs about 111 tokens; SKILL.md has 652 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~111

When it runs · the whole SKILL.md, loaded when a task matches

~2.2k

With references · SKILL.md plus every file in references/, read only if the agent opens them

~5.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from brightdata/skills at commit 81f51af, republished under its MIT licence (© brightdata). 652 words, ~2,231 tokens.

name

data-feeds

description

Extract structured data from 40+ supported platforms (Amazon, LinkedIn, Instagram, TikTok, Facebook, YouTube, Reddit, and more) via the Bright Data CLI (`bdata pipelines`). Use when the user wants clean JSON from a known platform URL rather than raw HTML. Hands off to `scrape` for unsupported URLs and to `search` when target URLs must be discovered first. Requires the Bright Data CLI; proactively guides install + login if missing.

if ! command -v bdata >/dev/null 2>&1; then
    echo "bdata CLI not installed — see bright-data-best-practices/references/cli-setup.md"
elif ! bdata zones >/dev/null 2>&1; then
    echo "bdata not authenticated — run: bdata login  (or: bdata login --device for SSH)"
fi

# List available pipeline types (source of truth)
bdata pipelines list

# Amazon product
bdata pipelines amazon_product \
    "https://www.amazon.com/dp/B08N5WRWNW" \
    --format json --pretty -o product.json

# Amazon product reviews (slower — reviews can be hundreds)
bdata pipelines amazon_product_reviews \
    "https://www.amazon.com/dp/B08N5WRWNW" \
    --timeout 1200 -o reviews.json

# Amazon product search (keyword + domain URL)
bdata pipelines amazon_product_search \
    "noise cancelling headphones" "https://www.amazon.com" \
    --format json --pretty -o search.json

# LinkedIn person profile
bdata pipelines linkedin_person_profile \
    "https://www.linkedin.com/in/example" -o person.json

# LinkedIn company
bdata pipelines linkedin_company_profile \
    "https://www.linkedin.com/company/example" -o company.json

# LinkedIn people search (url + first + last name)
bdata pipelines linkedin_people_search \
    "https://www.linkedin.com/company/example" "Jane" "Doe" \
    -o people.json

# Instagram posts
bdata pipelines instagram_posts \
    "https://www.instagram.com/example/" -o posts.json

# Google Maps reviews (url + days_limit, default 3)
bdata pipelines google_maps_reviews \
    "https://maps.google.com/?cid=1234567890" 90 -o reviews.json

# YouTube comments (url + num_comments, default 10)
bdata pipelines youtube_comments \
    "https://www.youtube.com/watch?v=abc123" 100 -o yt-comments.json

# NDJSON for big feeds (one record per line)
bdata pipelines linkedin_posts "https://www.linkedin.com/in/example" \
    --format ndjson -o posts.ndjson

# Raise polling timeout for long jobs
bdata pipelines amazon_product_reviews "<url>" --timeout 1800 -o out.json

© brightdata, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (references) in skills/data-feeds of brightdata/skills.

• SKILL.md
• references/examples.md
• references/flags.md
• references/patterns.md

Open the folder on GitHubat commit 81f51af