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Agent skill

Brightdata Performance Tuning

by jeremylongshore in jeremylongshore/tons-of-skills-marketplace

Analyze and tune a Bright Data workload from measured phase latency, failure classes, and bounded experiments instead of undocumented provider assumptions.

MITAuto-check passed

Install Brightdata Performance Tuning

skills CLI
$ npx skills add jeremylongshore/tons-of-skills-marketplace --skill brightdata-performance-tuning -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install jeremylongshore/tons-of-skills-marketplace brightdata-performance-tuning --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/.curated/brightdata-performance-tuning .claude/skills/brightdata-performance-tuning && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
brightdata-performance-tuning
GitHub stars
2.8k
Token cost
~954 tokens
SKILL.md length
359 words
Files
2 (incl. references)
Skills in repo
3,342
Repo updated
First seen
Licence
MIT

At a glance

Analyze and tune a Bright Data workload from measured phase latency, failure classes, and bounded experiments instead of undocumented provider assumptions.

  • Works in 4 steps: Build the baseline → Verify product fit → Run one-variable experiments → …
  • Improving throughput
  • SKILL.md covers Overview, Prerequisites, Instructions and Tool Discipline, plus 4 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Brightdata Performance Tuning is an agent skill from jeremylongshore/tons-of-skills-marketplace. Analyze and tune a Bright Data workload from measured phase latency, failure classes, and bounded experiments instead of undocumented provider assumptions. Use when improving throughput or tail latency. Trigger with: "speed up Bright Data", "tune Browser API performance", "reduce snapshot latency".

Its SKILL.md is about 950 tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including reference files (for example `references/official-docs.md`). Compatibility notes: Requires an approved workload, phase-level telemetry, representative fixtures, and a bounded canary environment

It works with Bright Data. The repository describes itself as: Model-agnostic agent-skills platform with a harness-free canonical layer, verified adapters, and the ccpi package manager. Explore at tonsofskills.com. The licence is MIT.

When your agent uses it

  • Improving throughput
  • With: speed up Bright Data
  • Tune Browser API performance
  • Reduce snapshot latency

Example prompts

  • “speed up Bright Data”
  • “tune Browser API performance”
  • “reduce snapshot latency”
  • “/brightdata-performance-tuning”

Requirements

  • Compatibility (from SKILL.md): Requires an approved workload, phase-level telemetry, representative fixtures, and a bounded canary environment
  • Pre-approved tools (allowed-tools): Read, Grep, Write, Edit

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Build the baseline
  2. Verify product fit
  3. Run one-variable experiments
  4. Decide from evidence

What it can do on your machine

Read from SKILL.md and the folder at commit cfae287. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Grep
    • Write
    • Edit

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

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

  • Network

    Links to these hosts (documentation or services it may open):

    • docs.brightdata.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.

  • Compatibility

    Requires an approved workload, phase-level telemetry, representative fixtures, and a bounded canary environment

    From compatibility in the SKILL.md frontmatter.

Context cost

Brightdata Performance Tuning loads about 954 tokens when it runs, and up to ~1.1k if it reads all its reference files. Until then it costs about 82 tokens; SKILL.md has 359 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~82
When it runs · the whole SKILL.md, loaded when a task matches
~954
With references · SKILL.md plus every file in references/, read only if the agent opens them
~1.1k

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

Auto-check passed

The automated check found no risky patterns in SKILL.md.

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 jeremylongshore/tons-of-skills-marketplace at commit cfae287, republished under its MIT licence (© jeremylongshore). 359 words, ~954 tokens.

Download SKILL.mdSave it as .claude/skills/brightdata-performance-tuning/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
brightdata-performance-tuning
description
Analyze and tune a Bright Data workload from measured phase latency, failure classes, and bounded experiments instead of undocumented provider assumptions. Use when improving throughput or tail latency. Trigger with: "speed up Bright Data", "tune Browser API performance", "reduce snapshot latency".
allowed-tools
Read, Grep, Write, Edit
compatibility
Requires an approved workload, phase-level telemetry, representative fixtures, and a bounded canary environment
version
2.0.0
argument-hint
[trace-or-workload]
model
inherit
effort
high
license
MIT
author
Jeremy Longshore <jeremy@intentsolutions.io>
tags
saas, web-data, bright-data, performance-tuning, operations

Bright Data Evidence-Based Performance Tuning

Overview

Improve the slowest measured phase while preserving authorization, correctness, and cost ceilings. Choose the Bright Data product that matches the interaction, separate provider time from local queue and processing time, change one variable, and retain rollback evidence.

Prerequisites

  • Approved targets and representative request or snapshot fixtures
  • Baseline traces for queue, connect, provider, transfer, parse, and downstream phases
  • Explicit latency, success, concurrency, byte, and cost thresholds

Instructions

Step 1: Build the baseline

Read traces and Grep for serialized work, unbounded concurrency, full-body buffering, repeated browser startup, hot polling, and retry amplification. Segment by product, target class, response size, and error class.

Step 2: Verify product fit

Use proxy requests for simple HTTP collection, Browser API when a browser session is actually required, and asynchronous scraper snapshots for batch workloads. Do not hide a product mismatch with more concurrency.

Step 3: Run one-variable experiments

Write or Edit a canary plan that changes only batch size, worker concurrency, connection reuse, browser-session reuse, polling cadence, streaming boundary, or downstream parallelism. Keep admission, byte, and cost ceilings fixed.

Step 4: Decide from evidence

Compare median and tail latency, success, 429, provider errors, bytes, queue time, and unit cost. Retain changes only when the target metric improves without violating safety, correctness, or budget constraints.

Show full SKILL.md (145 more words)Show less

Tool Discipline

Use Read and Grep for trace and implementation analysis. Use Write and Edit for benchmarks, fixtures, canary configuration, and the decision record. This skill does not generate production load or alter live Bright Data resources.

Output

  • Phase-level baseline and identified bottleneck
  • One-variable experiment matrix with ceilings
  • Keep or rollback decision supported by metrics

Examples

A snapshot workload spends most time parsing after download. Streamed NDJSON parsing lowers memory and tail latency in a fixed-size canary while provider concurrency and collection scope remain unchanged.

Error Handling

FailureMeaningResponse
Baseline mixes unlike productsComparison is invalidSegment proxy, Browser API, and snapshot paths
Throughput rises with more 429 responsesConcurrency exceeds an effective boundaryBack off and lower admission
Faster output loses recordsOptimization broke correctnessRoll back and add integrity assertions

Resources

© jeremylongshore, 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 1 other file (references) in skills/.curated/brightdata-performance-tuning of jeremylongshore/tons-of-skills-marketplace.

  • SKILL.md
  • references/official-docs.md

Open the folder on GitHubat commit cfae287

Compare with similar skills

Brightdata Performance Tuning next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Brightdata Performance Tuning compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Brightdata Performance Tuning this skilljeremylongshore/tons-of-skills-marketplace2.8k—~954Automated safety check: PassMIT
Design Mirrorbrightdata/skills2641 repos~2.1kAutomated safety check: PassMIT
Brightdata Proxybrightdata/skills264—~5.1kAutomated safety check: PassMIT
Bright Data MCPbrightdata/skills2641 repos~3.7kAutomated safety check: PassMIT
Live Researchbrightdata/skills264—~1.8kAutomated safety check: PassMIT
Brightdata SDK JSbrightdata/skills264—~3kAutomated safety check: PassMIT

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Works with

Questions about Brightdata Performance Tuning

What does Brightdata Performance Tuning do?

Analyze and tune a Bright Data workload from measured phase latency, failure classes, and bounded experiments instead of undocumented provider assumptions. Brightdata Performance Tuning is an agent skill from jeremylongshore/tons-of-skills-marketplace. Analyze and tune a Bright Data workload from measured phase latency, failure classes, and bounded experiments instead of undocumented provider assumptions.

When should I use Brightdata Performance Tuning?

Brightdata Performance Tuning fits situations like: improving throughput; with: speed up Bright Data; tune Browser API performance; reduce snapshot latency.

How do I install Brightdata Performance Tuning in Claude Code?

Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill brightdata-performance-tuning -a claude-code`. Or copy the skill folder (skills/.curated/brightdata-performance-tuning in jeremylongshore/tons-of-skills-marketplace) into .claude/skills/brightdata-performance-tuning in your project. Claude Code loads it when a task matches its description.

How do I install Brightdata Performance Tuning in Codex?

Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill brightdata-performance-tuning -a codex`. Or copy the skill folder (skills/.curated/brightdata-performance-tuning in jeremylongshore/tons-of-skills-marketplace) into .agents/skills/brightdata-performance-tuning in your project. Codex loads it when a task matches its description.

Can I use Brightdata Performance Tuning in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill brightdata-performance-tuning -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/brightdata-performance-tuning, .gemini/skills/brightdata-performance-tuning, .github/skills/brightdata-performance-tuning and .opencode/skills/brightdata-performance-tuning in your project.

What does Brightdata Performance Tuning need to run?

SKILL.md names no scripts, command-line tools or credentials: Brightdata Performance Tuning is instructions for the agent only. Its frontmatter pre-approves these tools: Read, Grep, Write, Edit. Compatibility (from SKILL.md): Requires an approved workload, phase-level telemetry, representative fixtures, and a bounded canary environment.

Does Brightdata Performance Tuning access the network?

SKILL.md names 1 domain. As links in the text: docs.brightdata.com. This is read from the text; nothing was executed.

Is Brightdata Performance Tuning safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Brightdata Performance Tuning use?

Brightdata Performance Tuning is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Brightdata Performance Tuning use?

About 954 tokens (SKILL.md is roughly 3.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 190 tokens, read only when the agent opens those files.

What are the alternatives to Brightdata Performance Tuning?

Skills that share tags, products or a category with Brightdata Performance Tuning: Design Mirror (brightdata/skills, 264 stars), Brightdata Proxy (brightdata/skills, 264 stars), Bright Data MCP (brightdata/skills, 264 stars) and Live Research (brightdata/skills, 264 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Brightdata Performance Tuning?

jeremylongshore (a GitHub user) maintains it in jeremylongshore/tons-of-skills-marketplace, which has 2,827 GitHub stars. The repository holds 3,342 skills in this directory. The repository was last updated on October 10, 2026.

Source: jeremylongshore/tons-of-skills-marketplace on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.