pine-optimizer

traderspost/pinescript-agents · updated Apr 8, 2026

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$npx skills add https://github.com/traderspost/pinescript-agents --skill pine-optimizer
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summary

Specialized in enhancing script performance, user experience, and visual presentation on TradingView.

skill.md

Pine Script Optimizer

Specialized in enhancing script performance, user experience, and visual presentation on TradingView.

Core Optimization Areas

Performance Optimization

  • Reduce calculation complexity
  • Minimize security() calls
  • Optimize array operations
  • Cache repeated calculations
  • Reduce compilation size

User Experience Enhancement

  • Intuitive input organization
  • Helpful tooltips and descriptions
  • Smart default values
  • Conditional input visibility
  • User-friendly alerts

Visual Optimization

  • Professional color schemes
  • Adaptive text sizes
  • Clean plot styles
  • Responsive layouts
  • Mobile-friendly displays

Code Efficiency

  • Remove redundant calculations
  • Optimize conditional logic
  • Reduce memory usage
  • Streamline data structures

Performance Optimization Techniques

1. Calculation Caching

// BEFORE - Inefficient
plot(ta.sma(close, 20) > ta.sma(close, 50) ? high : low)
plot(ta.sma(close, 20) > ta.sma(close, 50) ? 1 : 0)

// AFTER - Optimized with caching
sma20 = ta.sma(close, 20)
sma50 = ta.sma(close, 50)
condition = sma20 > sma50
plot(condition ? high : low)
plot(condition ? 1 : 0)

2. Security Call Optimization

// BEFORE - Multiple security calls
htfClose = request.security(syminfo.tickerid, "D", close)
htfHigh = request.security(syminfo.tickerid, "D", high)
htfLow = request.security(syminfo.tickerid, "D", low)

// AFTER - Single security call with tuple
[htfClose, htfHigh, htfLow] = request.security(syminfo.tickerid, "D", [close, high, low])

3. Array Operation Optimization

// BEFORE - Inefficient array operations
var array<float> values = array.new<float>()
for i = 0 to 100
    array.push(values, close[i])

// AFTER - Optimized with built-in functions
var array<float> values = array.new<float>(100)
if barstate.isconfirmed
    array.push(values, close)
    if array.size(values) > 100
        array.shift(values)

4. Conditional Logic Optimization

// BEFORE - Multiple condition checks
signal = close > open and close > close[1] and volume > volume[1] and rsi > 50

// AFTER - Short-circuit evaluation
signal = close > open
signal := signal and close > close[1]
signal := signal and volume > volume[1]
signal := signal and rsi > 50

User Experience Enhancements

1. Organized Input Groups

// Organized inputs with groups and tooltips
// ============================================================================
// INPUTS
// ============================================================================

// Moving Average Settings
maLength = input.int(20, "MA Length", minval=1, maxval=500, group="Moving Average",
                     tooltip="Length of the moving average. Lower values are more responsive.")
maType = input.string("EMA", "MA Type", options=["SMA", "EMA", "WMA", "VWMA"],
                      group="Moving Average",
                      tooltip="Type of moving average to use")

// Signal Settings
signalMode = input.string("Conservative", "Signal Mode",
                          options=["Conservative", "Normal", "Aggressive"],
                          group="Signal Settings",
                          tooltip="Conservative: Fewer, higher quality signals\nNormal: Balanced\nAggressive: More frequent signals")

// Visual Settings
showMA = input.bool(true, "Show MA", group="Visual Settings")
showSignals = input.bool(true, "Show Signals", group="Visual Settings")
showTable = input.bool(true, "Show Info Table", group="Visual Settings")

// Color Settings
bullishColor = input.color(color.green, "Bullish Color", group="Colors")
bearishColor = input.color(color.red, "Bearish Color", group="Colors")
neutralColor = input.color(color.gray, "Neutral Color", group="Colors")

2. Adaptive Color Schemes

// Professional color scheme with transparency
var color BULL_COLOR = color.new(#26a69a, 0)
var color BEAR_COLOR = color.new(#ef5350, 0)
var color BULL_LIGHT = color.new(#26a69a, 80)
var color BEAR_LIGHT = color.new(#ef5350, 80)

// Gradient colors for trends
trendStrength = (close - ta.sma(close, 50)) / ta.sma(close, 50) * 100
gradientColor = color.from_gradient(trendStrength, -2, 2, BEAR_COLOR, BULL_COLOR)

// Dark mode friendly colors
bgColor = color.new(color.black, 95)
textColor = color.new(color.white, 0)

3. Responsive Table Layout

// Auto-sizing table based on content
var table infoTable = table.new(position.top_right, 2, 1, bgcolor=color.new(color.black, 85))

// Dynamic row management
rowCount = 0
if showPrice
    rowCount += 1
if showMA
    rowCount += 1
if showRSI
    rowCount += 1

// Resize table if needed
if rowCount != table.rows(infoTable)
    table.delete(infoTable)
    infoTable := table.new(position.top_right, 2, rowCount, bgcolor=color.new(color.black, 85))

4. Smart Alert Messages

// Detailed alert messages with context
alertMessage = "🔔 " + syminfo.ticker + " Alert\n" + "Price: $" + str.tostring(close, "#,###.##") + "\n" + "Signal: " + (buySignal ? "BUY" : sellSignal ? "SELL" : "NEUTRAL") + "\n" + "Strength: " + str.tostring(signalStrength, "#.#") + "/10\n" + "Volume: " + (volume > ta.sma(volume, 20) ? "Above" : "Below") + " average\n" + "Time: " + str.format_time(time, "yyyy-MM-dd HH:mm")

alertcondition(buySignal or sellSignal, "Trade Signal", alertMessage)

Visual Optimization

1. Professional Plot Styling

// Clean, professional plotting
ma = ta.ema(close, maLength)

// Main plot with gradient fill
maPlot = plot(ma, "MA", color=trendColor, linewidth=2)
fillColor = close > ma ? BULL_LIGHT : BEAR_LIGHT
fill(plot(close, display=display.none), maPlot, fillColor, "MA Fill")

// Signal markers with proper sizing
plotshape(buySignal, "Buy Signal", shape.triangleup, location.belowbar, BULL_COLOR, size=size.small)
plotshape(sellSignal, "Sell Signal", shape.triangledown, location.abovebar, BEAR_COLOR, size=size.small)

2. Adaptive Text Sizing

// Dynamic label sizing based on timeframe
labelSize = timeframe.period == "1" ? size.tiny : timeframe.period == "5" ? size.small : timeframe.period == "15" ? size.small : timeframe.period == "60" ? size.normal : timeframe.period == "D" ? size.large : size.normal

if showLabels and buySignal
    label.new(bar_index, low, "BUY", style=label.style_label_up, color=BULL_COLOR, textcolor=color.white, size=labelSize)

3. Mobile-Friendly Display

// Compact display for mobile devices
compactMode = input.bool(false, "Compact Mode (Mobile)", group="Display",
                         tooltip="Enable for better mobile viewing")

// Adjust plot widths
plotWidth = compactMode ? 1 : 2

// Conditional table display
if not compactMode
    // Show full table
    table.cell(infoTable, 0, 0, "Full Info", text_color=color.white)
else
    // Show essential info only
    table.cell(infoTable, 0, 0, "Signal: " + (buySignal ? "↑" : sellSignal ? "↓" : "−"))

Code Quality Improvements

1. Memory Optimization

// Use var for persistent values
var float prevHigh = na
var int barsSinceSignal = 0
var array<float> prices = array.new<float>(100)

// Clear unused arrays
if array.size(prices) > 100
    array.clear(prices)

2. Error Prevention

// Robust error handling
safeDiv(num, den) => den != 0 ? num / den : 0
safeLookback(src, bars) => bars < bar_index ? src[bars] : src[bar_index]

// NA handling
getValue(src) => na(src) ? 0 : src

3. Compilation Size Reduction

// Use functions to reduce code duplication
plotSignal(cond, loc, col, txt) =>
    if cond
        label.new(bar_index, loc, txt, color=col, textcolor=color.white)

// Reuse styling variables
var commonStyle = label.style_label_center
var commonSize = size.normal

Optimization Checklist

  • Cached all repeated calculations
  • Minimized security() calls
  • Optimized array operations
  • Organized inputs with groups
  • Added helpful tooltips
  • Implemented professional color scheme
  • Optimized plot styles
  • Added mobile-friendly options
  • Reduced memory usage
  • Improved loading time
  • Enhanced visual appeal
  • Simplified user interactions

Balance optimization with readability. Don't over-optimize at the expense of maintainability.

how to use pine-optimizer

How to use pine-optimizer on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add pine-optimizer
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/traderspost/pinescript-agents --skill pine-optimizer

The skills CLI fetches pine-optimizer from GitHub repository traderspost/pinescript-agents and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/pine-optimizer

Reload or restart Cursor to activate pine-optimizer. Access the skill through slash commands (e.g., /pine-optimizer) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

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Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.560 reviews
  • Aditi Martinez· Dec 24, 2024

    We added pine-optimizer from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Omar Malhotra· Dec 20, 2024

    Keeps context tight: pine-optimizer is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Soo Diallo· Dec 20, 2024

    Solid pick for teams standardizing on skills: pine-optimizer is focused, and the summary matches what you get after install.

  • Lucas Desai· Dec 20, 2024

    pine-optimizer reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Mateo Wang· Dec 16, 2024

    pine-optimizer is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Pratham Ware· Dec 12, 2024

    Registry listing for pine-optimizer matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Sophia Thomas· Dec 4, 2024

    I recommend pine-optimizer for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Omar Chawla· Nov 23, 2024

    pine-optimizer fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Isabella Torres· Nov 11, 2024

    pine-optimizer is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Lucas Chen· Nov 11, 2024

    Registry listing for pine-optimizer matched our evaluation — installs cleanly and behaves as described in the markdown.

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