Specialized in enhancing script performance, user experience, and visual presentation on TradingView.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionpine-optimizerExecute the skills CLI command in your project's root directory to begin installation:
Fetches pine-optimizer from traderspost/pinescript-agents and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate pine-optimizer. Access via /pine-optimizer in your agent's command palette.
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 environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
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
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
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Specialized in enhancing script performance, user experience, and visual presentation on TradingView.
// 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)
// 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])
// 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)
// 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
// 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")
// 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)
// 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))
// 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)
// 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)
// 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)
// 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 ? "↓" : "−"))
// 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)
// 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
// 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
Balance optimization with readability. Don't over-optimize at the expense of maintainability.
Make data-driven prioritization decisions faster
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
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
ailabs-393/ai-labs-claude-skills
We added pine-optimizer from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Keeps context tight: pine-optimizer is the kind of skill you can hand to a new teammate without a long onboarding doc.
Solid pick for teams standardizing on skills: pine-optimizer is focused, and the summary matches what you get after install.
pine-optimizer reduced setup friction for our internal harness; good balance of opinion and flexibility.
pine-optimizer is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Registry listing for pine-optimizer matched our evaluation — installs cleanly and behaves as described in the markdown.
I recommend pine-optimizer for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
pine-optimizer fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
pine-optimizer is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Registry listing for pine-optimizer matched our evaluation — installs cleanly and behaves as described in the markdown.
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