id: pattern name: Pattern Skill tagline: Pattern description: Detects chart patterns in OHLC data from natural-language hypotheses or visual chart selections. version: 1.0.0 author: Vibe Trade Core category: analysis icon: chart-line color: "#ff6b00"

Tools this skill is allowed to invoke. Each id must exist in the central

tool catalog (skills/tools.py). The frontend enforces this allowlist — a

skill that emits a tool_call for an id not listed here is rejected.

tools:

• chart.pattern_selector
• chart.highlight_matches
• chart.draw_markers
• chart.focus_range
• script_editor.load
• script_editor.run
• bottom_panel.activate_tab
• bottom_panel.set_data
• notify.toast

Bottom panel tabs this skill contributes. component must match a key in

apps/web/src/components/BottomPanel.tsx::BOTTOM_PANEL_COMPONENTS.

output_tabs:

• id: pattern_analysis label: Pattern Analysis component: PatternContent
• id: pine_script label: Pine Script component: PineScriptPanel

Store slots this skill writes to via tool_calls (documentation only).

store_slots:

• patternMatches
• currentScript

Hints for the chat input when this skill is the only one selected.

input_hints: placeholder: "Describe a pattern to detect..." supports_fingerprint: true

Pattern Skill

Purpose

Turn a trader's pattern idea — either written in natural language or drawn directly on the chart — into a runnable JavaScript detection script that scans the active OHLC dataset and returns match regions. Vibe Trade loads the script into the code editor, runs it against the data, and surfaces results in the Pattern Analysis bottom-panel tab.

This skill is the go-to for any "find me X in the price history" question.

When to use this skill

Vibe Trade should dispatch to the Pattern Skill when the user:

• Describes a known technical pattern by name — "bull flag", "double bottom", "head and shoulders", "ascending triangle", "breakout", etc.
• Has just drawn a region on the chart via the pattern selector (a serialized SHAPE: [...] fingerprint arrives as the user's message)
• Asks for a custom indicator — "RSI smoothed with a 20 EMA", "momentum oscillator with z-score normalization", etc.
• Pastes Pine Script code and asks for a JavaScript equivalent

Instructions

1. If the message is a fingerprint (contains SHAPE: and Sliding window):

   • First analyze the shape — what pattern does it resemble, what's the trend, the volatility, the implied direction?
   • Ask the user for confirmation before generating a detection script.
   • The chat router stores the fingerprint as context.pending_fingerprint and waits for a yes/proceed before dispatching again.

2. If the message confirms a pending fingerprint (context.pending_fingerprint is set and the user said yes), generate a shape-matching script:

   • Use Pearson correlation with threshold 0.50 — NOT 0.85+. Strict thresholds silently produce zero matches, which looks like the skill is broken.
   • Fall back to returning the top 5 candidates regardless of threshold.
   • Emit script_editor.load with the generated JS.
   • Emit bottom_panel.activate_tab → pattern_analysis.

3. If the message is a named pattern ("bull flag", "double bottom"):

   • Generate a detection script using price-structure rules, not shape correlation.
   • Use forgiving thresholds (3–5% tolerance, not 1%).
   • Emit the same two tool_calls as above.

4. If the message is an indicator request ("custom RSI...", "ATR-based stop"):

   • Generate an indicator script that returns an array of values (or null for insufficient data).
   • Wrap the result as script_type: "indicator" so the frontend registers it in the Resources dropdown.

5. If the user already has a script loaded and asks for a modification (edit mode — context.pattern_script is non-empty):

   • Return the modified script in full.
   • Do NOT switch the view to Code — the user is iterating and wants the chat feedback inline.

Inputs

Outputs

Returns a SkillResponse with:

• reply — short plain-language explanation of the script
• script — the generated JavaScript source
• script_type — "pattern" / "indicator" / "pine_convert"
• data.parameters — parameter hints extracted from the script
• data.indicators_used — list of indicator helpers referenced
• data.default_params / data.indicator_name — indicator mode only
• tool_calls — see below

Tools used

This skill may emit any of the following tool_calls:

Examples

Natural-language input

"Find bull flags after a 5% rally."

→ Returns a script that scans for a strong uptrend followed by a consolidation in a downward-sloping channel, with 3% tolerance on the flag bounds. Script loads into the editor; bottom panel switches to Pattern Analysis.

Indicator input

"Custom RSI smoothed with a 20-period EMA."

→ Returns an indicator function with default_params = {rsi_period: 14, ema_period: 20}. The frontend registers it in Resources and enables it on the chart.

Fingerprint input (from chart.pattern_selector)

Find this 285-bar pattern (scale-free): SHAPE: [0.02, 0.25, 0.67, ...] Sliding window: 285 bars

→ First pass: analyze the shape, ask for confirmation. → On confirmation: generate a correlation-based detector with threshold 0.50 and fallback-to-top-5 so the user always has something to look at.

Underlying implementation

This skill is wired through core/agents/processors.py::_pattern_processor, which calls core/agents/pattern_agent.py::PatternAgent.generate. The agent owns PATTERN_SYSTEM_PROMPT, INDICATOR_SYSTEM_PROMPT, and PINE_CONVERT_PROMPT.