Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add jadatorin/tradingview-opencode-agent --skill chart-analysisgit clone --depth 1 https://github.com/jadatorin/tradingview-opencode-agentWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/jadatorin/tradingview-opencode-agent/chart-analysis)<a href="https://agentmods.dev/skills/jadatorin/tradingview-opencode-agent/chart-analysis"><img src="https://agentmods.dev/badge/skills/jadatorin/tradingview-opencode-agent/chart-analysis/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/jadatorin/tradingview-opencode-agent/chart-analysis"><img src="https://agentmods.dev/badge/skills/jadatorin/tradingview-opencode-agent/chart-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00037 | $0.00590 |
| Opus 5.5 | $0.00015 | $0.00236 |
| Sonnet 5.5 | $0.00007 | $0.00118 |
| Haiku 4.5 | $0.00004 | $0.00059 |
Grade A, and why
chart-analysis scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Chart Analysis Workflow
You are performing technical analysis on a TradingView chart.
Step 1: Set Up the Chart
chart_set_symbol— switch to the requested symbolchart_set_timeframe— set the appropriate timeframe- Wait for the chart to load (the tool handles this)
Step 2: Add Indicators
Use chart_manage_indicator to add studies. Common names (must use FULL names):
- "Relative Strength Index" (not RSI)
- "Moving Average Exponential" (not EMA)
- "Moving Average" (for SMA)
- "MACD"
- "Bollinger Bands"
- "Volume"
- "VWAP"
- "Average True Range"
After adding, use indicator_set_inputs to customize settings (e.g., change EMA length to 200).
Step 3: Navigate to Key Areas
chart_scroll_to_date— jump to a specific date of interestchart_set_visible_range— zoom to a specific date windowchart_get_visible_range— check what's currently visible
Step 4: Annotate
Use drawing tools to mark up the chart:
draw_shapewithhorizontal_linefor support/resistancedraw_shapewithtrend_linefor trend channels (needs two points)draw_shapewithtextfor annotations
Step 5: Capture and Analyze
capture_screenshot— screenshot the annotated chartdata_get_ohlcv— pull recent price data for quantitative analysisquote_get— get the current real-time pricesymbol_info— get symbol metadata (exchange, type, session)
Step 6: Report
Provide the analysis:
- Current price and recent range
- Key support/resistance levels identified
- Indicator readings (RSI overbought/oversold, MACD crossover, etc.)
- Overall bias (bullish/bearish/neutral) with reasoning
Cleanup
If you added indicators the user didn't ask for, remove them:
chart_manage_indicatorwith action "remove" and the entity_iddraw_clearto remove all drawings if they were temporary
Context Management
- Use
summary: trueondata_get_ohlcv - Call
chart_get_stateonce at start to get indicator entity IDs - Screenshot is preferred over large data dumps for visual context
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 69 lines · 0 tokens per session scan A a989af4dd50c
chart-analysis is a skill published in the GitHub repository jadatorin/tradingview-opencode-agent (4 stars, last pushed 2mo ago), licensed MIT. It adds 37 tokens to every session and 590 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
tradovate-patterns
Tradovate JavaScript indicator scaffold and patterns. Provides class structure and export guidance. Use when developing Tradovate indicators.
pine-patterns
Pine Script v5/v6 indicator scaffold and patterns. Provides structure guidance and triggers doc-researcher for current syntax verification. Use when developing TradingView indicators.
tushare
A Python interface for Tushare, a financial data service that provides market and company information for stocks, funds, futures, and digital assets. It returns queried data as pandas tables.
social-media-intelligence
Social media intelligence: financial signal extraction from Twitter/X, Telegram, Discord, and Reddit for sentiment-driven trading strategies.
geopolitical-risk
Geopolitical risk analysis: quantify crisis signals, identify precursors, and build event-driven strategies for war, sanctions, and supply disruption scenarios.
vibe-trading
Professional finance research toolkit — backtesting (11 engines + benchmark comparison panel), factor analysis, Alpha Zoo (478 pre-built alphas across qlib158/alpha101/gtja191/academic/fundamental), options pricing, 90 finance skills, 30 multi-agent swarm teams, Trade Journal analyzer, and Shadow Account (extract →…