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 openclawby/tradingview-mcp --skill strategy-reportgit clone --depth 1 https://github.com/openclawby/tradingview-mcpWrote 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/openclawby/tradingview-mcp/strategy-report)<a href="https://agentmods.dev/skills/openclawby/tradingview-mcp/strategy-report"><img src="https://agentmods.dev/badge/skills/openclawby/tradingview-mcp/strategy-report/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/openclawby/tradingview-mcp/strategy-report"><img src="https://agentmods.dev/badge/skills/openclawby/tradingview-mcp/strategy-report.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 | $0.00030 | $0.00572 |
| Opus 5 | $0.00015 | $0.00286 |
| Sonnet 5 | $0.00006 | $0.00114 |
| Haiku 4.5 | $0.00003 | $0.00057 |
Grade A, and why
strategy-report scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
91% identical to strategy-report — 9 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Strategy Performance Report
You are generating a detailed performance report for a Pine Script strategy on TradingView.
Step 1: Gather Data
Collect all available performance data:
data_get_strategy_results— overall metrics (net profit, win rate, profit factor, etc.)data_get_trades— individual trade list (max 20)data_get_equity— equity curve data pointschart_get_state— current symbol, timeframe, and studies on chartsymbol_info— symbol metadata for context
Step 2: Capture Visuals
capture_screenshotwith region "chart" — the chart with strategy overlaycapture_screenshotwith region "strategy_tester" — the Strategy Tester panel
Step 3: Analyze
Key Metrics
Report these if available:
- Net Profit and % return
- Total Trades and Win Rate
- Profit Factor (target > 1.5)
- Max Drawdown ($ and %)
- Average Trade ($ and %)
- Sharpe Ratio if available
- Max Consecutive Losses
Trade Analysis
From the trade list:
- Largest winner and largest loser
- Average winner vs average loser (reward:risk)
- Long vs short performance breakdown
- Time in market
Equity Curve Assessment
- Is it smooth and upward-sloping?
- Any extended drawdown periods?
- Does it show consistency or was profit front/back-loaded?
Step 4: Generate Report
Format as a structured report:
## Strategy Report: [Strategy Name]
**Symbol:** [symbol] | **Timeframe:** [tf] | **Period:** [date range]
### Summary
[1-2 sentence overview of performance]
### Key Metrics
| Metric | Value |
|--------|-------|
| Net Profit | ... |
| Win Rate | ... |
| Profit Factor | ... |
| Max Drawdown | ... |
### Strengths
- [bullet points]
### Weaknesses
- [bullet points]
### Recommendations
- [specific actionable improvements]
Step 5: Suggest Improvements
Based on the analysis:
- If win rate < 50% but profit factor > 1: suggest tighter entries
- If max drawdown > 20%: suggest position sizing or stop loss adjustments
- If profit factor < 1.2: suggest the strategy may need fundamental changes
- If few trades: suggest widening the lookback or loosening entry criteria
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.
- 9d ago First seen · 84 lines · 30 tokens per session scan A 5935c67cdeb0
strategy-report is a skill published in the GitHub repository openclawby/tradingview-mcp (2 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 30 tokens to every session and 572 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to strategy-report, differing in 9 lines, and is treated as a copy.
Other skills, from other repositories
sector-rotation
An analysis framework for comparing industries in the Chinese A-share stock market, using business conditions, price momentum, valuation, and money flows. It produces rankings and higher- or lower-allocation suggestions.
strategy-pivot-designer
Detect backtest iteration stagnation and generate structurally different strategy pivot proposals when parameter tuning reaches a local optimum.
twitter-reader
Read Twitter/X for financial research using opencli (read-only). Use this skill whenever the user wants to read their Twitter feed, search for financial tweets, view bookmarks, look up user profiles, or gather market sentiment from Twitter/X. Triggers include: "check my feed", "search Twitter for", "show my…
chenhao-limit-up
A framework for judging Chinese A-share stocks that have reached the daily price-rise limit, using market mood, sector leadership, and trading momentum.
trading-risk-gate
Unified pre-trade safety gate: Ruin check (Law #1), ergodicity audit, and win-rate dominance validation. Absorbs: ergodicity-check, law-of-ruin, win-rate-dominance.
vectorbt
High-performance vectorized backtesting with parameter optimization, portfolio simulation, and rich performance metrics.