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 Bhala-Srinivash/nse-trading-skills --skill risk-reward-ratiogit clone --depth 1 https://github.com/Bhala-Srinivash/nse-trading-skillsWrote 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/bhala-srinivash/nse-trading-skills/risk-reward-ratio)<a href="https://agentmods.dev/skills/bhala-srinivash/nse-trading-skills/risk-reward-ratio"><img src="https://agentmods.dev/badge/skills/bhala-srinivash/nse-trading-skills/risk-reward-ratio/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/bhala-srinivash/nse-trading-skills/risk-reward-ratio"><img src="https://agentmods.dev/badge/skills/bhala-srinivash/nse-trading-skills/risk-reward-ratio.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 | $0.00104 | $0.00962 |
| Opus 5 | $0.00052 | $0.00481 |
| Sonnet 5 | $0.00021 | $0.00192 |
| Haiku 4.5 | $0.00010 | $0.00096 |
Grade A, and why
risk-reward-ratio 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 12d 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.
How it starts
The opening of the file, as written. The whole thing — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Risk-Reward Ratio
If the math doesn't work, don't take the trade. R:R is the simplest filter that separates good setups from bad ones.
Prerequisites
No dependencies required. Pure math — provide entry, stop, and target prices. No data tools needed.
Calculation
Risk = Entry price - Stop-loss price
Reward = Target price - Entry price
R:R = Reward ÷ Risk
Example:
Entry: Rs.1,800
Stop: Rs.1,700 → Risk = Rs.100 per share
Target: Rs.2,100 → Reward = Rs.300 per share
R:R = 300 ÷ 100 = 3:1
In rupee terms:
Total risk = Risk per share × Number of shares
Total reward = Reward per share × Number of shares
Minimum R:R by Win Rate
Your win rate determines the minimum R:R needed to be profitable over time.
| Win Rate | Min R:R (Breakeven) | Recommended Min | Trades Needed to Recover 1 Loss |
|---|---|---|---|
| 30% | 2.33:1 | 3:1 | ~3 winners |
| 40% | 1.50:1 | 2:1 | ~2 winners |
| 50% | 1.00:1 | 1.5:1 | 1 winner |
| 60% | 0.67:1 | 1:1 | <1 winner |
| 70% | 0.43:1 | 0.75:1 | <1 winner |
If you don't know your win rate, assume 40-50% and require at least 2:1 R:R.
Trade Filtering Rules
| R:R Ratio | Decision |
|---|---|
| Below 1:1 | Skip — you're risking more than you can gain |
| 1:1 to 1.5:1 | Only if win rate > 55% AND high-conviction setup |
| 1.5:1 to 2:1 | Acceptable for experienced traders with edge |
| 2:1 to 3:1 | Good — standard for swing trades |
| 3:1+ | Excellent — take these trades consistently |
Multi-Target R:R
For trades with multiple profit targets (scaling out):
Target 1 (50% of position): Rs.1,900 → R:R = 1:1
Target 2 (30% of position): Rs.2,000 → R:R = 2:1
Target 3 (20% of position): Rs.2,200 → R:R = 4:1
Weighted R:R = (0.5 × 1) + (0.3 × 2) + (0.2 × 4) = 1.9:1
This is useful when you plan to scale out at different levels.
Expected Value
For a more complete picture, calculate expected value per trade:
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.
- 12d ago First seen · 100 lines · 104 tokens per session scan A 8d77a23a1049
risk-reward-ratio is a skill published in the GitHub repository Bhala-Srinivash/nse-trading-skills (38 stars, last pushed 6mo ago), licensed MIT. It adds 104 tokens to every session and 962 once invoked, about $0.0005 per session on Opus 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 2026-08-30.
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