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 agiprolabs/claude-trading-skills --skill exit-strategiesgit clone --depth 1 https://github.com/agiprolabs/claude-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/agiprolabs/claude-trading-skills/exit-strategies)<a href="https://agentmods.dev/skills/agiprolabs/claude-trading-skills/exit-strategies"><img src="https://agentmods.dev/badge/skills/agiprolabs/claude-trading-skills/exit-strategies/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/agiprolabs/claude-trading-skills/exit-strategies"><img src="https://agentmods.dev/badge/skills/agiprolabs/claude-trading-skills/exit-strategies.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector pass
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 | $0.00025 | $0.02483 |
| Opus 5 | $0.00013 | $0.01241 |
| Sonnet 5 | $0.00005 | $0.00497 |
| Haiku 4.5 | $0.00003 | $0.00248 |
Grade A, and why
exit-strategies 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 13d 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 — 297 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Exit Strategies
Entries are easy, exits are everything. A mediocre entry with a disciplined exit will outperform a perfect entry with no exit plan. This skill covers systematic, rule-based exit methods for crypto and Solana token trading.
Why Exits Matter
- Entries determine if you participate. Exits determine how much you keep.
- Most traders spend 90% of effort on entries and 10% on exits — invert this.
- Without defined exits you rely on emotion, which guarantees inconsistency.
- Every trade should have three exits defined before entry: stop loss, take profit, and trailing stop.
Exit Categories
1. Stop Loss — Risk Management Exits
Predefined price level where you close the position to cap downside.
| Method | Description | Best For |
|---|---|---|
| Fixed percentage | Exit at entry − X% | Simple setups, beginners |
| ATR-based | Entry − ATR(14) × multiplier | Volatility-adaptive |
| Support level | Below nearest swing low | Technically defined risk |
| Maximum loss | Absolute SOL/USD cap | Account protection |
ATR-based stop (recommended default):
import pandas_ta as ta
atr = df.ta.atr(length=14)
stop_loss = entry_price - (atr.iloc[-1] * 2.0) # 2x ATR below entry
Multiplier guide:
- 1.5× — Tight. High win rate needed. Good for scalps.
- 2.0× — Standard. Balances noise filtering with risk.
- 3.0× — Wide. For swing trades in volatile conditions.
See references/stop_loss_methods.md for complete methodology.
2. Take Profit — Target Exits
Predefined levels where you lock in gains.
Fixed risk/reward targets:
risk = entry_price - stop_loss_price
tp_2r = entry_price + (risk * 2) # 2:1 R:R
tp_3r = entry_price + (risk * 3) # 3:1 R:R
tp_5r = entry_price + (risk * 5) # 5:1 R:R
Scaled exit framework (recommended for meme/PumpFun tokens):
| Tranche | Size | Target | Action After |
|---|---|---|---|
| 1 | 25% | 2× risk | Move stop to breakeven |
| 2 | 25% | 3–5× risk | Trail remainder |
| 3 | 25% | 5–10× risk | Tighten trail |
| 4 | 25% | Trailing stop | Moonbag — let it ride |
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 13d ago First seen · 297 lines · 25 tokens per session scan A 1c35d488e9d9
exit-strategies is a skill published in the GitHub repository agiprolabs/claude-trading-skills (356 stars, last pushed 9d ago), licensed MIT. It adds 25 tokens to every session and 2,483 once invoked, about $0.0001 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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