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 agentmods add skills/algochains/algochains-mcp-server/position-sizenpx skills add AlgoChains/algochains-mcp-server --skill position-sizegit clone --depth 1 https://github.com/AlgoChains/algochains-mcp-serverWrote 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/algochains/algochains-mcp-server/position-size)<a href="https://agentmods.dev/skills/algochains/algochains-mcp-server/position-size"><img src="https://agentmods.dev/badge/skills/algochains/algochains-mcp-server/position-size.svg" alt="Measured on agentmods" 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 | $0.00000 | $0.00478 |
| Opus 5 | $0.00000 | $0.00239 |
| Sonnet 5 | $0.00000 | $0.00096 |
| Haiku 4.5 | $0.00000 | $0.00048 |
Grade A, and why
position-size 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 5d 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
position-size
Tier: 0 (safe, no live money)
Trigger: Pre-trade, on-demand
MCP Tool: run_algoclaw_skill("position-size", {"symbol":"MNQ","entry":18050,"stop":17990,"capital":50000})
What It Does
Computes optimal position size using multiple methods, returns the conservative minimum.
- R-Multiple (Van Tharp):
(capital × risk_pct) / (entry - stop) per tick × tick_value - Volatility-Targeted (Carver/pysystemtrade):
(target_vol × capital) / (realized_vol × notional_per_contract) - Conservative dual: min(R-multiple, vol-targeted)
- IDM check: if symbol is correlated with another active position, apply IDM reduction
Steps
- Call
compute_r_multiple_size(symbol, entry, stop, capital, risk_pct=1.0) - Call
compute_volatility_targeted_size(symbol, current_price, annualized_vol_pct, capital) - Call
compute_idm(instruments=[symbol, ...active_positions...])if >1 active instrument - Return conservative minimum with explanation
Output Format
{
"symbol": "MNQ",
"entry": 18050,
"stop": 17990,
"capital": 50000,
"methods": {
"r_multiple": {
"contracts": 2,
"r_points": 60,
"r_dollars": 120,
"risk_dollars": 500,
"risk_pct": 1.0
},
"vol_targeted": {
"contracts": 2,
"target_vol_pct": 20,
"realized_vol_pct": 18.5
}
},
"idm": {
"applied": false,
"reason": "MNQ is only active instrument"
},
"recommended": 2,
"method_used": "r_multiple (both methods agree)",
"r_multiple_targets": {"1R": 500, "2R": 1000, "3R": 1500}
}
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.
- 5d ago First seen · 54 lines · 0 tokens per session scan A cb11ed56de6e
position-size is a skill published in the GitHub repository AlgoChains/algochains-mcp-server (1 stars, last pushed 4d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 478 tokens. 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.
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