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 mphinance/alpha-skills --skill position-sizergit clone --depth 1 https://github.com/mphinance/alpha-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/mphinance/alpha-skills/position-sizer)<a href="https://agentmods.dev/skills/mphinance/alpha-skills/position-sizer"><img src="https://agentmods.dev/badge/skills/mphinance/alpha-skills/position-sizer/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/mphinance/alpha-skills/position-sizer"><img src="https://agentmods.dev/badge/skills/mphinance/alpha-skills/position-sizer.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 | $0.00058 | $0.01543 |
| Opus 5.5 | $0.00023 | $0.00617 |
| Sonnet 5.5 | $0.00012 | $0.00309 |
| Haiku 4.5 | $0.00006 | $0.00154 |
Grade A, and why
position-sizer 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.
This is a copy
100% identical to position-sizer — 0 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 — 182 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Position Sizer
Overview
Calculate the optimal number of shares to buy for a long stock trade based on risk management principles. Supports three sizing methods:
- Fixed Fractional: Risk a fixed percentage of account equity per trade (default: 1%)
- ATR-Based: Use Average True Range to set volatility-adjusted stop distances
- Kelly Criterion: Calculate mathematically optimal risk allocation from historical win/loss statistics
All methods apply portfolio constraints (max position %, max sector %) and output a final recommended share count with full risk breakdown.
When to Use
- User asks "how many shares should I buy?"
- User wants to calculate position size for a specific trade setup
- User mentions risk per trade, stop-loss sizing, or portfolio allocation
- User asks about Kelly Criterion or ATR-based position sizing
- User wants to check if a position fits within portfolio concentration limits
Prerequisites
- No API keys required
- Python 3.9+ with standard library only
Workflow
Step 1: Gather Trade Parameters
Collect from the user:
- Required: Account size (total equity)
- Mode A (Fixed Fractional): Entry price, stop price, risk percentage (default 1%)
- Mode B (ATR-Based): Entry price, ATR value, ATR multiplier (default 2.0x), risk percentage
- Mode C (Kelly Criterion): Win rate, average win, average loss; optionally entry and stop for share calculation
- Optional constraints: Max position % of account, max sector %, current sector exposure
If the user provides a stock ticker but not specific prices, use available tools to look up the current price and suggest entry/stop levels based on technical analysis.
Step 2: Execute Position Sizer Script
Run the position sizing calculation:
# Fixed Fractional (most common)
python3 skills/position-sizer/scripts/position_sizer.py \
--account-size 100000 \
--entry 155 \
--stop 148.50 \
--risk-pct 1.0 \
--output-dir reports/
# ATR-Based
python3 skills/position-sizer/scripts/position_sizer.py \
--account-size 100000 \
--entry 155 \
--atr 3.20 \
--atr-multiplier 2.0 \
--risk-pct 1.0 \
--output-dir reports/
# Kelly Criterion (budget mode - no entry)
python3 skills/position-sizer/scripts/position_sizer.py \
--account-size 100000 \
--win-rate 0.55 \
--avg-win 2.5 \
--avg-loss 1.0 \
--output-dir reports/
# Kelly Criterion (shares mode - with entry/stop)
python3 skills/position-sizer/scripts/position_sizer.py \
--account-size 100000 \
--entry 155 \
--stop 148.50 \
--win-rate 0.55 \
--avg-win 2.5 \
--avg-loss 1.0 \
--output-dir reports/
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.
- 1mo ago First seen · 182 lines · 58 tokens per session scan A 1fe79c0d268c
position-sizer is a skill published in the GitHub repository mphinance/alpha-skills (28 stars, last pushed 10d ago), licensed MIT. It adds 58 tokens to every session and 1,543 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to position-sizer, differing in 0 lines, and is treated as a copy.
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