risk-management

risk-management is a skill for Claude Code from mnemox-ai/tradememory-protocol. It costs 89 tokens per session (1,509 once invoked), scanned A, original, MIT.

A set of trading risk rules that considers both the money exposed in a position and the trader's changing behavior. It tracks factors such as drawdown, winning or losing streaks, confidence, and risk appetite.

In plain words
What is it for?
Use it to check risk before trades, adjust position sizing, manage drawdowns, detect tilt, and add behavioral guardrails to trading agents.
Why use it?
It helps detect when losses or emotional drift may be causing decisions to depart from the trader's normal rules.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the tradememory plugin — 3 skills, 5 commands, 1 MCP server shipped together

Good fit Use it to check risk before trades, adjust position sizing, manage drawdowns, detect tilt, and add behavioral guardrails to trading agents.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/mnemox-ai/tradememory-protocol/risk-management
About the project

TradeMemory is a memory and audit layer for AI trading agents that records trading decisions, outcomes, and context in a tamper-evident history. It is for traders and automated trading systems that need agents to recall past decisions and document their reasoning. Catalogue add-ons provide skills, commands, MCP tools, and related workflow components for using it.

mnemox-ai/tradememory-protocol · 1,417 stars · on GitHub · mnemox.ai

Install

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.

Any agent
npx skills add mnemox-ai/tradememory-protocol --skill risk-management
Clone the repo
git clone --depth 1 https://github.com/mnemox-ai/tradememory-protocol

Made for: Claude Code.

Or install tradememory, the plugin that ships this one along with the rest of its 3 skills, 5 commands, 1 MCP server.

Wrote 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.

agentmods badge for risk-management

README.md
[![agentmods](https://agentmods.dev/badge/skills/mnemox-ai/tradememory-protocol/risk-management/github.svg)](https://agentmods.dev/skills/mnemox-ai/tradememory-protocol/risk-management)
Your own site
<a href="https://agentmods.dev/skills/mnemox-ai/tradememory-protocol/risk-management"><img src="https://agentmods.dev/badge/skills/mnemox-ai/tradememory-protocol/risk-management/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.

agentmods 80×15 button for risk-management

Your own site · 80×15
<a href="https://agentmods.dev/skills/mnemox-ai/tradememory-protocol/risk-management"><img src="https://agentmods.dev/badge/skills/mnemox-ai/tradememory-protocol/risk-management.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 89 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,509 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00089 $0.01509
Opus 5 $0.00044 $0.00754
Sonnet 5 $0.00018 $0.00302
Haiku 4.5 $0.00009 $0.00151

Measured 12d ago against content hash a73ad4852853, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

risk-management 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.

tradememory-plugin/skills/risk-management/SKILL.md · 145 lines

How it starts

The opening of the file, as written. The whole thing — 145 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Risk Management

Overview

Risk management in TradeMemory is behavioral, not just mathematical. Traditional risk management calculates position sizes and stop losses. TradeMemory adds a behavioral layer: it monitors your execution patterns, detects emotional drift, and flags when you're deviating from your own rules.

The system tracks two kinds of risk:

  1. Position risk — How much capital is at stake on each trade
  2. Behavioral risk — Are you making decisions rationally or emotionally

Affective State Model

TradeMemory maintains a real-time emotional state model for the trading agent:

Dimension Range What It Tracks
Confidence 0.0 - 1.0 Self-assessed confidence, calibrated against outcomes
Drawdown 0% - 100% Current peak-to-trough equity drawdown
Win Streak 0 - N Consecutive winning trades
Loss Streak 0 - N Consecutive losing trades
Risk Appetite low / normal / high Derived from confidence + drawdown + streaks

How Affective State Updates

  • After a win: Confidence += f(P&L magnitude), win streak ++, loss streak reset
  • After a loss: Confidence -= f(P&L magnitude), loss streak ++, win streak reset
  • Drawdown crossing thresholds: Risk appetite auto-reduces at 5%, 10%, 15% drawdown
  • Daily review: Confidence recalibrated against actual hit rate

Using Affective State

Check get_agent_state before every trading session:

get_agent_state() → {
  confidence: 0.42,
  drawdown: 8.3%,
  win_streak: 0,
  loss_streak: 3,
  risk_appetite: "low"
}

Action rules:

  • risk_appetite == "low" → Reduce position size by 50% or skip marginal setups
  • loss_streak >= 3 → Stop trading for the session. Review, don't revenge trade.
  • confidence < 0.3 → Paper trade only until confidence recovers
  • drawdown > 15% → Hard stop. No new positions until daily review.

Behavioral Risk Indicators

1. Disposition Effect

What: Cutting winners short and holding losers too long. Detection: get_behavioral_analysisdisposition_ratio

  • Ratio < 1.0 = Good (holding winners longer than losers)
  • Ratio > 1.5 = Problem (losers held 50% longer than winners)
  • Ratio > 2.0 = Critical (classic retail trader failure mode)

Read the full file on GitHub · 145 lines

Changes

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.

  1. 12d ago First seen · 145 lines · 89 tokens per session scan A a73ad4852853

Subscribe to this mod's changes

risk-management is a skill published in the GitHub repository mnemox-ai/tradememory-protocol (1,417 stars, last pushed 3d ago), licensed MIT. It adds 89 tokens to every session and 1,509 once invoked, about $0.0004 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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