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.
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.
git clone --depth 1 https://github.com/mnemox-ai/tradememory-protocolWrote 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/commands/mnemox-ai/tradememory-protocol/daily-review)<a href="https://agentmods.dev/commands/mnemox-ai/tradememory-protocol/daily-review"><img src="https://agentmods.dev/badge/commands/mnemox-ai/tradememory-protocol/daily-review/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/commands/mnemox-ai/tradememory-protocol/daily-review"><img src="https://agentmods.dev/badge/commands/mnemox-ai/tradememory-protocol/daily-review.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 | $0.00010 | $0.00590 |
| Opus 5 | $0.00005 | $0.00295 |
| Sonnet 5 | $0.00002 | $0.00118 |
| Haiku 4.5 | $0.00001 | $0.00059 |
Grade A, and why
daily-review 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 11d 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 — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Daily Review
Trigger an AI-powered reflection on recent trading activity. Analyzes trades, identifies behavioral patterns, updates affective state, and produces actionable insights.
Workflow
Step 1: Determine Review Period
If a date is provided, review that day's trades. Default: review all trades since last reflection.
Step 2: Gather Data
Load the trading-memory skill context, then:
- Use
get_strategy_performanceto pull recent trade stats - Use
get_behavioral_analysisto check behavioral drift - Use
get_agent_stateto read current affective state - Use
recall_memorieswith recent context to surface relevant patterns
Step 3: Analyze Patterns
The reflection should cover:
Trade Execution Quality
- Did entries match the strategy rules? Or were they impulsive?
- Were stop losses honored? Any manual overrides?
- Position sizing: consistent with risk rules or erratic?
Behavioral Drift Detection
- Disposition effect: cutting winners, holding losers?
- Revenge trading: increased size after losses?
- Overtrading: more trades than the strategy signals justify?
- Session discipline: trading outside designated sessions?
Strategy Performance
- Which strategies fired today?
- Win/loss breakdown per strategy
- Any strategy consistently underperforming?
Step 4: Update Affective State
Based on the review, the affective state should be recalibrated:
- Confidence: up after good execution, down after poor discipline
- Risk appetite: reduce after drawdown, normalize after recovery
- Streak awareness: flag tilt risk after consecutive losses
Step 5: Produce Report
Structure:
## Daily Review — [Date]
### Summary
- Trades today: N (W wins, L losses)
- P&L: $XXX
- Best: [trade details]
- Worst: [trade details]
### Behavioral Check
- Disposition ratio: X.X (target < 1.0)
- Hold time balance: [OK / Winners cut short / Losers held too long]
- Position sizing: [Consistent / Erratic]
### Insights
1. [Specific, data-backed observation]
2. [Specific, data-backed observation]
### Tomorrow's Focus
- [One concrete action item based on today's data]
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.
- 11d ago First seen · 89 lines · 10 tokens per session scan A 708519a06691
daily-review is a command published in the GitHub repository mnemox-ai/tradememory-protocol (1,416 stars, last pushed 2d ago), licensed MIT. It adds 10 tokens to every session and 590 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.
Other commands, from other repositories
tax-sanity
Tax awareness sanity-check (NOT tax advice). Walks through entity tax-efficiency, deductions, growth structure, quarterly estimateds, multi-jurisdiction, retirement/long-term questions. Produces a Tax Readiness Checklist for the person's meeting with their accountant.
memory-budget
Define or review memory budgets, ownership, and key memory risks.
alpha
Scan for alpha opportunities and spawn parallel research agents.
positions
Review all open positions with parallel subagents. Decides HOLD or SELL for each position.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.