Borrowing it
Nothing to install: this file belongs to tusharagg1/aifolimizer. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/tusharagg1/aifolimizer/master/.claude/skills/trade-journal/SKILL.mdgit clone --depth 1 https://github.com/tusharagg1/aifolimizerWrote 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/tusharagg1/aifolimizer/trade-journal)<a href="https://agentmods.dev/skills/tusharagg1/aifolimizer/trade-journal"><img src="https://agentmods.dev/badge/skills/tusharagg1/aifolimizer/trade-journal/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/tusharagg1/aifolimizer/trade-journal"><img src="https://agentmods.dev/badge/skills/tusharagg1/aifolimizer/trade-journal.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 | $0.00114 | $0.01503 |
| Opus 5 | $0.00057 | $0.00751 |
| Sonnet 5 | $0.00023 | $0.00301 |
| Haiku 4.5 | $0.00011 | $0.00150 |
Grade B, and why
trade-journal scanned grade B with 1 finding 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 10d 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.
Strips warnings and disclaimersmediumAnti-refusal
Omitting safety caveats hides risk from the user and is a common jailbreak preamble.
**Step 3 - Mirror back one line.** If `emotion` ∈ {fomo, revenge, fear} OR `conviction_source` ∈ {tip, social, gut} OR `pre_trade_check_passed=false`, flag it plainly: "Logged. Note: this is a {emotion}/{source} entry - How it starts
The opening of the file, as written. The whole thing — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Trade Journal (Psychological Layer)
Goal
Most retail losses are emotional, not analytical. decision_memory records the facts of a trade (price, conviction label, thesis) and shadow_account derives biases from price/date. Neither captures what the trader felt at the moment of entry. This skill does - emotion, conviction source, self-rated confidence, plan adherence - then get_journal_insights cross-tabs felt-state against realized outcomes to answer the one question that beats technical analysis: what emotional state precedes my losing trades?
This is a capture + mirror skill, not a recommender. It does not approve or reject trades (that's pre-trade-check).
When to invoke
- ENTRY: user just placed (or is about to place) a trade and wants it journaled. Natural follow-on from pre-trade-check.
- EXIT: user closed a position - reconcile the open journal entry.
- REVIEW: "why do I keep losing", "what's my pattern", "trade journal insights".
How to run
Step 0 - Detect phase (REQUIRED): entry, exit, or review. If ambiguous, ask.
ENTRY phase
Step 1 - Capture felt-state (ask only what the user has not already said):
- Ticker?
- Emotion right now? → one of:
calm | fomo | fear | revenge | conviction | bored | uncertain - Where did the conviction come from? →
thesis | chart | tip | social | news | gut - Confidence 1-5? (1 = a flyer, 5 = highest conviction)
- What is the plan? (entry / stop / exit stated BEFORE the trade - one sentence)
- Did pre-trade-check pass? (yes/no - if they skipped it, note that, it is itself a signal)
Step 2 - Persist:
Call mcp__aifolimizer__log_trade_journal with:
ticker=<TICKER>
emotion=<emotion>
conviction_source=<source>
confidence_1to5=<1-5>
plan_intended=<one-sentence plan>
felt_note=<free text, optional>
pre_trade_check_passed=<true/false>
Step 3 - Mirror back one line. If emotion ∈ {fomo, revenge, fear} OR conviction_source ∈ {tip, social, gut} OR pre_trade_check_passed=false, flag it plainly: "Logged. Note: this is a {emotion}/{source} entry - historically your weakest setup (check insights)." Do not moralize beyond one line.
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.
- 10d ago First seen · 115 lines · 114 tokens per session scan B 11a553b76674
trade-journal is a skill published in the GitHub repository tusharagg1/aifolimizer (2 stars, last pushed 8d ago), licensed MIT. It adds 114 tokens to every session and 1,503 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it B with 1 finding (strips warnings and disclaimers). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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