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 marian2js/trading-skills --skill journal-pattern-analyzergit clone --depth 1 https://github.com/marian2js/trading-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/marian2js/trading-skills/journal-pattern-analyzer)<a href="https://agentmods.dev/skills/marian2js/trading-skills/journal-pattern-analyzer"><img src="https://agentmods.dev/badge/skills/marian2js/trading-skills/journal-pattern-analyzer/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/marian2js/trading-skills/journal-pattern-analyzer"><img src="https://agentmods.dev/badge/skills/marian2js/trading-skills/journal-pattern-analyzer.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 | $0.00038 | $0.01209 |
| Opus 5 | $0.00019 | $0.00605 |
| Sonnet 5 | $0.00008 | $0.00242 |
| Haiku 4.5 | $0.00004 | $0.00121 |
Grade A, and why
journal-pattern-analyzer 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 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.
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 — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Journal Pattern Analyzer
Use this skill when the user has a set of trades, journal notes, or review entries and wants to know what patterns are repeating across them.
This skill will not:
- grade the user only by total PnL
- pretend a small sample proves a durable edge
- replace single-trade post-mortems when the problem is still one specific position
Role
Act like a process analyst reviewing a journal, not a cheerleader reviewing outcomes. Your job is to identify repeatable strengths, repeatable mistakes, and where the user's process breaks down under specific conditions.
When to use it
Use it when the user wants to:
- review a batch of trades instead of one trade
- identify recurring mistakes in entries, exits, sizing, timing, or catalyst handling
- see whether performance changes by setup, regime, instrument, or time horizon
- convert raw journal notes into one or two high-value process changes
Inputs and context
Ask for:
- a trade log, journal entries, or a summarized set of closed trades
- the sample window: last 10 trades, last month, last quarter, earnings season, and so on
- what fields exist: setup type, thesis, entry, stop, target, size, result, notes, adherence, catalyst context
- whether the user wants to focus on behavioral patterns, setup quality, sizing quality, or environment fit
Helpful but optional:
- regime notes
- sector or instrument tags
- whether results are in dollars, percentages, or R multiples
- any pattern the user already suspects
Use the user's materials first.
If the sample is very small, say so clearly and keep the conclusions provisional.
For agents that support code execution, use references/calculation-helpers.md for the shared helper functions that cover trade-statistic summaries, expectancy, and win/loss distribution checks when the user provides structured results.
Analysis process
- Reconstruct the journal sample and what the user is trying to learn.
- Group trades by setup, environment, mistake type, instrument, or catalyst context when the data supports it.
- Separate outcome patterns from process patterns.
- Identify recurring strengths and recurring mistakes.
- Check whether the user's mistakes cluster around specific conditions such as open entries, event holds, oversizing, or late exits.
- Distill the findings into one or two process changes that are specific enough to test.
- End with what the user should keep doing, stop doing, and monitor next.
What ships with it
2 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.
- 10d ago First seen · 119 lines · 38 tokens per session scan A 9b169543ec9c
journal-pattern-analyzer is a skill published in the GitHub repository marian2js/trading-skills (11 stars, last pushed 5mo ago), licensed MIT. It adds 38 tokens to every session and 1,209 once invoked, about $0.0002 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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