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 nimadorostkar/Claude-Skills-collection --skill trade-journalgit clone --depth 1 https://github.com/nimadorostkar/Claude-Skills-collectionWrote 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/nimadorostkar/claude-skills-collection/trade-journal)<a href="https://agentmods.dev/skills/nimadorostkar/claude-skills-collection/trade-journal"><img src="https://agentmods.dev/badge/skills/nimadorostkar/claude-skills-collection/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/nimadorostkar/claude-skills-collection/trade-journal"><img src="https://agentmods.dev/badge/skills/nimadorostkar/claude-skills-collection/trade-journal.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00039 | $0.01402 |
| Opus 5 | $0.00019 | $0.00701 |
| Sonnet 5 | $0.00008 | $0.00280 |
| Haiku 4.5 | $0.00004 | $0.00140 |
Grade A, and why
trade-journal 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 9d 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 — 130 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Trade Journal
Purpose
Learn from your own decisions rather than from your outcomes. A profitable trade taken against your rules is a bad trade that will be repeated; a losing trade taken correctly is a good one. Without a journal, these are indistinguishable.
When to Use
- Recording a trade at entry.
- Reviewing performance over a period.
- After a losing streak, to determine whether the process or the market changed.
- Identifying recurring mistakes.
Capabilities
- Structured trade recording: thesis, plan, execution.
- Process-versus-outcome separation.
- Pattern identification across trades.
- Rule-adherence tracking.
- Converting findings into rule changes.
Inputs
- The trades: entry, exit, size, and — crucially — the reasoning at the time.
- The plan as it was stated before the outcome was known.
- Market context.
Outputs
- A record that permits honest review.
- Identified patterns, not anecdotes.
- Specific rule changes with a date.
Workflow
- Record the thesis before the outcome — At entry, in writing: why, where the stop is, where the target is, and what would prove the thesis wrong. Written afterwards, this is a rationalization, and it will be a flattering one.
- Grade the process, not the profit and loss — Did you follow your rules? That is a binary question with a clear answer, and it is the only one you control.
- Categorize by setup and by mistake — Not by outcome. "Chased an extended entry" is a category. "Lost money" is not.
- Review a sample large enough to be meaningful — Twenty trades minimum. Any five trades can be attributed to anything.
- Look for the pattern, not the story — Are the losses concentrated in one setup? One time of day? Trades taken after a loss? These are behavioral patterns and they recur.
- Change one rule, and date it — Then measure whether it helped. Changing five rules at once means learning nothing.
Best Practices
- The most common and most costly journaling error is writing the thesis after the outcome is known. Memory is not merely imperfect; it actively reconstructs the past to justify the present.
- Grade every trade against the rules, independently of whether it made money. A rule violation that was profitable is the most dangerous event in trading, because it is reinforced.
- Look for the revenge trade: the position taken immediately after a loss, larger than the rules permit, in a setup you would normally skip. It is nearly universal and it is visible in the data.
- Track the trades you did not take. A rule that keeps you out of losers is doing its job, and it is invisible without a record.
- Screenshot the chart at entry. Your memory of what the setup looked like will drift toward whatever justifies the outcome.
- A journal you do not review is a diary. The review is the entire point.
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.
- 9d ago First seen · 130 lines · 39 tokens per session scan A d08a50574c23
trade-journal is a skill published in the GitHub repository nimadorostkar/Claude-Skills-collection (26 stars, last pushed 25d ago), licensed MIT. It adds 39 tokens to every session and 1,402 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 2026-09-03.
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