Getting it into your agent
There is no command for this one: it runs only inside a plugin, and the catalogue could not identify which plugin ships it. The source is linked below.
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.
[](https://agentmods.dev/skills/jui-hung-yuan/smarthome-mcp-lab/trading-journal)<a href="https://agentmods.dev/skills/jui-hung-yuan/smarthome-mcp-lab/trading-journal"><img src="https://agentmods.dev/badge/skills/jui-hung-yuan/smarthome-mcp-lab/trading-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/jui-hung-yuan/smarthome-mcp-lab/trading-journal"><img src="https://agentmods.dev/badge/skills/jui-hung-yuan/smarthome-mcp-lab/trading-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 | $0.00103 | $0.00998 |
| Opus 5 | $0.00051 | $0.00499 |
| Sonnet 5 | $0.00021 | $0.00200 |
| Haiku 4.5 | $0.00010 | $0.00100 |
Grade A, and why
trade-republic-analytics 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.
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.
Trade Republic Analytics
Jui's trading journal pipeline lives at:
/Users/jui-hungyuan/development/playground/agents/trading-journal-analytics
What this skill does
Given an uploaded Trade Republic transaction CSV (or a path to one), the pipeline:
- Saves the CSV into
data/raw/with the correct filename pattern - Runs
prepare_positions.py→ producesdata/processed/closed_positions.csvandopen_positions.csv - Runs
compute_metrics.py→ producesdata/processed/metrics.json - Summarises the key metrics for the user
Step-by-step
1. Locate the uploaded CSV
The uploaded file will be under the session's uploads directory (e.g. /sessions/.../mnt/uploads/).
Read it to confirm it looks like a TR export (should have columns like date, type, ISIN, shares, amount or similar).
2. Name and copy the file
TR raw files follow this naming convention:
YYYYMMDD_TR_transactions.csv
- If the uploaded filename already matches this pattern, keep the date from it.
- Otherwise, use today's date:
$(date +%Y%m%d). - Copy (do NOT rename the original) to:
/Users/jui-hungyuan/development/playground/agents/trading-journal-analytics/data/raw/YYYYMMDD_TR_transactions.csv
Use cp via Bash — don't read and re-write the CSV manually (preserves exact bytes).
3. Run the pipeline
Always run both scripts in order from the project root:
cd /Users/jui-hungyuan/development/playground/agents/trading-journal-analytics
uv run python src/prepare_positions.py
uv run python src/compute_metrics.py
Capture stdout/stderr for each. If prepare_positions.py fails, do NOT proceed to compute_metrics.py — report the error.
4. Read and summarise results
After the pipeline finishes, read data/processed/metrics.json and present a clean summary.
Always include in the summary:
- Total realised P&L (EUR)
- Win rate (% of closed trades that were profitable)
- Number of closed positions and open positions
- Best and worst single trade
- Any open positions currently held (ticker, avg entry, current P&L if available)
Format:
Use a clean table or structured list — not raw JSON. Round EUR amounts to 2 decimal places.
If
metrics.jsonhas fields you don't recognise, include them under "Other metrics" rather than silently dropping them.
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.
- 12d ago First seen · 89 lines · 103 tokens per session scan A bd332baa32e8
trade-republic-analytics is a skill published in the GitHub repository jui-hung-yuan/smarthome-mcp-lab (0 stars, last pushed 1mo ago), licensed MIT. It adds 103 tokens to every session and 998 once invoked, about $0.0005 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 2026-08-31.
Other skills, from other repositories
sector-rotation
An analysis framework for comparing industries in the Chinese A-share stock market, using business conditions, price momentum, valuation, and money flows. It produces rankings and higher- or lower-allocation suggestions.
strategy-pivot-designer
Detect backtest iteration stagnation and generate structurally different strategy pivot proposals when parameter tuning reaches a local optimum.
twitter-reader
Read Twitter/X for financial research using opencli (read-only). Use this skill whenever the user wants to read their Twitter feed, search for financial tweets, view bookmarks, look up user profiles, or gather market sentiment from Twitter/X. Triggers include: "check my feed", "search Twitter for", "show my…
chenhao-limit-up
A framework for judging Chinese A-share stocks that have reached the daily price-rise limit, using market mood, sector leadership, and trading momentum.
furusato
A Japanese hometown-tax donation manager for furusato nozei, a system where donations to municipalities can qualify for an income-tax or local-tax deduction. It reads donation receipts, stores donation records, and calculates deduction limits.
reading-receipt
An image-reading workflow for extracting structured information from receipts, invoices, and hometown-tax donation certificates. It can first extract text from PDFs and otherwise read their images.