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 skloxo/TideTrading --skill strategy-generategit clone --depth 1 https://github.com/skloxo/TideTradingWrote 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/skloxo/tidetrading/strategy-generate)<a href="https://agentmods.dev/skills/skloxo/tidetrading/strategy-generate"><img src="https://agentmods.dev/badge/skills/skloxo/tidetrading/strategy-generate/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/skloxo/tidetrading/strategy-generate"><img src="https://agentmods.dev/badge/skills/skloxo/tidetrading/strategy-generate.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 | $0.00021 | $0.03108 |
| Opus 5 | $0.00010 | $0.01554 |
| Sonnet 5 | $0.00004 | $0.00622 |
| Haiku 4.5 | $0.00002 | $0.00311 |
Grade A, and why
strategy-generate 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 5d 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 — 203 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Workflow
- Requirements parsing: parse user intent, extract instrument codes, time range, and strategy logic, then write
config.json - Strategy design: think through the 5 questions of data / signal / position sizing / backtest / validation
- Strategy coding: write
code/signal_engine.py(following theSignalEnginecontract) - Syntax check:
bash("python -c \"import ast; ast.parse(open('code/signal_engine.py').read()); print('OK')\"") - Run backtest: call the
backtesttool (built into the engine; no need to writerun_backtest.py) - Evaluate results: read
artifacts/metrics.csvand judge by the review criteria - Iterative fixing: if results are poor, modify with
edit_file→ runbacktest→ re-evaluate
You only need to write signal_engine.py and config.json. The backtest tool automatically handles data loading and backtest execution.
Requirements Parsing
Extract the following from the user's description:
- Instrument codes: process them according to the normalization rules below
- Time range: if the user does not specify dates, default to 10 years back from today (for example, if today is
2026-03-18, thenstart_date=2016-03-18,end_date=2026-03-18) - Strategy logic: entry / exit conditions and indicator parameters
If critical information is missing, you must ask the user instead of guessing:
- Instrument not specified → ask which instrument they want to backtest (offer several popular suggestions)
- Strategy description is vague (for example, "help me build a strategy") → provide 2-3 strategy directions for the user to choose from
- Mixed markets but not clearly specified → confirm the data source
Write config.json first, then write code. config.json must be placed in the root of run_dir.
Strategy Design
Before writing code, think through these 5 questions:
- Data requirements: what fields are needed (basic OHLCV only, daily valuation fields such as
pe/pb/roe, or statement fields such asincome_total_revenue/fina_indicator_roe?), data frequency (daily), and market (which determines the data source) - Signal logic: what are the entry conditions? What are the exit conditions? Direction (long / short / long-short)? Are there filters (volume, trend confirmation, and so on)?
- Position management: equal-weight allocation or scaling in/out? Risk control (stop-loss, maximum position)? In portfolio strategies, once top N names are selected, each weight = 1/N
- Backtest parameters: time range, initial capital (default 1,000,000), commission (default 0.1%)
- Validation checklist: signal consistency (no NaN signals), position check (normalized to prevent leverage), and completeness of generated artifacts
What ships with it
1 file 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.
- 5d ago First seen · 203 lines · 21 tokens per session scan A 010dd17f6ce3
strategy-generate is a skill published in the GitHub repository skloxo/TideTrading (10 stars, last pushed 2d ago), licensed MIT. It adds 21 tokens to every session and 3,108 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 2026-09-03.
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