Trading Skills

AI skills for better trading and investing decisions.

trading-skills is a portable, capability-first collection of SKILL.md packages for agent tools. It helps traders and investors think more clearly, prepare better, size cleaner, manage risk more explicitly, and review decisions without hindsight theater.

The point is not to generate magic signals. The point is to give AI structure: better trade prep, better risk process, better execution planning, and better post-trade learning.

The core library is analysis-first. Optional live-trade integrations can sit alongside it as separate, provider-specific skills without changing the default analytical workflows.

If this repo helps your workflow, star it.

Start Here

If you are not sure where to begin:

• New trade idea and want one go / no-go workflow: pre-trade-check
• Preparing around an upcoming report: earnings-trade-prep
• Too many names and need triage: watchlist-review
• Need to pressure-test the actual claim: thesis-validation
• Need to check if the plan is even tradable: execution-plan-check
• Need to size the trade conservatively: position-sizing
• Need to review portfolio overlap before adding: portfolio-risk-review
• Managing an open position: position-management
• Reviewing a closed trade: post-trade-debrief

Why Trading Skills

Generic AI gives you words. Trading and investing require process.

This repo is built to improve the parts of decision-making that actually matter:

• Better prep: identify what matters before the trade, not after it.
• Better risk control: make invalidation, structure, concentration, and sizing explicit.
• Better execution discipline: separate a good idea from an executable plan.
• Better review quality: turn journals and post-mortems into repeatable improvements.
• Better trust: disclose assumptions, source quality, and missing information instead of pretending certainty.

This is especially useful if you already use AI but want less confident prose and more rigorous thinking.

How It Works

The repo contains three useful layers:

• Groups: browsing categories such as market context, idea discovery, and review
• Atomic skills: reusable capabilities such as thesis-validation, risk-reward-sanity-check, or position-sizing
• Workflow skills: wrappers such as pre-trade-check and post-trade-debrief that chain the minimum necessary atomic skills

The default usage pattern is simple:

1. Start from the user’s actual context.
2. Run the smallest skill or workflow that fits the job.
3. Carry forward a compact trade context, not a pile of chat history.
4. Use external data only when the user’s material is not enough.
5. Keep trust high by disclosing what is known, assumed, and missing.