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 agiprolabs/claude-trading-skills --skill rl-executiongit clone --depth 1 https://github.com/agiprolabs/claude-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/agiprolabs/claude-trading-skills/rl-execution)<a href="https://agentmods.dev/skills/agiprolabs/claude-trading-skills/rl-execution"><img src="https://agentmods.dev/badge/skills/agiprolabs/claude-trading-skills/rl-execution/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/agiprolabs/claude-trading-skills/rl-execution"><img src="https://agentmods.dev/badge/skills/agiprolabs/claude-trading-skills/rl-execution.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 | $0.00023 | $0.01817 |
| Opus 5 | $0.00012 | $0.00908 |
| Sonnet 5 | $0.00005 | $0.00363 |
| Haiku 4.5 | $0.00002 | $0.00182 |
Grade A, and why
rl-execution 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 11d 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 — 244 lines — stays where its author put it; the contents beside it link to each section on GitHub.
RL Execution Optimization
Reinforcement learning (RL) for trade execution teaches an agent to split and time large orders so that total market impact is minimized. Instead of following a fixed schedule (TWAP, VWAP), an RL agent observes real-time market state and adapts its trading rate on the fly.
Why Execution Optimization Matters
Every trade has a cost beyond the quoted spread:
| Cost Component | Cause | Typical Magnitude |
|---|---|---|
| Spread cost | Crossing the bid-ask | 5-50 bps on DEXs |
| Temporary impact | Consuming liquidity | Scales with trade rate |
| Permanent impact | Information leakage | Scales with total size |
| Timing risk | Price drifts while waiting | Scales with volatility and time |
A 100 SOL market buy on a thin pool can move the price 2-5%. Splitting it into ten 10 SOL slices over a few minutes can cut that cost by 30-60%. The question is how to split optimally — and that is where execution algorithms and RL come in.
The RL Framework for Execution
State Space
The agent observes at each decision step:
state = [
remaining_qty, # How much is left to trade (0-1 normalized)
time_remaining, # Fraction of allowed horizon remaining
current_price, # Current mid-price (normalized to arrival price)
spread, # Current bid-ask spread
volatility, # Recent realized volatility
volume, # Recent trading volume (normalized)
]
Action Space
Discrete actions controlling how much to trade this step:
actions = [0%, 10%, 25%, 50%, 100%] # of remaining quantity
A small action space keeps the problem tractable. Each action represents the fraction of the remaining order to execute in the current time step.
Reward Function
The reward penalizes execution cost relative to a benchmark:
reward = -(execution_price - arrival_price) * quantity_traded
Summed over all steps, the total reward equals the negative implementation shortfall. The agent learns to minimize total cost.
What ships with it
4 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.
- 11d ago First seen · 244 lines · 23 tokens per session scan A 24a729f221aa
rl-execution is a skill published in the GitHub repository agiprolabs/claude-trading-skills (354 stars, last pushed 8d ago), licensed MIT. It adds 23 tokens to every session and 1,817 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 2026-08-30.
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