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 market-microstructuregit 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/market-microstructure)<a href="https://agentmods.dev/skills/agiprolabs/claude-trading-skills/market-microstructure"><img src="https://agentmods.dev/badge/skills/agiprolabs/claude-trading-skills/market-microstructure/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/market-microstructure"><img src="https://agentmods.dev/badge/skills/agiprolabs/claude-trading-skills/market-microstructure.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Memory Poisoning · line 61 Skill manipulates agent memory, state, or stored context. Memory corruption can alter personality, override safety rules, or cause unpredictable behavior.Fix: Protect agent memory and state from modification by untrusted content. Use read-only memory for critical instructions and validate all state changes.
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 | $0.00026 | $0.02770 |
| Opus 5 | $0.00013 | $0.01385 |
| Sonnet 5 | $0.00005 | $0.00554 |
| Haiku 4.5 | $0.00003 | $0.00277 |
Grade A, and why
market-microstructure 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 13d 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 — 310 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Market Microstructure — DEX Orderflow Analysis
Overview
Market microstructure on Solana DEXes differs fundamentally from traditional finance. There are no orderbooks on AMMs — every trade is a swap against a liquidity pool. Yet trade flow analysis remains powerful: the sequence, size, and direction of swaps reveal accumulation, distribution, whale activity, and wash trading patterns.
This skill covers:
- Trade classification — identifying buys vs sells from swap direction
- Volume profiles — time-based and size-based breakdowns
- Buyer/seller pressure — ratio metrics, net flow, trade count asymmetry
- Trade size distribution — whale detection, retail vs institutional flow
- Flow momentum signals — acceleration, volume spikes, composite scores
- Token velocity — turnover rate as a sentiment proxy
- Wash trading detection — spotting fake volume and bot patterns
Why Microstructure Matters on DEXes
On CEXes, microstructure means orderbook depth, bid-ask spread, and queue position. On AMMs, liquidity sits in pool curves — there is no spread or queue. But the trade tape (the chronological list of swaps) contains rich signal:
- Who is trading? — Whale wallets vs retail, smart money vs bots
- How are they trading? — Large single swaps vs DCA-style splits
- When are they trading? — Volume clustering around events or time zones
- What direction? — Net buy vs sell pressure over sliding windows
These signals feed into entry/exit timing, position sizing, and token quality scoring.
Trade Classification
Buy vs Sell Identification
On Solana DEXes, every swap has an input token and output token:
| Swap Direction | Classification | Meaning |
|---|---|---|
| SOL → Token | Buy | Trader spending SOL to acquire token |
| USDC → Token | Buy | Trader spending stables to acquire token |
| Token → SOL | Sell | Trader converting token back to SOL |
| Token → USDC | Sell | Trader converting token to stables |
| Token A → Token B | Context-dependent | Classify based on which token you're analyzing |
What ships with it
5 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.
- 13d ago First seen · 310 lines · 26 tokens per session scan A 65d5b86cdf96
market-microstructure is a skill published in the GitHub repository agiprolabs/claude-trading-skills (356 stars, last pushed 9d ago), licensed MIT. It adds 26 tokens to every session and 2,770 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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