Borrowing it
Nothing to install: this file belongs to kimrejstrom/alpacalyzer-algo-trader. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/kimrejstrom/alpacalyzer-algo-trader/main/.agents/skills/pydantic-model/SKILL.mdgit clone --depth 1 https://github.com/kimrejstrom/alpacalyzer-algo-traderWrote 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/kimrejstrom/alpacalyzer-algo-trader/pydantic-model)<a href="https://agentmods.dev/skills/kimrejstrom/alpacalyzer-algo-trader/pydantic-model"><img src="https://agentmods.dev/badge/skills/kimrejstrom/alpacalyzer-algo-trader/pydantic-model/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/kimrejstrom/alpacalyzer-algo-trader/pydantic-model"><img src="https://agentmods.dev/badge/skills/kimrejstrom/alpacalyzer-algo-trader/pydantic-model.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 | $0.00033 | $0.00805 |
| Opus 5 | $0.00016 | $0.00402 |
| Sonnet 5 | $0.00007 | $0.00161 |
| Haiku 4.5 | $0.00003 | $0.00081 |
Grade A, and why
pydantic-model 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 — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Scope Constraint
| Model Purpose | Location |
|---|---|
| Trading data (signals, positions) | src/alpacalyzer/data/models.py |
| Strategy configuration | src/alpacalyzer/strategies/config.py |
| Event types (logging) | src/alpacalyzer/events/models.py |
| Execution state | src/alpacalyzer/execution/state.py |
| Agent responses | src/alpacalyzer/data/models.py |
Steps
1. Study existing models
Read src/alpacalyzer/data/models.py for the established patterns. Also check src/alpacalyzer/strategies/config.py and src/alpacalyzer/events/models.py.
Key patterns: Pydantic v2 syntax (model_dump(), ConfigDict, field_validator with @classmethod), Field() with descriptions for GPT guidance, validators for business logic.
LLM output resilience
Models that receive LLM output need extra hardening because LLMs frequently return wrong types, missing fields, or invented enum values. Patterns used in TradingStrategy:
- Defaults for commonly-omitted fields:
quantity: int = 0,entry_point: float = 0.0,strategy_notes: str = "" - Type coercion validators:
field_validator("risk_reward_ratio", mode="before")parses"1:1.47"→1.47 - Flexible input types:
entry_criteria: list[EntryCriteria | str]accepts both structured dicts and plain strings - Enum normalization:
EntryCriteria.entry_typevalidator maps near-miss values like"price_above_ma50"→"above_ma50" - Default collections:
entry_criteria: list[...] = Field(default_factory=list)instead of required list
2. Create model
Follow these conventions:
- Inherit from
pydantic.BaseModel - Use
Field(description=...)for all fields (helps GPT structured output) - Add
@field_validatorfor business constraints (e.g.,stop_loss_pct < target_pct) - Add
@model_validator(mode='after')for cross-field validation - Use
Literalfor constrained string choices
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 · 84 lines · 33 tokens per session scan A 7847d6bd6d53
pydantic-model is a skill published in the GitHub repository kimrejstrom/alpacalyzer-algo-trader (2 stars, last pushed 3mo ago), licensed MIT. It adds 33 tokens to every session and 805 once invoked, about $0.0002 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.
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