What is gpt-integration?

Development guidance for changing LLM calls, agent prompts, or structured output in a trading application. Structured output means asking a language model to return data in a defined format.

Use it when editing model calls, prompts, response schemas, or related tests, including code that uses OpenRouter-compatible models.

It identifies the supported integration files and testing pattern, helping changes use the shared client instead of deprecated calls.

How kimrejstrom/alpacalyzer-algo-trader configures Claude Code and Cod

Reuse

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.

Copy the file

curl -O https://raw.githubusercontent.com/kimrejstrom/alpacalyzer-algo-trader/main/.agents/skills/gpt-integration/SKILL.md

Clone the repo

git clone --depth 1 https://github.com/kimrejstrom/alpacalyzer-algo-trader

Made for: Claude Code, Codex.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/kimrejstrom/alpacalyzer-algo-trader/gpt-integration/github.svg)](https://agentmods.dev/skills/kimrejstrom/alpacalyzer-algo-trader/gpt-integration)

Your own site

<a href="https://agentmods.dev/skills/kimrejstrom/alpacalyzer-algo-trader/gpt-integration"><img src="https://agentmods.dev/badge/skills/kimrejstrom/alpacalyzer-algo-trader/gpt-integration/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/kimrejstrom/alpacalyzer-algo-trader/gpt-integration"><img src="https://agentmods.dev/badge/skills/kimrejstrom/alpacalyzer-algo-trader/gpt-integration.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 32 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,156 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

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	$0.00032	$0.01156
Opus 5.5	$0.00013	$0.00462
Sonnet 5.5	$0.00006	$0.00231
Haiku 4.5	$0.00003	$0.00116

Measured 2mo ago against content hash dbbf8c5d51c1, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

gpt-integration 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 2mo 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.

.agents/skills/gpt-integration/SKILL.md · 88 lines

How it starts

The opening of the file, as written. The whole thing — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Scope Constraint

LLM abstraction layer: src/alpacalyzer/llm/ (primary — use this)
Agent prompts: src/alpacalyzer/prompts/ (Markdown files) and inline in agent files
Legacy GPT calls: src/alpacalyzer/gpt/call_gpt.py (deprecated — do not use for new code)
Tests must mock the LLM client (auto-mocked via conftest.py)

Steps

1. Study the LLM integration

Read these files:

src/alpacalyzer/llm/client.py — LLMClient class (OpenAI-compatible, supports OpenRouter)
src/alpacalyzer/llm/config.py — LLMTier enum (FAST/STANDARD/DEEP) and model mapping
src/alpacalyzer/llm/structured.py — instructor-based structured output with automatic retry-with-validation-feedback and manual fallback
src/alpacalyzer/agents/warren_buffet_agent.py — example agent using LLM
src/alpacalyzer/prompts/ — prompt templates (Markdown)
tests/conftest.py — auto-mocking setup

Key patterns: agents define a system prompt (investment philosophy), call LLMClient.complete_structured() with a Pydantic response model and an LLMTier, and return results that update LangGraph state. Every call emits an LLMCallEvent for observability.

Structured output pipeline

complete_structured() uses a three-layer approach:

instructor (Mode.JSON, max_retries=2) — wraps the OpenAI client, validates the response against the Pydantic model, and if validation fails, feeds the error back to the LLM and retries automatically.
Manual fallback — if instructor exhausts retries, falls back to raw json_object mode with schema injected as a system message, plus coercion helpers (_coerce_dict_lists, _strip_code_fences).
Model-level hardening — Pydantic models themselves have field_validators that tolerate common LLM mistakes (e.g. risk_reward_ratio: "1:1.47" → 1.47, entry_criteria as string → list). This means most LLM output parses on the first try without needing retries.

2. Modify or create prompts

System prompts should have: clear identity, investment philosophy/principles, analysis framework, and output expectations. Keep prompts concise — token limits vary by model tier.

Read the full file on GitHub · 88 lines

Changes

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.

2mo ago First seen · 88 lines · 32 tokens per session scan A dbbf8c5d51c1

Subscribe to this mod's changes

gpt-integration is a skill published in the GitHub repository kimrejstrom/alpacalyzer-algo-trader (2 stars, last pushed 5mo ago), licensed MIT. It adds 32 tokens to every session and 1,156 once invoked, about $0.0001 per session on Opus 5.5. 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.

Data and AI Prompting

gpt-integration next to 6 skills from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
gpt-integrationkimrejstrom/alpacalyzer-algo-trader	Skill	2	5mo ago	MIT	A	32	Claude Code, Codex
llm-app-patternsdavila7/claude-code-templates	Skill	33k	yesterday	MIT	A	54	Claude Code, Codex
prompt-engineerJeffallan/claude-skills	Skill	12k	7d ago	MIT	A	93	Claude Code
prompt-optimizationlangwatch/langwatch	Skill	5.0k	yesterday	Apache-2.0	A	105	Claude Code, Codex
omh-model-optimizationrlaope/oh-my-hermes	Skill	5.2k	yesterday	MIT	A	84	Claude Code, Codex
ideogram4digitalsamba/claude-code-video-toolkit	Skill	2.5k	yesterday	MIT	A	99	Claude Code
prompt-library-for-startupsaws/agent-toolkit-for-aws	Skill	3.4k	yesterday	Apache-2.0	A	228	Claude Code

alpacalyzer-algo-trader: Skill for Claude Code