LLM-TradeBot backend.instructions.md

Project-specific coding rules for the backend of an AI trading system that uses language models to adjust trading strategies.

In plain words
What is it for?
Use them while changing backend services, data providers, configuration, command-line options, APIs, authentication, schedules, notifications, or report data, then run the prescribed checks.
Why use it?
They help keep existing data flows, provider fallbacks, configuration, reports, and connected web or desktop clients consistent when backend code changes.

Instructions file for GitHub Copilot

Install

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.

agentmods
npx agentmods add instructions/ethanalgox/llm-tradebot/backend
Clone the repo
git clone --depth 1 https://github.com/EthanAlgoX/LLM-TradeBot

Made for: GitHub Copilot.

Per session 176 This file is loaded in full into every session.
When invoked 176 The same file — it is already loaded in full.
Security scan A 0 findings. Scan, not verified.
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.

ModelPer sessionOnce invoked
Fable 5 $0.00176 $0.00176
Opus 5 $0.00088 $0.00088
Sonnet 5 $0.00035 $0.00035
Haiku 4.5 $0.00018 $0.00018

Measured 2d ago against content hash 0d01c998fd8d, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

LLM-TradeBot backend.instructions.md 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 2d 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.

.github/instructions/backend.instructions.md · 12 lines

What it actually says

Backend Instructions

  • Preserve current pipeline boundaries and reuse existing services, repositories, schemas, and fallback logic instead of creating parallel paths.
  • Changes touching config, CLI flags, schedule semantics, API behavior, auth, or report payloads must sync .env.example and assess Web/Desktop compatibility.
  • Changes in data_provider/ must preserve provider priority, normalization behavior, timeout/retry expectations, and graceful degradation.
  • Prefer ./scripts/ci_gate.sh when feasible; otherwise run python -m py_compile on changed files plus the closest deterministic tests.
  • Do not let a single provider, notification channel, or optional integration failure break the main analysis flow unless the requirement explicitly demands fail-fast behavior.
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.

  1. 2d ago First seen · 12 lines · 176 tokens per session scan A 0d01c998fd8d

Subscribe to this mod's changes

LLM-TradeBot backend.instructions.md is an instructions file published in the GitHub repository EthanAlgoX/LLM-TradeBot (316 stars, last pushed 14d ago), licensed MIT. It adds 176 tokens to every session, about $0.0009 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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