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 agentmods add instructions/mario-so/ohlcv/claude-mdgit clone --depth 1 https://github.com/Mario-SO/ohlcvWhat 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 | $0.02563 | $0.02563 |
| Opus 5 | $0.01282 | $0.01282 |
| Sonnet 5 | $0.00513 | $0.00513 |
| Haiku 4.5 | $0.00256 | $0.00256 |
Grade A, and why
ohlcv CLAUDE.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 3d 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 — 270 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CLAUDE.md
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
Project Overview
A modern, high-performance Zig library for fetching and parsing OHLCV (Open-High-Low-Close-Volume) financial data from remote CSV files. The library provides:
- 33 technical indicators covering trend, momentum, volatility, volume, and advanced trading systems
- Multiple data sources (HTTP, local files, in-memory)
- High-performance parsing with streaming support for large datasets
- Advanced memory management with pooling and arena allocation
- Comprehensive benchmarking and performance monitoring
- Clean, explicit API with Zig's memory safety guarantees
Build and Development Commands
Core Commands
# Build the library and demo
zig build
# Run the demo application
zig build run
# Run all tests
zig build test
# Run benchmarks
zig build benchmark # Basic benchmark
zig build benchmark-performance # Comprehensive performance tests
zig build benchmark-streaming # Streaming vs non-streaming comparison
# Run memory profiler
zig build profile-memory
Testing Individual Components
# Run specific test file (example)
zig test test/unit/test_time_series.zig -I lib --dep ohlcv -Mohlcv=lib/ohlcv.zig
Architecture Overview
Module Organization
The library is exposed through lib/ohlcv.zig which re-exports all public APIs. The codebase follows a clean separation of concerns:
-
Data Sources (
lib/data_source/)DataSourceinterface for polymorphic data accessHttpDataSourcefor fetching from URLsFileDataSourcefor local filesMemoryDataSourcefor in-memory data
-
Parsing (
lib/parser/)CsvParserhandles CSV parsing with robust error handlingStreamingCsvParserprocesses large files in chunks without full memory loadfast_parser.zigprovides optimized parsing primitives with SIMD-aware line counting- Skips invalid rows, headers, pre-1970 dates, and zero values
- Supports multiple line endings (CRLF, LF, CR)
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.
- 3d ago First seen · 270 lines · 2,563 tokens per session scan A 3fd775b865cd
ohlcv CLAUDE.md is an instructions file published in the GitHub repository Mario-SO/ohlcv (23 stars, last pushed 4d ago), licensed MIT. It adds 2,563 tokens to every session, about $0.0128 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.
Other instructions, from other repositories
machine-learning-for-trading copilot-instructions.md
Instructions for stefan-jansen/machine-learning-for-trading, covering repository conventions for code review, prose and comment style, notebooks, data schema and results and dataframes.
pumpfun-bonkfun-bot CLAUDE.md
Instructions for chainstacklabs/pumpfun-bonkfun-bot, covering agent guide, ground rules, layout, naming inside learning-examples/ and commands.
LLM-TradeBot copilot-instructions.md
Instructions for EthanAlgoX/LLM-TradeBot, covering repository instructions, core rules, validation and ai asset governance.
polymarket-alpha-bot CLAUDE.md
Instructions for chainstacklabs/polymarket-alpha-bot, covering claude.md, commands, development, pipeline and seed data.
LLM-TradeBot backend.instructions.md
Instructions for EthanAlgoX/LLM-TradeBot, a project described as: A multi-agent AI trading system using LLMs to optimize strategies and adapt to market conditions in real-time.
LLM-TradeBot client.instructions.md
Instructions for EthanAlgoX/LLM-TradeBot, a project described as: A multi-agent AI trading system using LLMs to optimize strategies and adapt to market conditions in real-time.