Borrowing it
Nothing to install: this file belongs to typedef-ai/fenic. 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/typedef-ai/fenic/main/CLAUDE.mdgit clone --depth 1 https://github.com/typedef-ai/fenicWrote 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/instructions/typedef-ai/fenic/claude-md)<a href="https://agentmods.dev/instructions/typedef-ai/fenic/claude-md"><img src="https://agentmods.dev/badge/instructions/typedef-ai/fenic/claude-md/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/instructions/typedef-ai/fenic/claude-md"><img src="https://agentmods.dev/badge/instructions/typedef-ai/fenic/claude-md.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 | $0.01047 | $0.01047 |
| Opus 5.5 | $0.00419 | $0.00419 |
| Sonnet 5.5 | $0.00209 | $0.00209 |
| Haiku 4.5 | $0.00105 | $0.00105 |
Grade A, and why
fenic 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 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.
How it starts
The opening of the file, as written. The whole thing — 129 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CLAUDE.md
@AGENTS.md
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository. The @AGENTS.md line above pulls in the cross-agent quick-reference for writing fenic pipelines (also read natively by Codex, Cursor, Gemini CLI, etc.); the rest of this file covers developing fenic itself. For deep authoring help, the fenic-mechanics skill auto-loads in Claude Code.
Project Overview
Fenic is a PySpark-inspired DataFrame framework specifically designed for AI and LLM applications. It provides semantic operators for LLM-powered data transformations, native support for unstructured data (markdown, transcripts, JSON), and efficient batch inference across multiple model providers (OpenAI, Anthropic, Google).
Development Commands
Setup and Dependencies
# Initial setup (installs dependencies and builds Rust extensions)
just setup
# Sync Python dependencies
just sync
# Build Rust extensions (required after Rust code changes)
just sync-rust
# Sync with cloud extras
just sync-cloud
Testing
# Run local tests (excludes cloud tests)
just test
# or
just test-local
# Run cloud-related tests
just test-cloud
# Run specific test file
uv run pytest tests/path/to/test_file.py
# Run specific test
uv run pytest tests/path/to/test_file.py::test_function_name
Linting and Formatting
# Ruff is configured for linting (Google-style docstrings)
# Auto-formats code examples in docstrings
uv run ruff check .
uv run ruff format .
Documentation
# Preview documentation locally
just preview-docs
Architecture Overview
Directory Structure
src/fenic/: Main Python source codeapi/: Public API - Session, DataFrame, Functions, IOcore/: Core logic - logical plans, expressions, types, optimizer_backends/: Execution backends (local using Polars, cloud using gRPC)_inference/: LLM inference clients for different providers
rust/: Rust source for performance-critical operations (text chunking, markdown/JSON parsing)tests/: Test suite (pytest-based)examples/: Example applications demonstrating fenic usage
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 · 129 lines · 1,047 tokens per session scan A da648820827f
fenic CLAUDE.md is an instructions file published in the GitHub repository typedef-ai/fenic (676 stars, last pushed 2d ago), licensed Apache-2.0. It adds 1,047 tokens to every session, about $0.0042 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 2026-08-30.
Other instructions, from other repositories
vscode buildNext.instructions.md
Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
next.js AGENTS.md
AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, cache eligibility and rollout and applying the notice (cutover).
langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.
vscode copilot-instructions.md
Copilot instructions for microsoft/vscode, covering vs code copilot instructions, project overview, root folders, core architecture (src/ folder) and built-in extensions (extensions/ folder).
deepseek-harness AGENTS.md
AGENTS.md instructions for deepseek-ai/deepseek-harness, covering agents.md, pre-stable apis and released session data, repository layout, commands and host sandbox failures.