fusion CLAUDE.md

fusion CLAUDE.md is an instructions file for coding agents from yasinyaman/fusion. It costs 1,178 tokens per session, scanned A, original, Apache-2.0.

A project instruction file for Fusion, a DuckDB-based analytics engine that reads data through a REST service and exposes it to AI tools. DuckDB is a database designed to run inside an application.

In plain words
What is it for?
Working on Fusion code, including installation, tests, linting, demonstrations, MCP use, and REST-server operation.
Why use it?
It gives the coding agent the project's setup commands, architecture, data flow, and file layout in one place.

Instructions file

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/yasinyaman/fusion/claude-md
Clone the repo
git clone --depth 1 https://github.com/yasinyaman/fusion

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.

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README.md
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Your own site
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Per session 1,178 This file is loaded in full into every session.
When invoked 1,178 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 $0.01178 $0.01178
Opus 5 $0.00589 $0.00589
Sonnet 5 $0.00236 $0.00236
Haiku 4.5 $0.00118 $0.00118

Measured 5d ago against content hash 734484c3ea6e, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

fusion 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 5d 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.

CLAUDE.md · 102 lines

How it starts

The opening of the file, as written. The whole thing — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Fusion OLAP Engine

DuckDB-powered in-memory analytics engine — LLM tool for data access via MCP, REST API, and OpenAI Function Calling.

Project Overview

  • Language: Python 3.10+
  • Core dependency: DuckDB 1.2+
  • Data source: Warp REST API (PostgreSQL/MySQL)

Commands

  • Install: pip install -e ".[all]"
  • Test: pytest tests/ -v
  • Demo: python -m demo.demo
  • Lint: ruff check fusion/
  • MCP Server: fusion-mcp --warp-url http://localhost:8000 --database mydb
  • REST Server: fusion-rest --warp-url http://localhost:8000 --auto-discover --port 9000

Architecture

Warp REST API --> WarpConnector --> DuckDB (in-memory)
(PostgreSQL/MySQL)                       |
                                    LLM --> Tool Layer --> Result
                                         (MCP / REST / SDK)

Three main layers:

  1. Data Source — Warp REST API connector (auto-discovery, pagination, schema inference, query pushdown)
  2. DuckDB Core — In-memory columnar store, materialized views, query cache, catalog manager, lazy loading
  3. LLM Tool Layer — 10 tools (MCP Server + REST API + OpenAI Function Calling), ToolExecutor, SQL guardrails

File Structure

fusion/
├── __init__.py              # Public API exports
├── engine.py                # OLAPEngine — main orchestration + pushdown routing
├── cache.py                 # QueryCache (LRU with TTL)
├── catalog.py               # SchemaCatalog — multi-source metadata
├── guardrails.py            # SQLGuardrails — blocks destructive SQL
├── result.py                # QueryResult — output format conversions
├── strategy.py              # FetchStrategy — smart table loading + pushdown eligibility
├── exceptions.py            # Custom exception hierarchy
├── connectors/
│   ├── __init__.py          # Connector registry (warp only)
│   ├── base.py              # BaseConnector (abstract, supports_pushdown property)
│   └── warp.py              # WarpConnector (Warp REST API client, pushdown capable)
├── tools/
│   ├── __init__.py          # Tool layer exports
│   ├── definitions.py       # 10 tool schemas (OpenAI + MCP format)
│   ├── executor.py          # ToolExecutor — routes tool calls to engine
│   ├── mcp_server.py        # MCP Server (stdio transport, FastMCP)
│   └── rest_server.py       # REST API Server (FastAPI, Swagger UI)
└── views/
    └── materialized.py      # MaterializedViewManager

Read the full file on GitHub · 102 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.

  1. 5d ago First seen · 102 lines · 1,178 tokens per session scan A 734484c3ea6e

Subscribe to this mod's changes

fusion CLAUDE.md is an instructions file published in the GitHub repository yasinyaman/fusion (2 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 1,178 tokens to every session, about $0.0059 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.