Borrowing it
Nothing to install: this file belongs to marc-shade/Ollama-Workbench. 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/marc-shade/Ollama-Workbench/main/CLAUDE.mdgit clone --depth 1 https://github.com/marc-shade/Ollama-WorkbenchWrote 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/marc-shade/ollama-workbench/claude-md)<a href="https://agentmods.dev/instructions/marc-shade/ollama-workbench/claude-md"><img src="https://agentmods.dev/badge/instructions/marc-shade/ollama-workbench/claude-md.svg" alt="Measured on agentmods" 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 | $0.02035 | $0.02035 |
| Opus 5 | $0.01018 | $0.01018 |
| Sonnet 5 | $0.00407 | $0.00407 |
| Haiku 4.5 | $0.00203 | $0.00203 |
Grade A, and why
Ollama-Workbench CLAUDE.md scanned grade A with 1 finding 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 8d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- Ollama must be running at `http://localhost:11434`. Check: `curl -s http://localhost:11434/api/tags` How it starts
The opening of the file, as written. The whole thing — 173 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
Ollama Workbench is a Streamlit-based platform for managing, testing, and interacting with AI models from Ollama and external providers (OpenAI, Groq, Mistral). It runs as a Streamlit app on port 8501 with a companion Flask API server on a dynamic port for Chrome extension communication.
Commands
# Run the app (recommended - handles Ollama server startup)
./start_workbench.sh
# Run manually
streamlit run main.py
# Run all tests
python scripts/run_all_tests.py
# Run pytest suite
python -m pytest tests/ -v
python -m pytest tests/test_chat_interfaces.py -v # specific file
python -m pytest tests/test_chat_interfaces.py::TestChatInterface -k "test_session" # specific test
# Verify all package imports work
python scripts/test_imports.py
# Lint
ruff check .
flake8 .
# Setup from scratch
python scripts/setup_workbench.py
Package Structure
The codebase is organized into the ollama_workbench package with 8 sub-packages. main.py is the only Python file at the root — it's the Streamlit entry point.
main.py # Streamlit entry point + Flask API
ollama_workbench/
providers/ # AI provider API clients
ollama_utils.py # Ollama (primary) - model mgmt, embeddings, monitoring
openai_utils.py # OpenAI
groq_utils.py # Groq
mistral_utils.py # Mistral
external_providers.py # Provider configuration UI
chat/ # Chat interfaces
chat_interface.py # Base chat (token counting, RAG, agent prompts, CoT)
enhanced_chat_interface.py # Wraps chat_interface with Open WebUI styling
multimodel_chat.py # Multi-model simultaneous chat
multimodal_chat.py # Vision/image chat
voice_interface.py # Voice chat (requires pyaudio)
persona_chat.py # Persona group chat
collaborative_workspace.py # Collaborative workspace
canvas.py # Document canvas for workspace
voice_utils.py # Voice/TTS utilities
tts_utils.py # Text-to-speech helpers
workflows/ # AI workflow orchestration
build.py # Autonomous software development
research.py # Multi-source research automation
brainstorm.py # AI brainstorming with multiple agents
projects.py # AI-assisted project/task management
nodes.py # CEF visual workflow builder
agents.py # Agent definitions
info_brainstorm.py # Brainstorm info helpers
knowledge/ # Knowledge management & RAG
simplified_rag.py # Enhanced RAG interface (active)
enhanced_corpus.py # GraphRAG corpus management
repo_docs.py # Repository analysis
web_to_corpus.py # Web crawling to corpus
search_libraries.py # Multi-engine web search
models/ # Model management & testing
model_comparison.py # Response quality comparison
model_tests.py # Performance benchmarks
feature_test.py # Feature capability tests
vision_comparison.py # Vision model comparison
local_models.py # List/manage local models
pull_model.py # Download new models
show_model.py # Model details viewer
remove_model.py # Model removal
update_models.py # Batch model updates
model_management.py # Management dashboard
model_capabilities.py # Capability testing UI
model_capability_registry.py # Capability detection
model_onboarding.py # Model onboarding tests
test_visualization.py # Test result visualization
server/ # Server management
server_configuration.py # Ollama server config
server_monitoring.py # Resource monitoring
performance_metrics.py # Performance tracking
openai_compatibility.py # OpenAI-compatible API
core/ # Core infrastructure
config.py # Application configuration
session_utils.py # Session state management
db_init.py # Database initialization
error_handling.py # Error handling utilities
ui/ # UI components
styles.py # Theming (apply_styles -> colors, theme)
prompts.py # Prompt template management
file_management.py # File browser/editor
structured_output.py # JSON schema output UI
tool_playground.py # Tool calling playground
contextual_response.py # Contextual response testing
welcome.py # Help/welcome page
global_vrm_loader.py # VRM model loader
tests/ # All test files
scripts/ # Standalone utility scripts
persona_lab/ # Persona generation lab
observability/ # Optional Opik integration
tts_server/ # Standalone TTS Flask server
prompts/ # JSON prompt templates
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.
- 8d ago First seen · 173 lines · 2,035 tokens per session scan A beea2fbb36c5
Ollama-Workbench CLAUDE.md is an instructions file published in the GitHub repository marc-shade/Ollama-Workbench (50 stars, last pushed 1mo ago), licensed MIT. It adds 2,035 tokens to every session, about $0.0102 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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