ContextForge — Prompts
ContextForge is a gateway/proxy infrastructure tool, not an LLM prompting framework. It ships prompt-related infrastructure (a Prompts Registry with Jinja2 templates, multimodal support, and versioning) but does not include agent-instruction prompt files in the traditional sense.
AGENTS.md (Developer Instructions)
The AGENTS.md file provides instructions for AI coding assistants working on the ContextForge codebase itself. This is the closest thing to a "prompt file":
## Project Overview
ContextForge is an open source registry and proxy that federates MCP, A2A, and REST/gRPC APIs
with centralized governance, discovery, and observability. It federates tools, agents, and APIs,
optimizes agent and tool calling, and supports plugins, auth/RBAC, rate-limiting, virtual servers,
multi-transport protocols, and an optional Admin UI.
## Authentication & RBAC Overview
ContextForge implements a **two-layer security model**:
1. **Token Scoping (Layer 1)**: Controls what resources a user CAN SEE (data filtering)
2. **RBAC (Layer 2)**: Controls what actions a user CAN DO (permission checks)
### Token Scoping Quick Reference
**API / legacy tokens** — JWT `teams` claim is the sole authority:
| JWT `teams` State | `is_admin: true` | `is_admin: false` |
|-------------------|------------------|-------------------|
| Key MISSING | PUBLIC-ONLY [] | PUBLIC-ONLY [] |
| `teams: null` | ADMIN BYPASS | PUBLIC-ONLY [] |
| `teams: []` | PUBLIC-ONLY [] | PUBLIC-ONLY [] |
| `teams: ["t1"]` | Team + Public | Team + Public |
Prompting technique: Architectural reference document with decision table. Designed to prevent AI assistants from misimplementing the RBAC model by showing all state combinations explicitly.
CLAUDE.md (Developer Agent Instructions)
AGENTS.md
(The .claude/CLAUDE.md simply symlinks to the root AGENTS.md — one source of truth for all AI assistants.)
Prompts Registry (Runtime Feature)
ContextForge ships a PromptService that manages Jinja2 prompt templates for use by MCP clients. Prompts are versioned, support rollback, and can include multimodal content. This is infrastructure for storing and serving prompts to LLMs — not the framework's own agent instructions.
Plugin AGENTS.md (Plugin Developer Instructions)
# plugins/AGENTS.md
Plugin framework overview. Plugins are Python packages installed via `pip install <plugin-name>`.
Each plugin implements:
- `pre_call_hook(request) -> request`: Transform/filter before upstream
- `post_call_hook(response) -> response`: Transform/filter after upstream
- `metadata()`: Name, version, description, configuration schema
Prompting technique: Contract-first specification with interface requirements — tells AI assistants exactly what a plugin must implement without prose explanation.