AI Decision System
Replayable, offline-verifiable records for AI-assisted go/no-go decisions: experiment promotions, typed model answers and human-approved actions.
MCP retrieval stack for AI coding assistants. Hybrid code search, ReFRAG micro-chunking, local LLM enhancement.
Developer toolsPython · JavaScriptGitHub @Context-Engine-AIOnlineOwner unconfirmed
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Context-Engine MCP - Agentic Context Compression Suite
Translation pending
Taken from the repository README
Loading the full README.
Read the full README ↗Context Engine ships AI agent skills that teach your coding assistant how to use 30+ MCP tools for semantic search, symbol graph navigation, memory, and more.
Guidance: Recommend pasting this into your project's CLAUDE.md or AGENTS.md to reach subagents:
# Context Engine
When the `context-engine` MCP server is connected, this workspace is indexed: for any question about the codebase, call `repo_search` FIRST, before ls/find/Grep/Read, and again whenever you move to a new area; for how/why questions that span several files use `context_answer`. Results are verified source with exact line ranges: treat them as already read. Use Grep only to confirm an exact literal in a file you already know, or to count. For questions asking for every/all sites, run one repo_search per distinct pattern (queries=[...]) and follow the refs. If the tools are listed as deferred, load them with ToolSearch first.
| Need | Tool |
|---|---|
| Find code by behavior, concept, error text or identifier | `repo_search` (`queries=[...]` for several at once; `under`, `language`, `path_glob` to narrow) |
| Explain how/why across several files, with citations | `context_answer` |
| Definition, callers, callees, importers, references of a symbol | `symbol_graph` (several names: `A\|B\|C`) |
| Multi-hop impact before a change | `graph_query` |
| Code that may live in another indexed repo | `cross_repo_search` |
Before editing, fetch every involved symbol in one call; before a rename or signature change, get every site with `symbol_graph` `query_type=references`. Search again only after learning something new.
Recommended: Install natively from the public GitHub repo:
# Add the marketplace (one-time)
/plugin marketplace add Context-Engine-AI/Context-Engine
# Install the skill
/plugin install context-engine
This pulls the skill directly from GitHub and auto-loads MCP tool guidance into your session.
Alternatively, copy the rules file manually:
cp -r skills/context-engine/ your-project/.claude/
Context Engine rules are included in .cursorrules at the root of your workspace. Cursor picks this up automatically when the file is present.
# Copy to your project root
cp .cursorrules your-project/.cursorrules
Recommended: Install natively using the built-in skill installer — just ask Codex:
"Install the context-engine skill from https://github.com/Context-Engine-AI/Context-Engine"
Codex will pull .codex/skills/context-engine/ (including SKILL.md) into ~/.codex/skills/ automatically.
Or install manually:
cp -r .codex/skills/context-engine/ ~/.codex/skills/context-engine/
cp -r .codex/skills/ your-project/.codex/skills/
cp -r .augment/ your-project/.augment/
cp GEMINI.md your-project/GEMINI.md
The core skill file works with any AI assistant that supports custom instructions:
cp skills/context-engine/SKILL.md your-project/
Then tell your assistant: "Read SKILL.md for instructions on using Context Engine MCP tools."
If you use Claude Code, Codex, or another terminal-based MCP client, install the MCP bridge to connect your codebase to Context Engine without VS Code:
npm install -g @context-engine-bridge/context-engine-mcp-bridge
# Authenticate, index your codebase, and start watching for changes
ctxce connect <your-api-key> --workspace /path/to/repo
# Run as a background daemon (recommended)
ctxce connect <your-api-key> --workspace /path/to/repo --daemon
ctxce status # Check if the daemon is running
ctxce stop # Stop the background daemon
| Flag | Alias | Description |
|---|---|---|
--workspace <path> | -w | Workspace root (default: cwd) |
--daemon | -d, --bg | Run as background daemon |
--interval <sec> | File watch interval in seconds (default: 30) | |
--no-watch | --once | Index once, don't watch for changes |
--skip-index | --auth-only | Authenticate only, skip initial index |
Once connected, point your MCP client at the bridge:
# stdio mode (for Claude Code, Codex, etc.)
ctxce mcp-serve --workspace /path/to/repo
# HTTP mode (for clients that speak HTTP)
ctxce mcp-http-serve --workspace /path/to/repo --port 30810
The daemon and MCP server share auth via ~/.ctxce/auth.json. Logs are at ~/.context-engine/daemon.log.
For full bridge documentation, see Context-Engine-MCP-Bridge.
The skills teach your AI assistant to:
search as the default tool — auto-routes queries to the best backend (semantic search, Q&A, symbol graph, tests, config)symbol_graph — find callers, callees, definitions, importers, subclassesbatch_search, batch_symbol_graph, batch_graph_query for 75%+ token savingsmemory_store and memory_find for persistent context across sessionscross_repo_search with boundary tracing for multi-repo codebasespattern_search for retry loops, error handling, singletons across languagessearch_commits_for and change_history_for_pathSee skills/context-engine/SKILL.md for the complete tool reference.
npm i -g @context-engine-bridge/context-engine-mcp-bridge && ctxce connect <api-key> --daemon© 2025 Context Engine Inc. and John Donalson.