Agent skill
codebase-management
Set up, index, and manage SocratiCode codebase indexing. Use when the user wants to index a project, check infrastructure health, start/stop file watching, configure context artifacts, troubleshoot indexing issues, manage the code graph, or any SocratiCode administrative task. Activates when the user mentions indexing, setting up search, SocratiCode infrastructure, or managing the codebase index.
Install this agent skill to your Project
npx add-skill https://github.com/giancarloerra/SocratiCode/tree/main/skills/codebase-management
SKILL.md
SocratiCode Management
Set up, index, and manage SocratiCode codebase indexing, file watching, code graphs, and context artifacts.
First-Time Setup
- Check infrastructure:
codebase_health— verifies Docker, Qdrant, Ollama/embedding provider, and embedding model - Start indexing:
codebase_index— runs in background, returns immediately - Poll progress:
codebase_status— call every ~60 seconds until 100% complete- This also keeps the MCP connection alive (some hosts disconnect idle connections)
- Done: Graph auto-builds after indexing. File watcher auto-starts. Ready to search.
On first use, SocratiCode automatically pulls Docker images, starts containers, and downloads the embedding model (~5 min one-time setup).
Incremental Updates & File Watching
The file watcher keeps the index automatically updated. It auto-starts after indexing.
codebase_watch { action: "start" }— start the watcher (runs catch-up update first)codebase_watch { action: "stop" }— stop the watchercodebase_watch { action: "status" }— list watched projects (including cross-process)codebase_update— manual incremental update (only changed files, synchronous). Usually not needed if watcher is active.
Managing Indexes
codebase_stop— gracefully pause in-progress indexing. Current batch finishes and checkpoints. All progress preserved. Resume withcodebase_index.codebase_remove— delete entire index (destructive). Safely stops watcher, cancels indexing, waits for graph builds.codebase_list_projects— list all indexed projects with metadata, graph info, and artifact status.
Managing the Code Graph
The dependency graph is auto-built after indexing. Manual management is rarely needed.
codebase_graph_build— manually rebuild (background, async). Poll withcodebase_graph_status.codebase_graph_remove— delete graph (auto-rebuilds on nextcodebase_index)codebase_graph_status— check build progress or graph readiness
Context Artifacts Setup
To index non-code knowledge, create .socraticodecontextartifacts.json in the project root:
{
"artifacts": [
{
"name": "database-schema",
"path": "./docs/schema.sql",
"description": "PostgreSQL schema — all tables, indexes, constraints, foreign keys."
}
]
}
Supported types: SQL schemas, OpenAPI/Protobuf API specs, Terraform/CloudFormation configs, Kubernetes manifests, architecture docs, environment configs — any text-based file or directory.
codebase_context_index— manually index/re-index all artifacts (usually auto-triggered)codebase_context_remove— remove all indexed artifacts (blocked during indexing)
Troubleshooting
| Problem | Solution |
|---|---|
| Docker not available | Install Docker Desktop from https://docker.com, ensure it's running |
| Slow indexing on macOS/Windows | Docker can't use GPU. Install native Ollama from https://ollama.com/download for Metal/CUDA acceleration. Or use cloud embeddings. |
| Want cloud embeddings instead | Set EMBEDDING_PROVIDER=openai + OPENAI_API_KEY, or EMBEDDING_PROVIDER=google + GOOGLE_API_KEY |
| Search returns no results | Check codebase_status — project may not be indexed. Run codebase_index. |
| Stale results | Check if watcher is active (codebase_status). Run codebase_update or codebase_watch { action: "start" }. |
| Indexing was interrupted | Run codebase_index again — it resumes from the last checkpoint automatically. |
| Another process is indexing | codebase_status detects cross-process indexing. Wait for it, or use codebase_stop. |
Key Environment Variables
| Variable | Default | Description |
|---|---|---|
QDRANT_MODE |
managed |
managed (Docker) or external (remote/cloud Qdrant) |
QDRANT_URL |
— | Full URL for remote Qdrant (e.g. https://xyz.cloud.qdrant.io:6333) |
QDRANT_API_KEY |
— | API key for remote Qdrant |
EMBEDDING_PROVIDER |
ollama |
ollama, openai, or google |
OPENAI_API_KEY |
— | Required when EMBEDDING_PROVIDER=openai |
GOOGLE_API_KEY |
— | Required when EMBEDDING_PROVIDER=google |
OLLAMA_MODE |
auto |
auto (detect native, fallback Docker), docker, external |
EMBEDDING_MODEL |
nomic-embed-text |
Model name (provider-specific) |
SEARCH_DEFAULT_LIMIT |
10 |
Default result limit for codebase_search (1-50) |
SEARCH_MIN_SCORE |
0.10 |
Default minimum RRF score threshold (0-1) |
MAX_FILE_SIZE_MB |
5 |
Maximum file size for indexing in MB |
EXTRA_EXTENSIONS |
— | Additional file extensions to index (e.g. .tpl,.blade,.hbs) |
For full parameter details on every tool, see references/tool-reference.md.
Recommended Agent Skills
Expand your agent's capabilities with these related and highly-rated skills.
codebase-exploration
Explore and understand codebases using SocratiCode semantic search, dependency graphs, and context artifacts. Use when exploring code, understanding architecture, finding functions/types, analyzing dependencies, searching database schemas or API specs, or when socraticode/codebase_search tools are available. Activates when the user asks about code structure, wants to find where a feature lives, or needs to understand how code is organized.
verl-rl-training
Provides guidance for training LLMs with reinforcement learning using verl (Volcano Engine RL). Use when implementing RLHF, GRPO, PPO, or other RL algorithms for LLM post-training at scale with flexible infrastructure backends.
openrlhf-training
High-performance RLHF framework with Ray+vLLM acceleration. Use for PPO, GRPO, RLOO, DPO training of large models (7B-70B+). Built on Ray, vLLM, ZeRO-3. 2× faster than DeepSpeedChat with distributed architecture and GPU resource sharing.
gguf-quantization
GGUF format and llama.cpp quantization for efficient CPU/GPU inference. Use when deploying models on consumer hardware, Apple Silicon, or when needing flexible quantization from 2-8 bit without GPU requirements.
Claude Code Guide
Master guide for using Claude Code effectively. Includes configuration templates, prompting strategies "Thinking" keywords, debugging techniques, and best practices for interacting with the agent.
qdrant-vector-search
High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.
Didn't find tool you were looking for?