Notre avis
Atlas permet d'ingérer et de rechercher du code et de la documentation dans une mémoire sémantique persistante via Qdrant et les embeddings Voyage.
Points forts
- Stockage persistant du contexte entre sessions, évitant les pertes d'information.
- Recherche sémantique rapide avec des embeddings de haute qualité.
- Ingestion récursive de répertoires et support de multiples formats de fichiers.
Limites
- Nécessite Qdrant en cours d'exécution localement (Docker) et une clé API Voyage.
- Le temps d'ingestion initial peut être long pour de grands volumes.
- La mémoire RAM peut être élevée (1.4 Go pour 1M vecteurs).
Utilisez Atlas lorsque vous devez conserver des informations contextuelles entre sessions ou rechercher des décisions passées dans un projet.
Évitez Atlas pour des tâches ponctuelles sans besoin de mémoire persistante ou si vous ne pouvez pas exécuter Qdrant localement.
Analyse de sécurité
SûrThe skill describes local document ingestion into a semantic database using standard bash commands (bun, docker, curl). It does not instruct destructive actions, exfiltration, or execution of untrusted code. The only network interaction is localhost health check and the Voyage API, both legitimate.
Aucun point d'attention détecté
Exemples
Remember the API architecture we discussed in the last session and store it for future reference.Ingest all the .atlas research files in the project directory.Find all mentions of authentication patterns in the ingested codebase.name: atlas description: Ingest project documentation and code into persistent semantic memory (Qdrant + Voyage embeddings). Use when user wants to remember context across sessions, ingest docs, or search previous work. Requires Qdrant running locally and VOYAGE_API_KEY set. allowed-tools: Bash(bun:*)
Atlas - Persistent Semantic Memory
Atlas provides automatic context ingestion and retrieval using Voyage embeddings + Qdrant vector database. Solves the context overflow problem by storing knowledge persistently across sessions.
Quick Start
Prerequisites
- Qdrant running locally:
docker run -d -p 6333:6333 qdrant/qdrant
- VOYAGE_API_KEY set (get from https://voyageai.com):
export VOYAGE_API_KEY="your-key-here"
- Verify setup:
curl http://localhost:6333/health
Ingesting Context
Store files in Atlas memory for persistent retrieval:
Ingest Single File
cd ~/production/atlas
bun run --filter @inherent.design/atlas atlas ingest /path/to/file.md
Ingest Directory (Recursive)
cd ~/production/atlas
bun run --filter @inherent.design/atlas atlas ingest /path/to/docs/ --recursive
Ingest Multiple Paths
cd ~/production/atlas
bun run --filter @inherent.design/atlas atlas ingest README.md src/index.ts docs/ -r
What gets ingested:
- Supported:
.md,.ts,.tsx,.js,.jsx,.json,.yaml,.qntm,.rs,.go,.py,.sh,.css,.html - Ignored:
node_modules,.git,dist,build,coverage,.atlas
Processing:
- Chunks text (768 tokens, 13% overlap) for semantic coherence
- Embeds with Voyage-3-large (1024-dim)
- Stores in Qdrant with dual-indexing (semantic QNTM keys + temporal timestamps)
- Preserves original text for future consolidation
Searching Context
Retrieve relevant context semantically:
Basic Search
cd ~/production/atlas
bun run --filter @inherent.design/atlas atlas search "typescript patterns"
Limited Results
cd ~/production/atlas
bun run --filter @inherent.design/atlas atlas search "memory consolidation" --limit 10
Temporal Filtering (Since Date)
cd ~/production/atlas
bun run --filter @inherent.design/atlas atlas search "sleep patterns" --since "2025-12-25"
Chronological Timeline
cd ~/production/atlas
bun run --filter @inherent.design/atlas atlas timeline --since "2025-12-01"
When to Use This Skill
Use Atlas when:
- User asks to "remember this across sessions"
- Project context is too large for single session
- User wants to search previous work/decisions
- Documentation needs to be queryable
- Building on previous research or code
Examples:
- "Remember the API architecture we discussed"
- "What did we decide about the database schema?"
- "Find all mentions of authentication patterns"
- "Ingest all the .atlas research files"
Architecture
Built on .atlas research (Steps 1-4 + Sleep Patterns):
Stack:
- Voyage-3-large embeddings (1024-dim, 9.74% better than OpenAI)
- Qdrant HNSW index (M=16, int8 quantization, 4x compression)
- RecursiveCharacterTextSplitter (semantic boundaries)
- Dual-indexing (QNTM semantic keys + RFC 3339 timestamps)
Production Config (from Step 3 research):
- Recall@10: >0.98
- Latency: 10-50ms (p95)
- Memory: 1.4GB RAM + 5GB disk per 1M vectors
Technical Details
For implementation details, see:
- docs/architecture.md - Complete technical architecture
Packages:
@inherent.design/atlas-core- Core library (embeddings, storage, search)@inherent.design/atlas- Command-line interface@inherent.design/atlas-mcp- MCP server for Claude Code integration
Expert Next.js App Router
Developpement
Un skill qui transforme Claude en expert Next.js App Router.
Générateur de README
Developpement
Crée des README.md professionnels et complets pour vos projets.
Rédacteur de Documentation API
Developpement
Génère de la documentation API complète au format OpenAPI/Swagger.