Notre avis
Atlas ingère la documentation et le code dans une mémoire sémantique persistante avec Qdrant et Voyage pour une recherche contextuelle inter-sessions.
Points forts
- Recherche sémantique rapide et précise dans de grands volumes de fichiers
- Double indexation temporelle et sémantique pour une récupération flexible
- Intégration native avec Claude Code via MCP
- Ingestion récursive de répertoires avec filtrage intelligent
Limites
- Nécessite un serveur Qdrant local et une clé API Voyage
- Consommation mémoire et disque non négligeable avec beaucoup de vecteurs
- Pas de prise en charge des images ou fichiers binaires
Utilisez Atlas lorsque vous avez besoin de conserver et d'interroger du contexte de projet sur plusieurs sessions sans perdre d'informations.
Évitez Atlas pour des tâches ponctuelles avec du contexte minimal ou si vous ne pouvez pas maintenir l'infrastructure requise.
Analyse de sécurité
SûrThe skill uses specific bun commands for document ingestion and semantic search via an external embedding API without destructive or exfiltrating patterns. It does not contain malicious payloads, obfuscated code, or instructions to disable safety.
Aucun point d'attention détecté
Exemples
Remember the API architecture we discussed last week.Find all mentions of authentication patterns in our codebase.Ingest all the .atlas research files from the research directory.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
Priorisation de Tâches
Productivite
Priorise vos tâches avec les frameworks Eisenhower, ICE et RICE.
Generateur de Rapport Hebdomadaire
Productivite
Generez des rapports de statut hebdomadaires structures et concis.
Rapport de Daily Standup
Productivite
Génère des rapports de daily standup structurés et concis.