Automated summary
Atlas ingests project documentation and code into persistent semantic memory using Qdrant and Voyage embeddings for cross-session context retrieval.
Strengths
- Fast, accurate semantic search across large file collections
- Dual temporal and semantic indexing for flexible retrieval
- Native integration with Claude Code via MCP
- Recursive directory ingestion with smart filtering
Limitations
- Requires a local Qdrant instance and a Voyage API key
- Non-trivial memory and disk usage with many vectors
- Does not support images or binary files
Use Atlas when you need to retain and query project context across sessions without losing information.
Avoid Atlas for one-off tasks with minimal context or if you cannot maintain the required infrastructure.
Security analysis
SafeThe 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.
No concerns found
How this analysis is madeExamples
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
From the file to a first result
The download contains exactly the content displayed on this page. This copy may differ from the latest version in the source repository.
View the source and supporting filesThis download contains SKILL.md only. Any scripts, references, templates and dependencies are not included. Get the required files from the source, preserving their directory structure, before continuing.
1. Download the file
Download SKILL.mdFrom the root of a test project, save the file as .skill-downloads/atlas-persistent-semantic-memory-mm5t8apq/SKILL.md, or use the command below. This folder is for inspecting the skill before installation.
Command for macOS/Linux or WSL: requires a POSIX shell and curl. On Windows without WSL, use the download button and create the folder manually.
mkdir -p '.skill-downloads/atlas-persistent-semantic-memory-mm5t8apq' && curl --fail --show-error --location 'https://skills-guides.com/api/skills/atlas-persistent-semantic-memory-mm5t8apq/download' --output '.skill-downloads/atlas-persistent-semantic-memory-mm5t8apq/SKILL.md.part' && mv '.skill-downloads/atlas-persistent-semantic-memory-mm5t8apq/SKILL.md.part' '.skill-downloads/atlas-persistent-semantic-memory-mm5t8apq/SKILL.md'2. Check prerequisites
Read SKILL.md before using it. If it references scripts or external services, inspect them and prepare the required access in your test environment.
Tools declared by the author (check availability in your agent): Bash(bun:*)
Original excerpt: 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
The platforms on this page are catalog indications. The paths below follow tool documentation; they do not prove this skill has been run in those tools. No minimum tool version is confirmed for this listing.
3. Load the skill
After inspection, copy the complete folder into your project using one of the commands below, then open a new tool session. The command stops if a folder with the same name already exists: compare the files before replacing it.
Claude Code
test -f '.skill-downloads/atlas-persistent-semantic-memory-mm5t8apq/SKILL.md' && test -s '.skill-downloads/atlas-persistent-semantic-memory-mm5t8apq/SKILL.md' && mkdir -p '.claude/skills' && test ! -e '.claude/skills/atlas' && cp -R '.skill-downloads/atlas-persistent-semantic-memory-mm5t8apq' '.claude/skills/atlas'OpenSkills
OpenSkills must already be installed. The local install/read procedure is checked with OpenSkills 1.5.0, which requires Node.js ≥ 20.6.0. Catalog slugs are not resolved automatically.
Official OpenSkills installationopenskills install './.skill-downloads/atlas-persistent-semantic-memory-mm5t8apq'openskills read 'atlas-persistent-semantic-memory-mm5t8apq'Reading should display the installed file content and its base directory. It does not run the skill or install its dependencies.
For an agent that reads AGENTS.md, then sync the index. This command changes AGENTS.md: review the diff before using it.
openskills syncManual method or another tool: attach SKILL.md to a conversation, or open it in an agent that can read files. Include any required supporting resources. No universal folder is recognized by every tool.
4. Try a first task
Use a short, synthetic input: an API endpoint, a small document or an interface brief. In a new conversation, attach the file or give the agent its path, then adapt this request:
Read .skill-downloads/atlas-persistent-semantic-memory-mm5t8apq/SKILL.md and apply its instructions to this example: [describe a test input]. Identify any missing files, dependencies or access. Produce the result requested by the skill without inventing data or running unreviewed scripts.Expected result: output matching the skill instructions, or a precise list of missing prerequisites. A read confirmation alone does not validate a first use. If an external resource is unavailable, that path remains untested.
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