nima-coreNeural Integrated Memory Architecture — Persistent memory, emotional intelligence, and semantic recall for AI agents. Memory pruner, VADER affect, 5 embeddin...
Install via ClawdBot CLI:
clawdbot install dmdorta1111/nima-coreRequires:
Grade Good — based on market validation, documentation quality, package completeness, maintenance status, and authenticity signals.
Accesses sensitive credential files or environment variables
/etc/passwdPotentially destructive shell commands in tool definitions
rm -rf ~Calls external URL not in known-safe list
https://github.com/lilubot/nima-coreUses known external API (expected, informational)
api.anthropic.comGenerated Mar 1, 2026
Deploy NIMA Core to enable a customer support AI agent with persistent memory and emotional intelligence. The agent can recall past interactions, adapt responses based on user sentiment, and consolidate insights from support tickets to improve future interactions, enhancing customer satisfaction and reducing repeat issues.
Use NIMA Core to create a therapeutic AI companion that tracks emotional states and memories over time. It can surface emotionally resonant memories for reflection, analyze patterns in user conversations, and provide personalized support, helping users manage mental well-being through consistent, empathetic interactions.
Implement NIMA Core in a research environment where multiple AI agents collaborate via the Hive Mind feature. They share memory databases to pool insights, perform precognitive pattern mining on data trends, and distill complex information into semantic gists, accelerating academic or business research projects.
Integrate NIMA Core into an educational AI tutor that uses persistent memory to track student progress and emotional engagement. It can recall previous lessons, adapt teaching styles based on affect analysis, and consolidate learning patterns to provide tailored feedback, improving educational outcomes.
Leverage NIMA Core for an enterprise AI that manages organizational knowledge with memory capture and pruning. It can filter noise from meetings, distill key insights into summaries, and enable semantic recall for employees, streamlining information retrieval and decision-making processes.
Offer NIMA Core as a free, open-source package with basic features, then generate revenue through paid premium support, customization services, and enterprise-level consulting. This model attracts developers and businesses seeking reliable assistance and tailored integrations.
Provide a SaaS platform where users subscribe to access enhanced cloud-based features like Hive Mind sharing, advanced embeddings via external APIs, and managed Redis pub/sub. This model targets organizations needing scalable, networked AI capabilities without local setup overhead.
License NIMA Core to large enterprises for integration into proprietary AI systems, with revenue from licensing fees, training workshops, and ongoing maintenance. This model caters to companies requiring secure, on-premises deployment with full control over data and customization.
💬 Integration Tip
Start with the default local embeddings to avoid external calls, then gradually enable optional features like Hive Mind or cloud embeddings based on specific use-case needs to minimize complexity.
Scored May 30, 2026
AI Analysis
The skill's design prioritizes local operation and opt-in external calls, with no evidence of hidden instructions, credential harvesting, or obfuscation. The signals found (like accessing /etc/passwd or using `rm -rf`) are likely false positives from generic rule matching, not actual malicious behavior in the skill's code. External API usage is documented, optional, and consistent with the skill's stated purpose.
Audited Apr 16, 2026 · audit v1.0
Search and analyze your own session logs (older/parent conversations) using jq.
Typed knowledge graph for structured agent memory and composable skills. Use when creating/querying entities (Person, Project, Task, Event, Document), linkin...
Enable and configure Moltbot/Clawdbot memory search for persistent context. Use when setting up memory, fixing "goldfish brain," or helping users configure memorySearch in their config. Covers MEMORY.md, daily logs, and vector search setup.
Local memory management for agents. Compression detection, auto-snapshots, and semantic search. Use when agents need to detect compression risk before memory loss, save context snapshots, search historical memories, or track memory usage patterns. Never lose context again.
You MUST use this for gathering contexts before any work. This is a Knowledge management for AI agents. Use `brv` to store and retrieve project patterns, dec...
Audit, clean, and optimize Clawdbot's vector memory (LanceDB). Use when memory is bloated with junk, token usage is high from irrelevant auto-recalls, or setting up memory maintenance automation.