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Niraven/README.md

Nino "Niraven" Amor

I build practical AI systems that can be explained to operators, shipped by teams, and trusted with real tools.

My lane sits between product, operations, community, and implementation: I map messy workflows, turn them into usable agent systems, and help teams adopt them without pretending the demo is the product.

Currently focused on agent tooling, MCP security, procedural memory, workflow automation, and SEA-focused AI products. Former Pokee AI contractor, where my public-safe scope was community operations, product education, onboarding support, workflow documentation, public demos, and adoption materials around agent workflows.

Current Direction

  • Agent systems in production: building tools, workflows, and handoff patterns that survive week two, not just launch day.
  • MCP security and reliability: making agent tool calls observable, rate-limited, auditable, and safer by default.
  • Agent memory: exploring procedural memory so agents can reuse workflows instead of relearning them every session.
  • AI product and adoption: translating technical agent capabilities into onboarding, templates, docs, and user-facing product narratives.
  • SEA automation products: turning local operational pain into small, shippable software and service offers.

Featured Work

Procedural memory for AI agents.

Myelin observes repeated agent workflows, clusters action sequences, promotes reusable procedures, and exposes learned behavior through MCP. It is built for agents that should get better at the work they actually do.

Local-first firewall and black-box recorder for MCP-connected agents.

  • Blocks poisoned MCP tool descriptions before agents can see them
  • Detects descriptor drift after a trusted baseline
  • Rate-limits runaway tool calls
  • Holds risky actions for approval
  • Produces redacted audit logs and share-safe run reports

This is the core thesis I am building around: production agents need infrastructure around tool access, not only better prompts.

Philippines-focused BidOps monitor for public procurement opportunities.

It started as a scraper and evolved into a product angle: help suppliers decide which government bids are worth pursuing, reviewing, or skipping before wasting time on weak-fit opportunities.

Readiness scorecards for MCP apps and partner launches.

This project reflects how I think about launches: not only "does it work?" but whether the product, docs, positioning, integration path, and user-facing proof are ready enough for real adoption.

Predecessor to Myelin.

Sigil explored local-first agent memory, SQLite-backed knowledge graphs, personas, orchestration, and multi-agent coordination. Myelin is the sharper successor focused on procedural memory.

Product And Operator Work

I am not only interested in technical demos. I care about adoption, positioning, and the path from prototype to operational value.

  • Former Pokee AI contractor across community operations, product education, onboarding support, public workflow demos, documentation, and adoption materials.
  • AI strategy and operations work across portfolio workflows, automation standards, and internal AI rollout.
  • Salesforce AgentForce and workflow automation experience across CRM, support, SOPs, dashboards, and low-code systems.
  • Community, onboarding, and launch work that turns AI capabilities into something users can understand and adopt.

What I Am Good At

Technical: Python, TypeScript, MCP, local-first tooling, SQLite, RAG, LLM workflows, agent orchestration, automation pipelines, security-minded tool design.

Product: AI product strategy, roadmapping, user onboarding, workflow mapping, GTM experiments, community feedback loops, founder/SMB automation offers.

Operations: turning ambiguous processes into tools, dashboards, SOPs, training materials, and repeatable execution systems.

How To Read This GitHub

This account is a build trail. Some repos are polished products, some are research prototypes, and some are product experiments. The connective tissue is practical AI:

  • Can an agent use this safely?
  • Can a human operator understand it?
  • Can a team adopt it without magic?
  • Can it become public proof, a product, or a client offer?

Links

Pinned Loading

  1. mcp-gateway mcp-gateway Public

    Local-first firewall and black-box run reporter for MCP tool calls: block poisoned tools, detect drift, rate-limit agents, and audit runs.

    TypeScript

  2. myelin myelin Public

    Agents should not relearn workflows. Myelin turns repeated agent behavior into reusable procedures via MCP.

    Python 1

  3. sigil-memory sigil-memory Public

    Predecessor to Myelin. Broad local-first agent memory prototype; new procedural-memory work lives at github.com/Niraven/myelin.

    Python

  4. openclaw/openclaw openclaw/openclaw Public

    Your own personal AI assistant. Any OS. Any Platform. The lobster way. 🦞

    TypeScript 384k 80.6k

  5. hermes-agent hermes-agent Public

    Forked from NousResearch/hermes-agent

    The agent that grows with you

    Python