Workflows

I ship with traditional full-stack workflows that follow proven engineering best practices — clear architecture, testing, reviews, and durable delivery habits — and layer agentic workflows on top when they amplify that craft.

01

Project configuration

Rules, AGENTS.md, slash commands, skills, and subagents so agents follow the repo’s standards — whether that’s Cursor, Claude Code, Codex, or another runtime.

02

Event-driven & always-on

Automations and always-on agentic workflows triggered by configured events — useful when work should continue without a constant interactive session.

03

Agents in products

Embedding agents into web apps with SDKs and APIs — Cursor SDK and similar surfaces — when the product needs agent capabilities, not just the editor or CLI.

04

MCP tool access

Configuring MCP servers so agents can reach databases, browsers, internal APIs, and other tools beyond the local codebase.

05

Institutional knowledgebases

Curating durable project docs, decision records, and domain references so agents inherit shared context instead of rediscovering it each session.

06

Hooks & quality gates

Wiring hooks and gates so agents hit lint, tests, or policy checks before and after turns — enforcement that keeps institutional standards from being optional.

Open source

Works in progress.

zanuka on GitHub
In developmentzanuka/nocciolo

Nocciolo

Italian for kernel / core — the durable core of project knowledge that agents inherit.

AI agents start every session cold — re-learning architecture decisions, coding standards, domain invariants, and the why behind the work from scattered docs and tribal knowledge. That wastes tokens, produces inconsistent output, and breaks long-running agentic workflows.

I’m building Nocciolo as a company brain configuration layer: it turns durable project knowledge — ADRs, READMEs, decision records — into a properly configured memory bank agents can retain and recall. By seeding banks likeHindsight, agents inherit shared context instead of rediscovering it every session.

  • Knowledge extraction — scans repos for durable knowledge while ignoring ephemeral noise.
  • Contextual seeding — configures missions and directives for memory banks, tuned for software projects.
  • MCP integration — emits Model Context Protocol snippets to wire memory into Cursor, Claude Code, and other agent harnesses.
  • Local-first governance — keeps sensitive architecture decisions under team control, versioned alongside the code.
  • Node.js / TypeScript CLI
  • Hindsight
  • MCP