The skills layer for AI agents

Skills to the
power of n.

2nth builds the skills layer for AI agents — structured, agent-loadable knowledge that lets businesses deploy AI that compounds instead of running pilots that stall.

One knowledge tree. Read by humans, loaded by agents, operated as a system. Built in Johannesburg for businesses that need AI grounded in their domain — and their regulations.

35+ live skill nodes 12 domains Agent Skills native (Claude Code, Claude.ai, API) Built in Johannesburg 🇿🇦
01 · The problem

Most AI doesn't compound.

Most organisations are running disconnected pilots — chatbots, copilots, one-off workflows. Activity without progress: experiments that don't connect, don't improve, and don't reuse what came before.

The fix isn't another tool. It's a harness: structured skills your agents load, run, and improve — instead of headcount-shaped AI bolted onto org charts.

harness, not headcount

02 · Three doors

One tree. Three ways in.

Customers

Build with us

We map your work into outcomes, roles, skills, and tasks — then deploy agents into your actual environment. CRM, ERP, data, comms. Not a sandbox.

  • Discovery → skill mapping → working agents in your stack
  • Grounded in the Knowledge Tree: your agents start domain-aware, not generic
  • SA regulatory context built in: POPIA, FSCA, ICASA where it matters
Developers

Load the tree

Every skill node is structured context your agent can load today. Agent Skills format — native in Claude Code, Claude.ai, and the API.

  • Browse 12 domains of agent-loadable explainers and skill nodes
  • Copy-paste a branch into your agent's context
  • Open format: SKILL.md + progressive disclosure, no lock-in
Partners

Put your IP on the tree

Your domain expertise, operationalized as a private branch — co-branded nodes, agent-ready context packages, institutional knowledge that compounds instead of walking out the door.

  • Private skills-tree branches under your brand
  • Construction is already a live branch — deep domain IP shipped as agent-ready nodes (openBIM, IFC, JBCC/NEC4, NHBRC)
  • Partner intake is a real form, not a mailto link
03 · The tree

The Knowledge Tree is the product.

Twelve domains. One tree. Every node serves two readers — the human learning and the agent operating. This isn't a content library; it's the context layer your agents run on.

AgentsLive TechnologyLive BusinessLive DataLive PeopleLive DesignLive ConstructionLive
FinanceComing
LegalComing
HealthcareComing
IoTComing
EducationComing
04 · For developers

The format is real.

Skill nodes are plain SKILL.md with progressive disclosure — load a branch into your agent's context and it starts domain-aware. No SDK, no lock-in.

Point Claude Code, Claude.ai, or the API at a node and it reads like documentation and loads like context.

Browse all 12 domains →
  load-a-skill.sh
# Point your agent at a live skill node
curl https://know.2nth.ai/explainers/agents/skills.html

# …or clone the public skills tree into Claude Code
git clone https://github.com/2nth-ai/skills

# a node is just SKILL.md + progressive disclosure:
---
name: agent-skills
description: Author and load Agent Skills that
  Claude reads as docs and loads as context.
domain: agents
---
# Agent Skills
A skill is structured, agent-loadable knowledge…
05 · How it works

Map. Structure. Deploy. Compound.

01

Map

We turn your work into outcomes, roles, skills, and tasks.

02

Structure

Each skill becomes an agent-loadable node on the tree.

03

Deploy

Agents run those skills in your real stack — CRM, ERP, data, comms.

04

Compound

Every run sharpens the skills the next run loads.

Every run generates data. Data sharpens skills. Skills compound across the tree. That's the 2ⁿ effect — scale by capability, not headcount.

06 · How we earn trust

We audit our own claims.

Validation memo

The validation memo.

We published a cross-page audit correcting three of our own overstated SA-residency claims, with primary sources. That's the standard every node on the tree is held to.

Read the SA LLM Residency validation memo →
Case write-up

Proposal generation, rebuilt as skills.

A professional-services team turned a manual proposal process into a mapped skill an agent runs in their own stack.

3 daysbefore
40 minafter

Anonymized at the client's request · specifics on a call

07 · Positioning

Built in Johannesburg. Grounded in the rules that apply here.

POPIA-aware data handling. FSCA and SARB context in finance nodes. NHBRC and JBCC in construction. ICASA in IoT. Load-shedding-resilient edge patterns. AI for African businesses can't be a US template with the logo swapped — the tree encodes the local layer.

08 · Get in touch

Start with a conversation.

Customer, developer, or partner — tell us which door you're coming through and we'll route you to the right next step.

No spam. We respond within one business day.