Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/nirholas/three.ws/giza-zkml-visualizationnpx skills add nirholas/three.ws --skill giza-zkml-visualizationgit clone --depth 1 https://github.com/nirholas/three.wsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/nirholas/three.ws/giza-zkml-visualization)<a href="https://agentmods.dev/skills/nirholas/three.ws/giza-zkml-visualization"><img src="https://agentmods.dev/badge/skills/nirholas/three.ws/giza-zkml-visualization.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00029 | $0.00911 |
| Opus 5 | $0.00015 | $0.00456 |
| Sonnet 5 | $0.00006 | $0.00182 |
| Haiku 4.5 | $0.00003 | $0.00091 |
Grade A, and why
giza-zkml-visualization scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Giza ZKML Agent Visualization
Overview
Giza is a protocol for deploying verifiable AI agents on-chain using Zero-Knowledge Machine Learning (ZKML). This skill covers how Giza agents work, proof verification, and how to interpret the visualization dashboards in SperaxOS.
Key Concepts
Zero-Knowledge Machine Learning (ZKML)
ZKML allows AI model inferences to be verified on-chain without revealing the model weights or input data. This creates trustless AI — anyone can verify that a model produced a specific output from a specific input, without needing to trust the model operator.
Giza Agents
On-chain AI agents deployed via the Giza protocol. Each agent:
- Runs a specific ML model (e.g., price prediction, risk scoring)
- Generates ZKML proofs for each inference
- Can be deployed on multiple chains (Starknet, Ethereum, Arbitrum, etc.)
- Has a verifiable track record of accuracy and performance
Proof Systems
Giza supports multiple proof backends:
- Cairo — Native to Starknet, fastest proving time for StarkNet deployments
- Noir — Aztec Labs' DSL for ZK circuits, good for Ethereum L1
- RISC Zero — General-purpose zkVM, supports any computation
SperaxOS Visualization Tool
Agent Overview Dashboard
Shows all deployed Giza agents with:
- Status indicators — Green (active), Yellow (pending), Red (inactive)
- Summary stats — Total active agents, inference count, proof count
- Agent rows — Name, chain, inference count, proof count per agent
Proof History View
Visualizes ZKML proof verification pipeline:
- Verification rate bar — Color-coded segments showing verified/pending/failed ratios
- Proof rows — Individual proofs with status badge, proof type (Cairo/Noir/RISC0), chain, duration, and timestamp
- Use this to monitor proof verification health and identify failures
Model Performance Dashboard
Detailed metrics for a specific AI model:
- Accuracy gauge — Green >95%, Yellow 85-95%, Red <85%
- Inference latency — Average time per inference
- Proof generation time — Average time to generate ZKML proof
- Inference volume sparkline — 30-day trend of inference activity
- Chain deployment tags — Which chains the model is deployed on
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 88 lines · 29 tokens per session scan A a899964c4316
giza-zkml-visualization is a skill published in the GitHub repository nirholas/three.ws (110 stars, last pushed today), licensed Apache-2.0. It adds 29 tokens to every session and 911 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
blockchain-cross-chain
Cross-chain protocols, IBC, LayerZero, Wormhole, Axelar, CCIP, bridges, atomic composability, shared sequencer, cross-chain message passing. Covers trust models (light clients, external validators, ZK proofs), bridge security, token representation (canonical, wrapped, native), relayer economics, and cross-chain…
fx-neural-net
A pulsing neural network — an AI backdrop. A bold, canvas-based effect from Open Design's html-ppt fx pack — for slides and launch videos. Guards prefers-reduced-motion (does not start). One per screen; on web use sparingly.
bitquery-automation
Automate Bitquery tasks via Rube MCP (Composio). Always search tools first for current schemas.
blockchain-management
Use this skill when asked about blockchain project management, DAO governance, multi-sig operations, treasury management, tokenomics design, and web3 project methodology. Languages: Solidity, TypeScript, Python. Covers DAO governance frameworks (Compound Governor, Aave, Snapshot, Tally), multi-sig wallet operations…
write-contracts
Generates secure Aptos Move V2 smart contracts with Object model, Digital Asset integration, security patterns, and storage type guidance. Includes comprehensive storage decision framework for optimal data structure selection. Triggers on: 'write contract', 'create NFT collection', 'build marketplace', 'implement…
threejs-docs
Comprehensive Three.js reference covering core API (objects, cameras, lights, materials, geometries, renderers, scenes, textures, math, animation, audio, loaders, helpers, nodes), all addons (controls, postprocessing, loaders, exporters, geometries, shaders, WebXR, physics), TSL (Three.js Shading Language) functions…