Pinocchio Development logo

Pinocchio Development

Organization
sendaifun
pinocchio-development

Comprehensive guide for building high-performance Solana programs using Pinocchio - the zero-dependency, zero-copy framework. Covers account validation, CPI patterns, optimization techniques, and migration from Anchor.

Overview

Publishersendaifun
Repositoryskills
Skill namepinocchio-development
Stars
128
Forks
81
Bundled files
7
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 7 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by sendaifun on GitHub. Read the source before you install it.

Installation

Install the Pinocchio Development AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/sendaifun/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/pinocchio-development .claude/skills/pinocchio-development
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Pinocchio Development in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Pinocchio Development on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Pinocchio Development is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Pinocchio Development Guide

Build blazing-fast Solana programs with Pinocchio - a zero-dependency, zero-copy framework that delivers 88-95% compute unit reduction and 40% smaller binaries compared to traditional approaches.

Overview

Pinocchio is Anza's minimalist Rust library for writing Solana programs without the heavyweight solana-program crate. It treats incoming transaction data as a single byte slice, reading it in-place via zero-copy techniques.

Performance Comparison

MetricAnchorNative (solana-program)Pinocchio
Token Transfer CU~6,000~4,500~600-800
Binary SizeLargeMediumSmall (-40%)
Heap AllocationRequiredRequiredOptional
DependenciesManySeveralZero*

*Only Solana SDK types for on-chain execution

When to Use Pinocchio

Use Pinocchio When:

  • Building high-throughput programs (DEXs, orderbooks, games)
  • Compute units are a bottleneck
  • Binary size matters (program deployment costs)
  • You need maximum control over memory
  • Building infrastructure (tokens, vaults, escrows)

Consider Anchor Instead When:

  • Rapid prototyping / MVPs
  • Team unfamiliar with low-level Rust
  • Complex account relationships
  • Need extensive ecosystem tooling
  • Audit timeline is tight (more auditors know Anchor)

Quick Start

1. Project Setup

toml
# Cargo.toml
[package]
name = "my-program"
version = "0.1.0"
edition = "2021"

[lib]
crate-type = ["cdylib", "lib"]

[features]
default = []
bpf-entrypoint = []

[dependencies]
pinocchio = "0.10"
pinocchio-system = "0.4"      # System Program CPI helpers
pinocchio-token = "0.4"       # Token Program CPI helpers
bytemuck = { version = "1.14", features = ["derive"] }

[profile.release]
overflow-checks = true
lto = "fat"
codegen-units = 1
opt-level = 3

2. Basic Program Structure

rust
use pinocchio::{
    account_info::AccountInfo,
    entrypoint,
    program_error::ProgramError,
    pubkey::Pubkey,
    ProgramResult,
};

// Declare entrypoint
entrypoint!(process_instruction);

pub fn process_instruction(
    program_id: &Pubkey,
    accounts: &[AccountInfo],
    instruction_data: &[u8],
) -> ProgramResult {
    // Route instructions by discriminator (first byte)
    match instruction_data.first() {
        Some(0) => initialize(accounts, &instruction_data[1..]),
        Some(1) => execute(accounts, &instruction_data[1..]),
        _ => Err(ProgramError::InvalidInstructionData),
    }
}

3. Account Definition with Bytemuck

rust
use bytemuck::{Pod, Zeroable};

// Single-byte discriminator for account type
pub const VAULT_DISCRIMINATOR: u8 = 1;

#[repr(C)]
#[derive(Clone, Copy, Pod, Zeroable)]
pub struct Vault {
    pub discriminator: u8,
    pub owner: [u8; 32],      // Pubkey as bytes
    pub balance: u64,
    pub bump: u8,
    pub _padding: [u8; 6],    // Align to 8 bytes
}

impl Vault {
    pub const LEN: usize = std::mem::size_of::<Self>();

    pub fn from_account(account: &AccountInfo) -> Result<&Self, ProgramError> {
        let data = account.try_borrow_data()?;
        if data.len() < Self::LEN {
            return Err(ProgramError::InvalidAccountData);
        }
        if data[0] != VAULT_DISCRIMINATOR {
            return Err(ProgramError::InvalidAccountData);
        }
        Ok(bytemuck::from_bytes(&data[..Self::LEN]))
    }

    pub fn from_account_mut(account: &AccountInfo) -> Result<&mut Self, ProgramError> {
        let mut data = account.try_borrow_mut_data()?;
        if data.len() < Self::LEN {
            return Err(ProgramError::InvalidAccountData);
        }
        Ok(bytemuck::from_bytes_mut(&mut data[..Self::LEN]))
    }
}

Instructions

Step 1: Define Account Validation

Create a struct to hold validated accounts:

rust
pub struct InitializeAccounts<'a> {
    pub vault: &'a AccountInfo,
    pub owner: &'a AccountInfo,
    pub system_program: &'a AccountInfo,
}

impl<'a> InitializeAccounts<'a> {
    pub fn parse(accounts: &'a [AccountInfo]) -> Result<Self, ProgramError> {
        let [vault, owner, system_program, ..] = accounts else {
            return Err(ProgramError::NotEnoughAccountKeys);
        };

        // Validate owner is signer
        if !owner.is_signer() {
            return Err(ProgramError::MissingRequiredSignature);
        }

        // Validate system program
        if system_program.key() != &pinocchio_system::ID {
            return Err(ProgramError::IncorrectProgramId);
        }

        Ok(Self {
            vault,
            owner,
            system_program,
        })
    }
}

Step 2: Implement Instruction Handler

rust
use pinocchio_system::instructions::CreateAccount;

pub fn initialize(accounts: &[AccountInfo], data: &[u8]) -> ProgramResult {
    let ctx = InitializeAccounts::parse(accounts)?;

    // Derive PDA
    let (pda, bump) = Pubkey::find_program_address(
        &[b"vault", ctx.owner.key().as_ref()],
        &crate::ID,
    );

    // Verify PDA matches
    if ctx.vault.key() != &pda {
        return Err(ProgramError::InvalidSeeds);
    }

    // Create account via CPI
    let space = Vault::LEN as u64;
    let rent = pinocchio::sysvar::rent::Rent::get()?;
    let lamports = rent.minimum_balance(space as usize);

    CreateAccount {
        from: ctx.owner,
        to: ctx.vault,
        lamports,
        space,
        owner: &crate::ID,
    }
    .invoke_signed(&[&[b"vault", ctx.owner.key().as_ref(), &[bump]]])?;

    // Initialize account data
    let vault = Vault::from_account_mut(ctx.vault)?;
    vault.discriminator = VAULT_DISCRIMINATOR;
    vault.owner = ctx.owner.key().to_bytes();
    vault.balance = 0;
    vault.bump = bump;

    Ok(())
}

Entrypoint Options

Pinocchio provides three entrypoint macros with different trade-offs:

1. Standard Entrypoint (Recommended for most cases)

rust
use pinocchio::entrypoint;

entrypoint!(process_instruction);
  • Sets up heap allocator
  • Configures panic handler
  • Deserializes accounts automatically

2. Lazy Entrypoint (Best for single-instruction programs)

rust
use pinocchio::lazy_entrypoint;

lazy_entrypoint!(process_instruction);

pub fn process_instruction(mut context: InstructionContext) -> ProgramResult {
    // Accounts parsed on-demand
    let account = context.next_account()?;
    let data = context.instruction_data();
    Ok(())
}
  • Defers parsing until needed
  • Best CU savings for simple programs
  • 80-87% CU reduction in memo program benchmarks

3. No Allocator (Maximum optimization)

rust
use pinocchio::{entrypoint, no_allocator};

no_allocator!();
entrypoint!(process_instruction);
  • Disables heap entirely
  • Cannot use String, Vec, Box
  • Best for statically-sized operations

CPI Patterns

System Program CPI

rust
use pinocchio_system::instructions::{CreateAccount, Transfer};

// Create account
CreateAccount {
    from: payer,
    to: new_account,
    lamports: rent_lamports,
    space: account_size,
    owner: &program_id,
}.invoke()?;

// Transfer SOL
Transfer {
    from: source,
    to: destination,
    lamports: amount,
}.invoke()?;

// Transfer with PDA signer
Transfer {
    from: pda_account,
    to: destination,
    lamports: amount,
}.invoke_signed(&[&[b"vault", owner.as_ref(), &[bump]]])?;

Token Program CPI

rust
use pinocchio_token::instructions::{Transfer, MintTo, Burn};

// Transfer tokens
Transfer {
    source: from_token_account,
    destination: to_token_account,
    authority: owner,
    amount: token_amount,
}.invoke()?;

// Mint tokens (with PDA authority)
MintTo {
    mint: mint_account,
    token_account: destination,
    authority: mint_authority_pda,
    amount: mint_amount,
}.invoke_signed(&[&[b"mint_auth", &[bump]]])?;

Custom CPI (Third-party programs)

rust
use pinocchio::{
    instruction::{AccountMeta, Instruction},
    program::invoke,
};

// Build instruction manually
let accounts = vec![
    AccountMeta::new(*account1.key(), false),
    AccountMeta::new_readonly(*account2.key(), true),
];

let ix = Instruction {
    program_id: &external_program_id,
    accounts: &accounts,
    data: &instruction_data,
};

invoke(&ix, &[account1, account2])?;

Account Validation Patterns

Pattern 1: TryFrom Trait

rust
pub struct DepositAccounts<'a> {
    pub vault: &'a AccountInfo,
    pub owner: &'a AccountInfo,
    pub system_program: &'a AccountInfo,
}

impl<'a> TryFrom<&'a [AccountInfo]> for DepositAccounts<'a> {
    type Error = ProgramError;

    fn try_from(accounts: &'a [AccountInfo]) -> Result<Self, Self::Error> {
        let [vault, owner, system_program, ..] = accounts else {
            return Err(ProgramError::NotEnoughAccountKeys);
        };

        // Validations
        require!(owner.is_signer(), ProgramError::MissingRequiredSignature);
        require!(vault.is_writable(), ProgramError::InvalidAccountData);

        Ok(Self { vault, owner, system_program })
    }
}

// Usage
let ctx = DepositAccounts::try_from(accounts)?;

Pattern 2: Builder Pattern

rust
pub struct AccountValidator<'a> {
    account: &'a AccountInfo,
}

impl<'a> AccountValidator<'a> {
    pub fn new(account: &'a AccountInfo) -> Self {
        Self { account }
    }

    pub fn is_signer(self) -> Result<Self, ProgramError> {
        if !self.account.is_signer() {
            return Err(ProgramError::MissingRequiredSignature);
        }
        Ok(self)
    }

    pub fn is_writable(self) -> Result<Self, ProgramError> {
        if !self.account.is_writable() {
            return Err(ProgramError::InvalidAccountData);
        }
        Ok(self)
    }

    pub fn has_owner(self, owner: &Pubkey) -> Result<Self, ProgramError> {
        if self.account.owner() != owner {
            return Err(ProgramError::IllegalOwner);
        }
        Ok(self)
    }

    pub fn build(self) -> &'a AccountInfo {
        self.account
    }
}

// Usage
let owner = AccountValidator::new(&accounts[0])
    .is_signer()?
    .is_writable()?
    .build();

Pattern 3: Macro-based Validation

rust
macro_rules! require {
    ($cond:expr, $err:expr) => {
        if !$cond {
            return Err($err);
        }
    };
}

macro_rules! require_signer {
    ($account:expr) => {
        require!($account.is_signer(), ProgramError::MissingRequiredSignature)
    };
}

macro_rules! require_writable {
    ($account:expr) => {
        require!($account.is_writable(), ProgramError::InvalidAccountData)
    };
}

PDA Operations

Deriving PDAs

rust
use pinocchio::pubkey::Pubkey;

// Find PDA with bump
let (pda, bump) = Pubkey::find_program_address(
    &[b"vault", user.key().as_ref()],
    program_id,
);

// Create PDA with known bump (cheaper)
let pda = Pubkey::create_program_address(
    &[b"vault", user.key().as_ref(), &[bump]],
    program_id,
)?;

PDA Signing for CPI

rust
// Single seed set
let signer_seeds = &[b"vault", owner.as_ref(), &[bump]];

Transfer {
    from: vault_pda,
    to: destination,
    lamports: amount,
}.invoke_signed(&[signer_seeds])?;

// Multiple PDA signers
let signer1 = &[b"vault", owner.as_ref(), &[bump1]];
let signer2 = &[b"authority", &[bump2]];

invoke_signed(&ix, &accounts, &[signer1, signer2])?;

Data Serialization

Fixed-Size with Bytemuck (Recommended)

rust
#[repr(C)]
#[derive(Clone, Copy, Pod, Zeroable)]
pub struct GameState {
    pub discriminator: u8,
    pub player: [u8; 32],
    pub score: u64,
    pub level: u8,
    pub _padding: [u8; 6],
}

// Zero-copy read
let state: &GameState = bytemuck::from_bytes(&data);

// Zero-copy write
let state: &mut GameState = bytemuck::from_bytes_mut(&mut data);

Variable-Size with Borsh

rust
use borsh::{BorshDeserialize, BorshSerialize};

#[derive(BorshSerialize, BorshDeserialize)]
pub struct Metadata {
    pub name: String,
    pub symbol: String,
    pub uri: String,
}

// Deserialize (allocates)
let metadata = Metadata::try_from_slice(data)?;

// Serialize
let mut buffer = Vec::new();
metadata.serialize(&mut buffer)?;

Manual Parsing (Maximum control)

rust
pub fn parse_u64(data: &[u8]) -> Result<u64, ProgramError> {
    if data.len() < 8 {
        return Err(ProgramError::InvalidInstructionData);
    }
    Ok(u64::from_le_bytes(data[..8].try_into().unwrap()))
}

pub fn parse_pubkey(data: &[u8]) -> Result<Pubkey, ProgramError> {
    if data.len() < 32 {
        return Err(ProgramError::InvalidInstructionData);
    }
    Ok(Pubkey::new_from_array(data[..32].try_into().unwrap()))
}

IDL Generation with Shank

Since Pinocchio doesn't auto-generate IDLs, use Shank:

rust
use shank::{ShankAccount, ShankInstruction};

#[derive(ShankAccount)]
pub struct Vault {
    pub owner: Pubkey,
    pub balance: u64,
}

#[derive(ShankInstruction)]
pub enum ProgramInstruction {
    #[account(0, writable, signer, name = "vault")]
    #[account(1, signer, name = "owner")]
    #[account(2, name = "system_program")]
    Initialize,

    #[account(0, writable, name = "vault")]
    #[account(1, signer, name = "owner")]
    Deposit { amount: u64 },
}

Generate IDL:

bash
shank idl -o idl.json -p src/lib.rs

Guidelines

  1. Always use single-byte discriminators for instructions and accounts
  2. Prefer bytemuck over Borsh for fixed-size data
  3. Use lazy_entrypoint! for single-instruction programs
  4. Validate all accounts before processing
  5. Use invoke_signed for PDA-owned account operations
  6. Add padding to align structs to 8 bytes
  7. Test with solana-program-test or Bankrun

Files in This Skill

pinocchio-development/
├── SKILL.md                           # This file
├── scripts/
│   ├── scaffold-program.sh            # Project generator
│   └── benchmark-cu.sh                # CU benchmarking
├── resources/
│   ├── account-patterns.md            # Validation patterns
│   ├── cpi-reference.md               # CPI quick reference
│   ├── optimization-checklist.md      # Performance tips
│   └── anchor-comparison.md           # Side-by-side comparison
├── examples/
│   ├── counter/                       # Basic counter program
│   ├── vault/                         # PDA vault with deposits
│   ├── token-operations/              # Token minting/transfers
│   └── transfer-hook/                 # Token-2022 hook
├── templates/
│   └── program-template.rs            # Starter template
└── docs/
    ├── migration-from-anchor.md       # Anchor migration guide
    └── edge-cases.md                  # Gotchas and solutions

Performance Benchmarks (2025)

Latest benchmarks demonstrate Pinocchio's efficiency:

ProgramAnchor CUPinocchio CUReduction
Token Transfer~6,000~600-80088-95%
Memo Program~650~10883%
Counter~800~10487%

Assembly implementation: 104 CU, Pinocchio: 108 CU, Basic Anchor: 649 CU

SDK Roadmap (Anza Plans)

The Anza team has announced plans for SDK v3:

Coming Improvements

  • Unified Base Types: Reusable types across Anchor and Pinocchio
  • New Serialization Library: Zero-copy, simpler enums, variable-length types
  • ATA Program Optimization: Pinocchio-optimized Associated Token Account
  • Token22 Optimization: Full Token Extensions support with minimal CU usage

Integration Progress

  • Pinocchio types are being integrated into the core Solana SDK
  • Improved interoperability between Anchor and Pinocchio programs

Notes

  • Pinocchio is unaudited - use with caution in production
  • Version 0.10.x is current (latest: pinocchio = "0.10")
  • pinocchio-system = "0.4" and pinocchio-token = "0.4" for CPI helpers
  • Token-2022 support via pinocchio-token is under active development
  • For client generation, use Codama with your Shank-generated IDL
  • Maintained by Anza (Solana Agave client developers)

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Pinocchio Development AI skill do?

Comprehensive guide for building high-performance Solana programs using Pinocchio - the zero-dependency, zero-copy framework. Covers account validation, CPI patterns, optimization techniques, and migration from Anchor.

Why use Pinocchio Development on TypingMind?

Because you install it once and use it with any model. Pinocchio Development is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Pinocchio Development in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/sendaifun/skills/tree/main/skills/pinocchio-development. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Pinocchio Development?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Pinocchio Development?

As many as you like. As long as a model supports skills, you can use Pinocchio Development with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Pinocchio Development AI skill free?

Yes. It is published on GitHub by sendaifun under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇