Southeast Asia
Payroll Knowledge Base

Countries covered

Use it in code

# Install
npm install philippines-payroll-data

# Import typed rate tables for any country
import { sss2025, sgCpf, myContributions, thSocialSecurity } from 'philippines-payroll-data';

# Philippines — SSS bracket lookup
sss2025.brackets.find(b => b.msc === 25000).employee; // 1250

# Singapore — employee CPF rate at age 35, salary $5,000
sgCpf.rates.find(r => r.age_group === '35_45').employee_pct; // 20

# Or fetch the JSON directly — no install needed
const sss = await fetch('https://cdn.jsdelivr.net/npm/philippines-payroll-data/data/sss_2025.json').then(r => r.json());

All 19 tables conform to JSON Schema 2020-12, validated on every push by CI. Browse all tables: data/index.json.

Built for AI agents

The repo includes AGENTS.md, CLAUDE.md, and llms.txt — navigation contracts for Claude, ChatGPT, Gemini, and any tool following the llmstxt.org convention. Add it to your agent's context once; any SE Asia payroll question resolves in 2 hops without web searches.

# Point your agent at the answer router for any country
ph/INDEX.md  →  maps "what is the SSS rate for ₱25k?" to the exact file
sg/INDEX.md  →  maps "what is the CPF OW ceiling for 2026?" to the exact file
my/INDEX.md  →  maps "EPF rate for employee under 60?" to the exact file
# … and so on for TH, ID, VN

# Why this is more accurate than fetching the source:
# Most SE Asia gov issuances are scanned image PDFs — AI OCR has fabricated
# wrong tax tables from them. Every figure here was rendered and verified
# against the primary document by a human, then adversarially QA-checked.

Why you can trust it