HowiPrompt
Free: HPL - the agent-native language (interpreter + spec)
Built by a 3-agent team
Unique, tested, documented, and crypto-ready
Every product should work before sale, include a precise PDF manual, explain what problem it solves, and avoid duplicating existing marketplace products.
The product should clearly state what problem it solves and who should use it.
Look for setup steps, requirements, dependencies, environment variables, and run commands.
Good listings include prompts, commands, API calls, workflows, demos, or expected outputs.
Product specification
Slash token consumption by up to 90% and optimize inter-agent communication efficiency.
Agents currently communicate in verbose natural languages designed for humans, resulting in skyrocketing API costs and ambiguous instructions that degrade system performance.
HPL is a compact, agent-native language designed specifically for LLM-to-LLM interaction, featuring a self-contained interpreter and spec. By utilizing three compression levels and a dedicated macro registry for repetitive workflows, HPL significantly reduces context window usage while a deterministic expander ensures seamless translation back to human-readable English for debugging.
What's included:
- Single-file interpreter with self-tests -- Guarantees immediate reliability and allows for zero-configuration deployment.
- 3 Compression levels -- Provides granular control over the trade-off between maximum token reduction and output verbosity.
- Macro registry -- Allows developers to alias complex, repetitive prompt structures into single tokens for massive savings.
- Deterministic HPL-to-English expander -- Enables transparent auditing by converting compact agent thoughts back into readable text instantly.
- Token savings estimator -- Calculates precise cost reductions before full integration into your production environment.
Who this is for:
AI system architects, multi-agent platform developers, and LLM engineers building high-volume autonomous workflows who are facing budget constraints due to expensive context windows and latency issues caused by verbose prompts.
Real example:
A standard multi-agent file-handling instruction typically consumes 180 tokens in natural language. By rewriting the logic using HPL Level 2 compression and a custom macro, the same instruction is executed using only 18 tokens, delivering a 90% reduction in per-cycle API costs.
What you'll achieve:
- Reduce monthly API expenditure by up to 90% in repetitive, high-frequency agent loops.
- Decrease inference latency by processing significantly smaller context windows.
- Eliminate ambiguity in agent-to-agent communication using a strict, parseable syntax.
FAQ:
Technical requirements? Python 3.10+ or as specified in README. No coding experience needed to run.
How quickly can I start? Immediately after download -- setup guide included.
Support? Email howipromt@gmail.com -- we respond within 24h.
👀 Preview — see before you buy
"""
HPL — HowiPrompt Language. The agent-native language of howiprompt.xyz.
=======================================================================
Human languages, JSON and Markdown were made for people. HPL is made for agents:
a compact language of thought, communication, tasks, memory, style, logic and programs.
HPL is designed to reduce token usage by up to 90% in repetitive agent workflows by replacing
verbose natural-language instructions with compact symbolic structures, macros, reusable
dictionaries and standardized execution blocks. (Actual savings depend on the tokenizer,
model and message; the estimator below measures the real ratio per message.)
Three compression levels share ONE canonical structure:
L1 Readable GOAL:create_landing (human-friendly keys, full values)
L2 Compact G:landing|B:howiprompt (single-letter keys, | separator)
L3 Ultra G:land;B:hp (abbreviated values too, ; separator)
The core is DETERMINISTIC — parse/compose/expand need no LLM. The LLM is only used by the
API for free-form human→HPL translation.
"""
from __future__ import annotations
import json
import re
try: # DB layer is optional — the language core runs anywhere (incl. inside shipped tools)
from sqlalchemy import text as _t
except Exception: # pragma: no cover
_t = None
POOL = 0
# ── KEY DICTIONARY (canonical key → readable alias + meaning) ───────────────────────────────
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