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AGENTS.md — Repository Knowledge

Quint Prompt Location

The comprehensive Quint specification language prompt is at: quint-prompt.md

It was synthesized from the quint-llm-kit repository (https://github.com/informalsystems/quint-llm-kit) which contains:

• Language reference docs in mcp-servers/kb/kb/docs/ (lang.md, builtin.md, quint.md)
• Patterns in mcp-servers/kb/kb/patterns/ (state-type, pure-functions, thin-actions, etc.)
• Guidelines in agentic/guidelines/ (quint-constraints.md, verification.md, etc.)

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IMPORTANT: Do NOT write multi-line Python as inline python3 -c — the terminal garbles it. Always write to a file first, then run.

Step 1: Quick triage with jq

Run these in order. Set CONVO first:

CONVO=~/.openhands/conversations/CONVERSATION_ID

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1 — Input: WebAssembly 3.0 SpecTec sources
specification/wasm-3.0/ (34 files, fed to SpecTec in numeric order)

File	Covers
0.1-aux.vars.spectec	Auxiliary variables
0.2-aux.num.spectec	Numeric auxiliaries
0.3-aux.seq.spectec	Sequence auxiliaries
1.0-syntax.profiles.spectec	Profiles
1.1-syntax.values.spectec	Values

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# Paste this into D2 Playground: https://play.d2lang.com

direction: right

classes: {
  outcome: {
    style: {
      shadow: true
    }
  }

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Planguage prompts

Planguage (Planning Language), developed by Tom Gilb, is a structured, keyword-driven notation designed to define project requirements, quality attributes, and goals precisely. It eliminates ambiguity in nonfunctional requirements by defining metrics, stakeholders, and targets for, example:

Tag: Unique ID (e.g., Performance).
Meter: How to measure (e.g., "Time for report").
Target: The desired level. 

It is a key part of Competitive Engineering and Evo (Evolutionary) project management.

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Toki Pona Word Reference (pu)

• a - (emotional emphasis/confirmation particle)
• akesi - lizard, reptile
• ala - nothing; not, no, empty; zero
• alasa - hunt, gather; (pre-verb) try to
• ale/ali - everything, universe; all, every; 100/infinite
• anpa - downward, lowly, humble; bow down; defeat
• ante - difference, change; different, other
• anu - (or)

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// https://strudel.cc/workshop/getting-started
// Made by Ray Myers for the Year of DSLs on "Craft vs Cruft" YouTube channel.
// Apologies to the Beastie Boys.

setcpm(110/4)


$: note(`<
[ -!6 c2 d2@2 -!7 ]
[ -!4 d2 c2 a1 d1@2 -!7 ]

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How To Build a Coding Agent

Over the past year coding agents have seen massive adoption and become flagship offerings from AI leaders like Anthropic, OpenAI, and Google. Watching these agents work can feel like magic. To fully explore their potential we need to demystify them. If you're considering contributing to an Open Source agent or even creating one of your own, this should get you off to the races!

The most common style of coding agent today is a chat loop between a user and an LLM that can perform useful software development tasks. To achieve that, there are just 3 tricks to understand beyond the LLM itself.

• How do we create an LLM chat loop?
• How can a chat loop use tools?
• What tools do we need?

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# LinkedIn Post
# https://www.linkedin.com/posts/cadrlife_let-me-translate-the-o3-coding-benchmark-activity-7277038051590029313-yJlR?utm_source=share&utm_medium=member_desktop

# Slides using the minimalist tool Sent
# https://tools.suckless.org/sent/

SWE-bench

SWE-bench measures AI coding agents
on realistic tasks from GitHub

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plugins {
    java
    application
}

group = "org.raymyers"
version = "1.0-SNAPSHOT"

repositories {
    mavenCentral()