agentmods.dev

uber-polya AGENTS.md

A problem-solving workflow based on George Pólya's “How to Solve It” method. It turns a real-world question into a mathematical model, solves it, and explains what the result means.

In plain words
What is it for?
Use it to model business or technical problems, choose and verify algorithms, assess sensitivity to changing inputs, and produce analysis reports.
Why use it?
It provides a structured path from an unclear practical problem to a checked solution and useful recommendations.

Instructions file for CodexOpenCode

4 6mo ago A scan 1,179 tokens original Apache-2.0
View the source file agtm1199/uber-polya
Install

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.

agentmods
npx agentmods add instructions/agtm1199/uber-polya/agents-md
Clone the repo
git clone --depth 1 https://github.com/agtm1199/uber-polya

Made for: Codex, OpenCode.

Per session 1,179 This file is loaded in full into every session.
When invoked 1,179 The same file — it is already loaded in full.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.01179 $0.01179
Opus 5 $0.00589 $0.00589
Sonnet 5 $0.00236 $0.00236
Haiku 4.5 $0.00118 $0.00118

Measured 2d ago against content hash c6a17834954c, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

uber-polya AGENTS.md 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.

AGENTS.md · 98 lines

How it starts

The opening of the file, as written. The whole thing — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.

uber-polya

Universal problem-solving engine implementing George Polya's "How to Solve It" methodology. Turns real-world problems into mathematically verified solutions with actionable insights.

What This Does

uber-polya solves problems through a three-phase pipeline:

  1. Model (Phase A): Translate the real-world problem into a formal mathematical model using Socratic dialogue, 91 mathematical structures, and 17 Polya heuristics.
  2. Solve (Phase B): Select the right algorithm from 305 cataloged algorithms, implement a verified Python solver, and prove correctness.
  3. Interpret (Phase C): Translate the solution back into real-world meaning with sensitivity analysis, visualizations, and actionable recommendations.

How to Use

When a user presents a problem to solve, follow the full protocol in docs/methodology.md. That document contains:

  • Step-by-step instructions for each phase
  • Structured artifact schemas (Formal Model, Solution Report, Interpretation Report)
  • Reference file paths and when to consult them
  • Self-checks, error recovery, and fast-track shortcuts
  • Python solver coding conventions

Quick Start

  1. Classify the problem: Is it a problem to Find or to Prove?
  2. Consult skills/uber-model/references/problem-classification.md for rapid pattern matching
  3. Follow Phase A → Phase B → Phase C as documented in docs/methodology.md
  4. At each phase gate, present your work and ask the user to confirm before proceeding

Project Structure

skills/
  uber-polya/               Orchestrator: chains the full pipeline
  uber-model/               Phase A: real-world problem → formal model
    references/             Heuristics, structures, problem classification, common mistakes
  uber-solve/               Phase B: formal model → verified solution
    references/             305 algorithms, 26 solver libraries, solving protocols
  uber-interpret/           Phase C: solution → actionable insight
    references/             Interpretation patterns, 37+ visualization templates
templates/
  latex/                    Jinja2 LaTeX templates + polya.sty for PDF reports
examples/                   36 worked examples with runnable Python solvers
docs/
  methodology.md            Full tool-agnostic protocol (start here)
  architecture.md           System design and expansion patterns
  creating-skills.md        How to build custom skills

Read the full file on GitHub · 98 lines

Changes

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.

  1. 2d ago First seen · 98 lines · 1,179 tokens per session scan A c6a17834954c

Subscribe to this mod's changes

uber-polya AGENTS.md is an instructions file published in the GitHub repository agtm1199/uber-polya (4 stars, last pushed 6mo ago), licensed Apache-2.0. It adds 1,179 tokens to every session, about $0.0059 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 2026-08-31.

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