Borrowing it
Nothing to install: this file belongs to JoshuaRamirez/advanced-prompting-engine. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/JoshuaRamirez/advanced-prompting-engine/main/CLAUDE.mdgit clone --depth 1 https://github.com/JoshuaRamirez/advanced-prompting-engineWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/instructions/joshuaramirez/advanced-prompting-engine/claude-md)<a href="https://agentmods.dev/instructions/joshuaramirez/advanced-prompting-engine/claude-md"><img src="https://agentmods.dev/badge/instructions/joshuaramirez/advanced-prompting-engine/claude-md/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/instructions/joshuaramirez/advanced-prompting-engine/claude-md"><img src="https://agentmods.dev/badge/instructions/joshuaramirez/advanced-prompting-engine/claude-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.04140 | $0.04140 |
| Opus 5.5 | $0.01656 | $0.01656 |
| Sonnet 5.5 | $0.00828 | $0.00828 |
| Haiku 4.5 | $0.00414 | $0.00414 |
Grade A, and why
advanced-prompting-engine CLAUDE.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 2mo 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.
How it starts
The opening of the file, as written. The whole thing — 260 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Universal Prompt Creation Engine
Project Overview
An MCP server that provides a 12-dimensional philosophical manifold for principled prompt construction. Built on a Construct of 12 faces — each a 12x12 grid of 144 observation points — interconnected through nexi, gems, and spokes. Organized by Vector Equilibrium geometry with cube pairing for harmonization. Implemented with NetworkX (graph topology) + numpy (computation) + SQLite (persistence) + Python MCP SDK (protocol).
The engine does not generate prompts. It measures intent across 12 philosophical axes and returns a construction basis from which the client constructs.
Architecture
- Construct: 12 faces (domains), each a 12x12 grid of 144 observation points with position-determined classification and potency. See
docs/CONSTRUCT-v2.md. - 3-Level Schema: Axiom Layer (12 faces with 2 sub-dimensions each) → Schema Layer (12x12 grids, 144 constructs per face, 1728 total) → Coordinate Layer (computed (x,y) positions)
- Inter-Face: 132 directional nexi (66 unique pairs) producing 132 gems, organized as 12 spokes converging on a central gem. Nexi stratified by cube model: 6 paired + 48 adjacent + 12 opposite.
- Cube Pairing: 6 complementary pairs (theoretical/applied). Paired faces harmonize through shared surfaces and positional correspondence.
- External Surface: 4 MCP tools (
create_prompt_basis,explore_space,extend_schema,interpret_basis) + 4 prompts + 4 resources - Internal Layers (top to bottom):
- Multi-Pass Orchestrator (stress_test, triangulate, deepen)
- Pipeline (8 stages — single forward pass)
- Graph Query Layer + Graph Mutation Layer (structured graph access)
- TF-IDF Cache (lifecycle-managed, auto-invalidate on graph mutation — used by explore_space, not by Stage 1)
- Semantic Bridge (GeometricBridge — pre-computed BGE-derived face similarity + axis projections + disambiguation overrides, used by Stage 1)
- NetworkX (topology, 1873 nodes, 2279 edges) + numpy (computation) + SQLite (persist, canonical/user tables)
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.
- 2mo ago First seen · 260 lines · 4,140 tokens per session scan A 2572843760b7
advanced-prompting-engine CLAUDE.md is an instructions file published in the GitHub repository JoshuaRamirez/advanced-prompting-engine (0 stars, last pushed 1mo ago), licensed MIT. It adds 4,140 tokens to every session, about $0.0166 per session on Opus 5.5. 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.
advanced-prompting-engine CLAUDE.md compared with instructions from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| advanced-prompting-engine CLAUDE.mdJoshuaRamirez/advanced-prompting-engine | Instructions file | 0 | 1mo ago | MIT | A | 4,140 | Claude Code |
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| deepseek-harness AGENTS.mddeepseek-ai/deepseek-harness | Instructions file | 274k | 2d ago | MIT | A | 4,313 | Codex, OpenCode |