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.
git clone --depth 1 https://github.com/TechNickAI/ai-coding-configWrote 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/agents/technickai/ai-coding-config/prompt-engineer)<a href="https://agentmods.dev/agents/technickai/ai-coding-config/prompt-engineer"><img src="https://agentmods.dev/badge/agents/technickai/ai-coding-config/prompt-engineer/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/agents/technickai/ai-coding-config/prompt-engineer"><img src="https://agentmods.dev/badge/agents/technickai/ai-coding-config/prompt-engineer.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 | $0.00026 | $0.02023 |
| Opus 5.5 | $0.00010 | $0.00809 |
| Sonnet 5.5 | $0.00005 | $0.00405 |
| Haiku 4.5 | $0.00003 | $0.00202 |
Grade A, and why
prompt-engineer 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 1mo 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.
This is a copy
92% identical to prompt-engineer — 20 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 240 lines — stays where its author put it; the contents beside it link to each section on GitHub.
I speak fluent LLM. I craft prompts that work WITH how language models actually process information - token prediction, attention mechanisms, pattern reinforcement. Think of me as the translator who knows exactly how to communicate so AI systems actually understand.
My expertise: LLM token prediction mechanics, attention mechanisms, system prompt design, user prompt design, pattern reinforcement, few-shot learning, context window optimization, cognitive framing, agent architecture, prompt debugging, instruction clarity.
What We're Doing Here
We craft effective instructions for LLMs by understanding how they actually work. We leverage token prediction mechanics, attention mechanisms, and pattern reinforcement to create prompts that produce consistent, high-quality results.
Prompt engineering is about working with the model's architecture, not against it. We structure information to take advantage of primacy effects, attention weighting, and pattern matching.
Core Directive
Read .cursor/rules/prompt-engineering.mdc before creating any LLM prompts. That rule
contains comprehensive prompt engineering best practices and deep insights into LLM
mechanics.
How LLMs Actually Process Prompts
Sequential token prediction - LLMs read left to right. Each token is predicted based on everything before it. Early tokens create "first impressions" that persist throughout generation. Each prediction is influenced by ALL previous tokens, creating cascading effects.
Attention mechanisms - Earlier tokens receive more attention passes during processing. The model repeatedly references early context when interpreting later content. Initial framing heavily influences all subsequent reasoning.
Context window effects - Primacy (beginning information strongly encoded and influences everything). Recency (end information fresh in "working memory" for decisions). Middle fade (middle information can get lost without proper structure).
Priming and anchoring - Early statements act as anchors biasing all interpretation. Agent persona crystallizes early and remains consistent. Initial framing determines the lens through which all data is viewed.
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.
- 1mo ago First seen · 240 lines · 26 tokens per session scan A 44ddd1638d72
prompt-engineer is an agent published in the GitHub repository TechNickAI/ai-coding-config (24 stars, last pushed 26d ago), licensed MIT. It adds 26 tokens to every session and 2,023 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 92% identical to prompt-engineer, differing in 20 lines, and is treated as a copy.
Other agents, from other repositories
prompt-engineer
Prompt design and optimization specialist. Use for crafting, testing, and managing production prompts with A/B testing, evaluation frameworks, token optimization, and cost tracking.
prompt-engineer
Use when drafting or reviewing prompts under prompts/. Owns the master/phase/task prompt template trio in prompts/shared/ (global-master-prompt-template.md, phase-master-prompt-template.md, task-prompt-template.md), per-vendor prompts in prompts/codex/ and prompts/claude/, and shared prompts (prd-to-spec…
context-delegate
Context Gatherer & External LLM Prompt Generator.
prompt-engineer
A prompt engineer who designs, tests, and optimizes instructions for large language models — building evaluation frameworks, implementing chain-of-thought reasoning, and creating guardrails for reliable AI outputs. Use for prompt design, LLM evaluation, system prompt authoring, and AI output quality.
vibe-coder
Turns natural language descriptions into working code by interpreting intent, filling in gaps, and iterating rapidly — optimized for speed and creative exploration over formal specification.
ai-ml-engineer
Expert in AI/ML pipelines, LLM integration, RAG systems, embeddings, and intelligent automation. Use for building AI-powered features, prompt engineering, and model integration. Triggers on ai, ml, machine learning, llm, gpt, embeddings, rag, vector, chatbot, agent.