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/claude_telemetryWrote 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/claude_telemetry/prompt-engineer)<a href="https://agentmods.dev/agents/technickai/claude_telemetry/prompt-engineer"><img src="https://agentmods.dev/badge/agents/technickai/claude_telemetry/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/claude_telemetry/prompt-engineer"><img src="https://agentmods.dev/badge/agents/technickai/claude_telemetry/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 | $0.00056 | $0.02062 |
| Opus 5.5 | $0.00022 | $0.00825 |
| Sonnet 5.5 | $0.00011 | $0.00412 |
| Haiku 4.5 | $0.00006 | $0.00206 |
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.
Copies of this mod
1 near-identical copy found in the catalogue:
- prompt-engineer — 92% identical, 20 lines differ
How it starts
The opening of the file, as written. The whole thing — 242 lines — stays where its author put it; the contents beside it link to each section on GitHub.
I'm Petra, and 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 · 242 lines · 56 tokens per session scan A d9d59e2662a5
prompt-engineer is an agent published in the GitHub repository TechNickAI/claude_telemetry (31 stars, last pushed 12mo ago), licensed MIT. It adds 56 tokens to every session and 2,062 once invoked, about $0.0002 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 2026-08-30.
Other agents, from other repositories
prompt-engineer
Optimizes prompts for LLMs and AI systems. Use when building AI features, improving agent performance, or crafting system prompts. Expert in prompt patterns and techniques.
ai-engineer
Build LLM applications, RAG systems, and prompt pipelines. Implements vector search, agent orchestration, and AI API integrations. Use PROACTIVELY for LLM features, chatbots, or AI-powered applications.
prompt-engineer
Prompt quality, instruction adapters, target-family evaluation, and model fit. DeepSeek caching is limited to repeated stable-prefix evals; conductor remains a partial planning framework.
ai-architect
Designs AI/agent systems (agent topology, prompt architecture, RAG design, eval gates, orchestration patterns, model tiering, memory/knowledge-graph design, autonomy guardrails). Advisory only — recommends architecture, does not implement production code. Use for agent design, prompt engineering, retrieval…
ai-engineer
Senior AI/LLM engineer — designs and hardens LLM features: prompt design with structured/JSON-schema output, RAG (chunking, embeddings, retrieval eval, reranking), eval harnesses (golden sets, regression, LLM-as-judge, hallucination detection), model/provider selection by cost/latency/quality…
token
Token and context management — context window optimization, token counting, truncation strategy, chunking design.