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.
npx agentmods add skills/manusco/resonance/ai-engineeringnpx skills add manusco/resonance --skill ai-engineeringgit clone --depth 1 https://github.com/manusco/resonanceWrote 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/skills/manusco/resonance/ai-engineering)<a href="https://agentmods.dev/skills/manusco/resonance/ai-engineering"><img src="https://agentmods.dev/badge/skills/manusco/resonance/ai-engineering.svg" alt="Measured on agentmods" 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 | $0.00000 | $0.02478 |
| Opus 5 | $0.00000 | $0.01239 |
| Sonnet 5 | $0.00000 | $0.00496 |
| Haiku 4.5 | $0.00000 | $0.00248 |
Grade A, and why
resonance-engineering-ai-engineering 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 5d 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 — 111 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/resonance-engineering-ai-engineering: ship AI features you can measure, not demos you hope work
Role: builder of reliable AI features on top of non-deterministic models. Input: A feature idea ("summarize tickets", "answer from our docs", "an agent that books travel"), a failing pipeline, or a cost/latency/quality complaint. Output: A design anchored to an eval set, with explicit guardrails, a cost/latency budget, and a named failure mode for every component. Definition of Done: An eval set exists and runs before the prompt is "final". Every external claim the model makes is grounded or fenced. A per-request cost and P95 latency budget is stated. Every retrieval and tool call has a defined failure path.
The model is a stochastic component, not a function. It will be confidently wrong. Your job is not to write a clever prompt. Your job is to build a system that measures its own quality, fails safely, and costs what you decided it costs. If you cannot measure it, you cannot ship it, you can only demo it.
The one rule
Evals before prompts. You do not tune a prompt against your own vibes. You write 20 to 50 real input/output cases, define how "good" is scored, then change the prompt and watch the number. A prompt with no eval is an opinion. This is the difference between "it worked when I tried it" and "it works".
Jobs to Be Done
| Job | Trigger | Output |
|---|---|---|
| New LLM feature | "Add AI that does X" | Eval set first, then prompt + context design, then the smallest model that passes |
| RAG pipeline | "Answer from our data" | Chunking + retrieval + grounding design, with retrieval quality measured separately from generation |
| Agent / tool loop | "It should take actions" | Tool contracts, a bounded control loop, stop conditions, and a check that an agent is even needed |
| Guardrails | "It said something wrong/unsafe" | Input/output validation, grounding checks, refusal paths, human-in-the-loop gates |
| Cost / latency fix | "Too slow / too expensive" | Model right-sizing, caching, routing, and a measured budget per request |
| RAG diagnosis | "It returns wrong answers" | Isolate retrieval vs. generation failure; fix the actual broken stage |
What ships with it
10 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- evals/01_new_llm_feature.json 1.1 KB
- evals/02_rag_diagnosis.json 1.2 KB
- evals/03_hallucination_and_cost.json 1.2 KB
- evals/04_planted_defect.json 1.4 KB
- references/agent_design.md 4.9 KB
- references/context_engineering.md 4.5 KB
- references/eval_driven_development.md 4.9 KB
- references/guardrails_and_safety.md 5.4 KB
- references/llmops_cost_latency.md 5.0 KB
- references/rag_architecture.md 5.1 KB
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.
- 5d ago First seen · 111 lines · 0 tokens per session scan A 3b08384d3ddb
resonance-engineering-ai-engineering is a skill published in the GitHub repository manusco/resonance (37 stars, last pushed 3d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,478 tokens. 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 skills, from other repositories
dspy
DSPy: declarative LM programs, auto-optimize prompts, RAG.
llm-app-patterns
Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG, building agents, or setting up LLM observability.
llm-ops
LLM Operations -- RAG, embeddings, vector databases, fine-tuning, prompt engineering avancado, custos de LLM, evals de qualidade e arquiteturas de IA para producao.
data-flywheel
Use this skill when a user wants to turn repeated human-approved agent work across Claude Code, Hermes, OpenClaw, Codex, Cursor, or custom .agent/ loops into local artifacts for retrieval, evals, prompt shrinking, and optional future open-weight model/adapters.
llm-application-dev
Building applications with Large Language Models - prompt engineering, RAG patterns, and LLM integration. Use for AI-powered features, chatbots, or LLM-based automation.
langchain-development
Expert guidance for LangChain and LangGraph development with Python, covering chain composition, agents, memory, and RAG implementations.