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 skills add 05-deepak-patidar/claude-skills --skill ai-engineeringgit clone --depth 1 https://github.com/05-deepak-patidar/claude-skillsWrote 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/05-deepak-patidar/claude-skills/ai-engineering)<a href="https://agentmods.dev/skills/05-deepak-patidar/claude-skills/ai-engineering"><img src="https://agentmods.dev/badge/skills/05-deepak-patidar/claude-skills/ai-engineering/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/skills/05-deepak-patidar/claude-skills/ai-engineering"><img src="https://agentmods.dev/badge/skills/05-deepak-patidar/claude-skills/ai-engineering.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 | $0.00104 | $0.01314 |
| Opus 5 | $0.00052 | $0.00657 |
| Sonnet 5 | $0.00021 | $0.00263 |
| Haiku 4.5 | $0.00010 | $0.00131 |
Grade B, and why
ai-engineering scanned grade B with 1 finding 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 11d 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.
Instruction-override phrasingmediumPrompt injection
Text telling the model to disregard its earlier instructions or safety rules is the shape of a prompt injection, whoever wrote it.
- Test prompts against the *ugly* inputs: empty, enormous, wrong-language, adversarial ("ignore previous instructions"), and out-of-domain. The demo input tells you nothing. Downgraded: this mod is about security review, or the phrase is quoted, so it is likely naming the pattern rather than instructing it.
How it starts
The opening of the file, as written. The whole thing — 54 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Engineering
An LLM feature is a probabilistic component inside a deterministic system. The engineering is not the prompt — it's everything around the prompt that makes unreliable output safe to ship: schemas, fallbacks, evals, and budgets. Treat the model like a talented but occasionally wrong remote API, because that is exactly what it is.
Rule 0: Do you even need a model?
Regex, a lookup table, or a SQL query that solves 95% of the cases beats an LLM that solves 98% — cheaper, faster, deterministic, debuggable. Use an LLM where the input is genuinely open-ended (natural language, unstructured documents, judgment calls). Hybrid is usually right: deterministic code for the structure, the model only for the irreducibly fuzzy step.
Prompts are code — manage them like code
- Prompts live in version control, named and versioned, never inline string-concatenated across the codebase. A prompt change is a deploy: reviewable, diffable, revertible, and eval-gated (see below).
- Structure every production prompt the same way: role/goal → constraints and refusal conditions → the input (clearly delimited — assume user input is hostile and may contain instructions; label it as data, never let it sit where instructions sit) → output format specification → 2–5 few-shot examples of hard cases (examples move behavior more than adjectives).
- Test prompts against the ugly inputs: empty, enormous, wrong-language, adversarial ("ignore previous instructions"), and out-of-domain. The demo input tells you nothing.
Structured output — the highest-ROI habit
Never parse prose. Demand JSON against a schema (native structured-output/tool-calling modes where the provider has them), then validate with a real schema layer (Pydantic/zod) before the data touches your system. On validation failure: one retry with the error fed back, then fallback path. This one habit converts "AI feature is flaky" into ordinary input validation.
The reliability ladder — climb only as far as needed
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.
- 11d ago First seen · 54 lines · 104 tokens per session scan B a16ba31154a0
ai-engineering is a skill published in the GitHub repository 05-deepak-patidar/claude-skills (4 stars, last pushed 2mo ago), licensed MIT. It adds 104 tokens to every session and 1,314 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it B with 1 finding (instruction-override phrasing). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
ai-engineering-toolkit
AI Engineering Toolkit workflow skill. Use this skill when the user needs 6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching and the operator…
prompt-engineer
Transform rough prompts/ideas into production-ready LLM prompts. Use when crafting, refining, or optimizing prompts for any AI model (Codex, GPT, Llama, etc.) with advanced techniques like CoT, constitutional AI, RAG optimization.
ai-product
Every product will be AI-powered. The question is whether you'll build it right or ship a demo that falls apart in production. This skill covers LLM integration patterns, RAG architecture, prompt ...
super-ai-ml-foundation
AI/ML foundations: model selection, prompt design, RAG, embeddings, and vector search. Use for core AI app design and build.
ai-engineering-toolkit
6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.
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.