llm-patterns
by alinaqillm-patterns helps you design AI-first application logic where LLMs handle reasoning, extraction, and generation while code handles validation, routing, and error handling. Use the llm-patterns skill for clearer prompt structure, testable LLM workflows, and practical guidance for Skill Authoring.
This skill scores 68/100, which means it is listable but should be presented with caveats. For directory users, it offers a real AI-first app design workflow—especially around using LLMs for classification, extraction, generation, and prompt/testing structure—but it is not tightly triggerable and lacks install-oriented guidance, so adoption will require some interpretation.
- Clear use case: AI-first applications where LLMs handle core logic, including classification, extraction, generation, and decision-making.
- Substantive workflow content with project structure guidance for prompts, LLM client wrappers, schemas, and LLM-specific tests/evals.
- No placeholder or experimental markers; the skill body is substantial and organized with multiple headings and code examples.
- user-invocable is false, so agents may not be able to trigger this skill directly without manual application of its patterns.
- No install command, scripts, references, or supporting files, which reduces operational clarity and trust for quick adoption.
Overview of llm-patterns skill
What llm-patterns is for
The llm-patterns skill helps you design AI-first application logic where an LLM performs the reasoning, extraction, or generation work and your code handles the plumbing. It is most useful when you are deciding how to structure prompts, where to place schema validation, and how to keep LLM behavior testable in production systems.
Best-fit use cases
Use the llm-patterns skill when your app depends on tasks like classification, extraction, summarization, transformation, or other natural-language decisions. It is a good fit for builders who want a clearer system design for LLM-driven features, not just a single prompt that “sort of works.”
What makes it different
The main value of llm-patterns is the separation of concerns: LLM for logic, code for plumbing. That framing matters if you are trying to reduce brittle business rules, improve prompt maintainability, and keep validation, routing, and error handling in conventional code.
How to Use llm-patterns skill
llm-patterns install and first read
Install the skill into your agent workflow, then open skills/llm-patterns/SKILL.md first. Because this repo does not include extra support files such as README.md, rules/, or scripts/, the skill body is the main source of guidance. For a quick decision, read the sections on core principle, project structure, client wrapper, prompt patterns, and testing.
Turn a rough goal into a usable prompt
The llm-patterns usage workflow works best when you supply a concrete task, the expected output shape, and the failure cases you care about. For example, instead of “help me add AI to my app,” use a prompt like: “Design an LLM extraction flow for support tickets, with Zod validation, a fallback path for low-confidence output, and test fixtures for deterministic regression tests.” That gives the skill enough context to recommend a real architecture instead of generic prompt advice.
What to provide upfront
When using llm-patterns for Skill Authoring or app design, include the domain, target LLM task, output schema, acceptable latency, and where humans review results. The skill is strongest when you state whether the model is doing classification, extraction, generation, or decision support, because those patterns have different prompt and test needs.
Workflow that produces better output
Start with the business job, map the LLM step to one narrow responsibility, then ask how to validate and test it. A practical llm-patterns guide usually ends with: prompt template, schema, fallback behavior, test strategy, and a note on what belongs in code instead of the model. If you need deterministic behavior, ask for fixture-based tests and evaluation cases early.
llm-patterns skill FAQ
Is llm-patterns only for advanced teams?
No. The skill is useful for beginners too, as long as they can describe a feature clearly. It does become more valuable as systems get more complex, because the biggest gains come from reducing ambiguity between prompt logic and application logic.
How is this different from a normal prompt?
A normal prompt gives you one-off output. The llm-patterns skill is about repeatable system design: where prompts live, how responses are validated, what gets tested, and how to keep the LLM from taking over responsibilities that code should own.
When should I not use it?
Do not use llm-patterns when the problem is simple rule-based logic, or when a deterministic algorithm is cheaper and more reliable. It is also a poor fit if you cannot define output constraints or do not have a plan for evaluating model quality.
How to Improve llm-patterns skill
Give stronger task boundaries
The best results come from narrow, testable requests. If you say “build an AI assistant,” you will get vague guidance; if you say “classify incoming tickets into three labels and extract two fields into JSON,” you will get a much more actionable architecture.
State the constraints that change the design
The skill works better when you specify latency limits, cost sensitivity, tolerance for errors, whether output must be machine-readable, and whether you need human review. These details affect whether the right pattern is a direct call, a typed wrapper, a staged pipeline, or a fallback workflow.
Ask for validation and test strategy
A common failure mode in LLM apps is focusing on prompt wording while ignoring regressions. Improve your llm-patterns output by asking for schemas, saved fixtures, mock responses, and evaluation cases that reflect real edge inputs, not just happy-path examples.
Iterate from output to production
After the first design, ask what would break in real use: malformed JSON, ambiguous inputs, confidence drops, prompt drift, or unsafe generations. Then refine the prompt spec or wrapper design with those failure modes in mind. That is where llm-patterns provides the most practical value.