# Prompt Engineering > Use when writing or improving prompts for a language model. Covers instruction structure, examples, reasoning elicitation, output formatting, and systematically diagnosing why a prompt fails. - **Type:** Skill - **Install:** `agentstack add skill-nimadorostkar-claude-skills-collection-prompt-engineering` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [nimadorostkar](https://agentstack.voostack.com/s/nimadorostkar) - **Installs:** 0 - **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [nimadorostkar](https://github.com/nimadorostkar) - **Source:** https://github.com/nimadorostkar/Claude-Skills-collection/tree/main/skills/ai/prompt-engineering - **Website:** https://github.com/nimadorostkar/Claude-Skills-collection ## Install ```sh agentstack add skill-nimadorostkar-claude-skills-collection-prompt-engineering ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # Prompt Engineering ## Purpose Write prompts that produce the right output reliably, and diagnose the ones that do not. Prompt engineering is an empirical discipline: the model's actual behavior, measured, beats any theory about what should work. ## When to Use - Writing a prompt for a production feature. - A prompt that works most of the time and fails unpredictably. - Reducing token cost or latency without losing quality. - Migrating a prompt between models. ## Capabilities - Instruction design: role, task, constraints, and output contract. - Few-shot examples, and selecting the ones that teach the boundary. - Reasoning elicitation for tasks that need it. - Output formatting and structured extraction. - Failure diagnosis and systematic iteration. ## Inputs - The task, and what a correct output looks like. - Real failure cases from the current prompt. - Constraints: latency, cost, and the model available. ## Outputs - A prompt with an explicit output contract. - An evaluation set of inputs with expected outputs. - A measured success rate, before and after. ## Workflow 1. **Define correct before writing the prompt** — Twenty real inputs and their correct outputs. Without this, "improving" a prompt is a matter of opinion and you will optimize for the last example you looked at. 2. **State the task, the constraints, and the output shape** — Be specific about what to do, what not to do, and exactly what format the answer takes. 3. **Add examples that teach the boundary** — Two or three examples covering the ambiguous cases, not the obvious ones. An example of the hard case is worth ten of the easy one. 4. **Elicit reasoning only where it helps** — For multi-step reasoning, ask the model to work through it before answering. For classification and extraction, it adds latency and cost without improving accuracy. 5. **Measure, change one thing, measure again** — On the evaluation set. A change that improves one example and silently breaks three others is a regression that feels like progress. 6. **Handle the failure mode** — Decide what happens when the model returns something unparseable, refuses, or hallucinates a field. That path will be taken. ## Best Practices - Specificity beats politeness. "Return exactly one of: approve, reject, escalate" outperforms "please try to categorize this appropriately". - Put the instructions before the data for short inputs, and after for long ones — models attend more reliably to the end of a long context. - Negative instructions are weakly followed. "Do not include a preamble" works less well than "Begin your response with the JSON object." - Structured output (a JSON schema, a tool call) is far more reliable than asking for JSON in prose and hoping. Use the API's native mechanism. - Long, elaborate prompts accumulate contradictions. When a prompt exceeds a page, look for instructions that conflict — the model is following one of them. - Prompt improvements do not transfer across models. Re-measure after any model change; a prompt tuned for one is not tuned for another. ## Examples **A prompt with an explicit contract and a boundary example:** ```text You classify customer support messages for routing. Categories (choose exactly one): - billing — charges, invoices, refunds, payment methods - technical — errors, outages, broken functionality, integration problems - account — login, permissions, plan changes, cancellation - other — anything not clearly covered above Rules: - If a message spans two categories, choose the one the customer needs resolved first to be unblocked. - If the message is too vague to classify with confidence, return "other" with confidence below 0.5. Do not guess. Respond with JSON only, matching this schema: {"category": "billing|technical|account|other", "confidence": 0.0-1.0, "reason": ""} Examples: Message: "I was charged twice for March and I can't log in to check." {"category": "account", "confidence": 0.8, "reason": "The double charge matters, but they cannot access the account to act on it, so access is the blocker."} Message: "hey" {"category": "other", "confidence": 0.2, "reason": "No classifiable content."} ``` The first example is the one that earns its place: it teaches the tie-break rule in a way the prose alone does not. **Diagnosing a failing prompt systematically:** ```text Symptom: the classifier returns "billing" for technical messages ~15% of the time. Hypotheses, tested one at a time against the 40-case evaluation set: H1: The category definitions overlap. Test: read them. "billing" says "payment methods"; several technical messages are about a broken payment *integration*. Result: CONFIRMED. Baseline 85% -> 94% after clarifying that integration failures are technical regardless of subject matter. H2: Reasoning would help. Test: add "Think step by step before answering." Result: no accuracy change, +340ms latency, +180 tokens. REVERTED. H3: More examples would help. Test: 6 examples instead of 2. Result: 94% -> 96%. Kept. Diminishing returns past 6 (tested 10: 96%). Final: 96%, at lower cost than the baseline. ``` ## Notes - The measurement set is the entire discipline. A prompt engineer without an evaluation set is guessing with extra confidence. - "Think step by step" is not free and is not universally helpful. It reliably improves multi-step arithmetic and logic; it reliably does nothing for classification. Measure rather than assume. - Prompt injection is a real vulnerability when user content enters a prompt. Never let user text be interpreted as instructions — delimit it clearly and treat the model's output as untrusted. ## Source & license This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [nimadorostkar](https://github.com/nimadorostkar) - **Source:** [nimadorostkar/Claude-Skills-collection](https://github.com/nimadorostkar/Claude-Skills-collection) - **License:** MIT - **Homepage:** https://github.com/nimadorostkar/Claude-Skills-collection Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/skill-nimadorostkar-claude-skills-collection-prompt-engineering - Seller: https://agentstack.voostack.com/s/nimadorostkar - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.