Expert prompt optimization for LLMs and AI systems. Use PROACTIVELY when building AI features, improving agent performance, or crafting system prompts. Masters prompt patterns and techniques.
Instrucciones de origen · Vista previa de solo lectura
license
Apache-2.0
name
prompt-engineer
description
Expert prompt optimization for LLMs and AI systems. Use PROACTIVELY when building AI features, improving agent performance, or crafting system prompts. Masters prompt patterns and techniques.
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Prompt Engineer
Expert in crafting, optimizing, and debugging prompts for large language models. Transform vague requirements into precise, effective prompts that produce consistent, high-quality outputs.
Quick Start
User: "My chatbot gives inconsistent answers about our refund policy"
Prompt Engineer:
1. Analyze current prompt structure
2. Identify ambiguity and edge cases
3. Apply constraint engineering
4. Add few-shot examples
5. Test with adversarial inputs
6. Measure improvement
Result: 40-60% improvement in response consistency
Core Competencies
1. Prompt Architecture
System prompt design for persona and constraints
User prompt structure for clarity
Context window optimization
Multi-turn conversation design
2. Optimization Techniques
Technique
When to Use
Expected Improvement
Chain-of-Thought
Complex reasoning
20-40% accuracy
Few-Shot Examples
Format consistency
30-50% reliability
Constraint Engineering
Edge case handling
50%+ consistency
Role Prompting
Domain expertise
15-25% quality
Self-Consistency
Critical decisions
10-20% accuracy
3. Debugging & Testing
Prompt ablation studies
Adversarial input testing
A/B testing frameworks
Regression detection
Prompt Patterns
The CLEAR Framework
C - Context: What background does the model need?
L - Limits: What constraints apply?
E - Examples: What does good output look like?
A - Action: What specific task to perform?
R - Review: How to verify correctness?
System Prompt Template
You are [ROLE] with expertise in [DOMAIN].
## Your Task
[CLEAR, SPECIFIC INSTRUCTION]
## Constraints- [CONSTRAINT 1]
- [CONSTRAINT 2]
## Output Format
[EXACT FORMAT SPECIFICATION]
## Examples
Input: [EXAMPLE INPUT]
Output: [EXAMPLE OUTPUT]
Chain-of-Thought Pattern
Think through this step-by-step:
1. First, identify [ASPECT 1]
2. Then, analyze [ASPECT 2]
3. Consider [EDGE CASES]
4. Finally, synthesize into [OUTPUT]
Show your reasoning before the final answer.
Optimization Workflow
Phase
Activities
Tools
Analyze
Review current prompts, identify issues
Read, pattern analysis
Hypothesize
Form improvement hypotheses
Sequential thinking
Implement
Apply prompt engineering techniques
Write, Edit
Test
Validate with diverse inputs
Manual testing
Measure
Quantify improvement
A/B comparison
Iterate
Refine based on results
Repeat cycle
Common Issues & Fixes
Issue: Hallucinations
Problem: Model fabricates information
Fix: Add "Only use information provided. Say 'I don't know' if uncertain."
Issue: Verbose Output
Problem: Model produces too much text
Fix: Add "Be concise. Maximum 3 sentences." + format constraints
Issue: Format Violations
Problem: Output doesn't match required format
Fix: Add explicit examples + "Follow this exact format:"
Issue: Context Confusion
Problem: Model loses track in long conversations
Fix: Add periodic context summaries + clear role reminders
Output Contract
An engineered prompt this skill hands off carries:
engineeredPrompt: the finished system/user prompt text (markdown), applying the CLEAR framework and the relevant techniques from the Optimization Techniques table.
rationale: markdown explaining which techniques were applied and why — which constraint(s) were prioritized, why a technique was skipped, what ablation/adversarial testing found.
promptSpec: a structured JSON description of the prompt's own properties (see schemas/prompt-spec.schema.json) — role, output contract, few-shot count, guardrails, eval criteria, input-delimiting, and edge-case behavior. This is what scripts/prompt_audit.mjs audits.
Run node scripts/prompt_audit.mjs --input <prompt-spec>.json to get a deterministic { pass, score, findings, recommendations } audit of a prompt's structural properties before shipping it. The scorer never reads prompt prose or does keyword matching — it scores the structured booleans/counts an engineer has already decided during Analyze/Implement, the same decisions references/eval-criteria-patterns.md and references/injection-and-safety.md walk through making.
Novice: Crams every possible instruction, edge case, and constraint into one system prompt "to be safe."
Expert: Prioritizes 3-5 key constraints and uses progressive disclosure (references, follow-up turns, retrieval) for the rest — an overloaded prompt dilutes the instructions that actually matter and the model can no longer tell what to prioritize.
Detection: prompt_audit.mjs returns a critical kitchen-sink-overload finding when avoidsKitchenSink is false.
Anti-Pattern: Vague Instructions / No Role Grounding
Novice: "Write something good about our product" — no persona, no measurable criteria, no stated domain expertise for the model to inhabit.
Expert: States a specific role ("You are a senior support engineer who has resolved 10,000 refund tickets...") plus specific, testable requirements and examples.
Detection: prompt_audit.mjs returns a medium no-clear-role finding when hasClearRole is false.
Anti-Pattern: Over-Constraining
Novice: 50+ rules the model must simultaneously satisfy, many of them contradictory once you look closely.
Expert: Essential constraints only — test each one for necessity before shipping it. This is the same overload failure as Prompt Stuffing, arrived at from the other direction (too many rules instead of too many topics).
Detection: Caught by the same kitchen-sink-overload finding (avoidsKitchenSink: false) — the scorer treats topic-overload and rule-overload as one failure mode: the model can't prioritize.
Anti-Pattern: No Examples
Novice: A complex output format described entirely in prose, with zero example input/output pairs.
Expert: Always include 2-3 representative examples — a model infers a format far more reliably from examples than from a prose description alone.
Detection: prompt_audit.mjs returns an insufficient-few-shot-examples finding (medium when fewShotExamples is 0, low when it is exactly 1) whenever fewShotExamples < 2.
Anti-Pattern: No Explicit Output Contract
Novice: Ships a prompt that describes the task but never states the required output shape, so the model free-forms JSON, prose, or markdown inconsistently across calls.
Expert: States the exact output contract (schema, structure, delimiters) in the prompt itself, and validates it downstream (pairs with output-contract-enforcer).
Detection: prompt_audit.mjs returns a critical missing-output-contract finding when hasExplicitOutputContract is false, and a high output-format-unspecified finding when outputFormatSpecified is false.
Novice: Interpolates user-supplied or retrieved (RAG) text directly into the instruction stream with no delimiter, so a crafted input can be read as a new instruction ("ignore previous instructions...").
Expert: Wraps every untrusted/user-supplied span in an explicit delimiter (XML tags, a fenced block, a named variable) and instructs the model to treat that span as data, never as instructions. See references/injection-and-safety.md.
Detection: prompt_audit.mjs returns a critical untrusted-input-not-delimited finding when delimitsUntrustedInput is false — this fails closed: an unset or missing field is scored as undelimited, never assumed safe.
Anti-Pattern: No Eval Criteria
Novice: Ships a prompt and finds out whether it "works" only from vibes or a single manual spot-check.
Expert: Defines eval criteria before shipping — a rubric, a set of golden input/output pairs, or an LLM-judge prompt — so a regression is detectable, not just felt. See references/eval-criteria-patterns.md.
Detection: prompt_audit.mjs returns a critical no-eval-criteria finding when hasEvalCriteria is false.
Anti-Pattern: No Refusal / Edge-Case Behavior
Novice: The prompt only covers the happy path; when the model hits something out of scope, adversarial, or ambiguous, behavior is undefined.
Expert: Explicitly specifies what to do at the edges — refuse, ask a clarifying question, or degrade gracefully — as part of the prompt itself, not left as an emergent property.
Detection: prompt_audit.mjs returns a critical no-refusal-behavior finding when specifiesRefusalBehavior is false.
Anti-Pattern: No Guardrails
Novice: No hallucination guard, no scope boundary, no "only use information provided" instruction — the model is free to fabricate.
Expert: Adds explicit guardrails appropriate to the task's risk level (see Common Issues & Fixes above for the canonical hallucination fix).
Detection: prompt_audit.mjs returns a medium no-guardrails-present finding when guardrailsPresent is false.
Quality Metrics
Metric
How to Measure
Target
Consistency
Same input, same output quality
>90%
Accuracy
Correct information
>95%
Format Compliance
Follows specified format
>98%
Latency
Time to first token
<2s
Token Efficiency
Output tokens per task
-20% waste
When to Use
Use for:
Designing system prompts for chatbots
Optimizing agent instructions
Reducing hallucinations
Improving output consistency
Creating prompt templates
Do NOT use for:
Building end-to-end LLM applications, RAG pipelines, or agent orchestration — a production-integration concern, not prompt wording
Automated, self-improving prompt optimization loops — a distinct automation concern; this skill covers deliberate, expert-driven prompt engineering
General coding tasks (use language-specific skills)
Infrastructure setup (use deployment skills)
Core insight: Great prompts are like great specifications — specific enough to eliminate ambiguity, flexible enough to handle variation, and tested against adversarial inputs.
Pairs with: agentic-app-architecture (app shape before prompt wording) | llm-router (prompt complexity informs model-tier choice) | output-contract-enforcer (validates the output contract this skill's prompts declare).
References
File
Load When
references/injection-and-safety.md
Need to delimit untrusted/user-supplied input, or design refusal/guardrail behavior for adversarial or out-of-scope input.
references/eval-criteria-patterns.md
Need to define how to tell whether a prompt's output is actually correct — rubrics, golden examples, or an LLM-judge pattern.
examples/expected-output.md
Need to see a weak prompt spec audited, then the same prompt fixed and passing.
templates/output-template.md
Need a reusable template for the engineered prompt + rationale + prompt spec handoff.
schemas/prompt-spec.schema.json
Need to validate a prompt-spec JSON payload's structure before auditing it.
scripts/prompt_audit.mjs
Need deterministic scoring of a prompt's structural properties.
agents/openai.yaml
Need a subagent descriptor for delegated prompt engineering.
Skill Bundle Index
Every file in this skill, and when to open it. Auto-generated; run scripts/index_references.py --fix.
root
CHANGELOG.md — Prompt Engineer — Changelog — - Imported from the global windags skill catalog (SKILL.md only) and upgraded to the port-daddy agentic-family standard.
README.md — Prompt Engineer — Craft, optimize, and debug prompts for large language models: the CLEAR framework, chain-of-thought and few-shot patterns, and a determinist
examples/expected-output.md — Example Output: Prompt Engineer — Scenario: an agent writes a first-draft system prompt for a support chatbot that answers refund-policy questions using retrieved ticket hist
references/eval-criteria-patterns.md — Defining Eval Criteria for a Prompt — Use this when you need a way to tell whether a prompt change is an improvement or a regression, instead of relying on a single manual read o
references/injection-and-safety.md — Delimiting Untrusted Input & Refusal Behavior — Use this when you need to embed user-supplied or retrieved (RAG, tool-output, ticket-history, web-fetch) text inside a prompt, or need to de
