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Prompt engineering

Skill vignesh2027/AI-AGENT-SKILLS/skills/prompt-engineering

Turn your ai agent into senior engineer..The result is fast code that fails slowly. AI Agent Skills solves this by giving agents the same disciplined workflows senior engineers use

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npx -y skills add vignesh2027/AI-AGENT-SKILLS --skill prompt-engineering

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Write prompts that are reliable, testable, versioned, and safe

SKILL.md

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Overview

Prompts are code. They have bugs, regressions, and security vulnerabilities. This skill applies software engineering discipline to prompt development: version control, testing, structured output validation, and safety evaluation.

When to Use

  • Before integrating any LLM into a product feature
  • When a prompt is behaving inconsistently
  • Before shipping a prompt to production
  • When iterating on prompt quality

Process

Step 1: Define the task precisely

Write the exact input → output contract for the prompt. What is the input format? What is the output format? What constitutes a correct output? What constitutes a failure?

Step 2: Collect a golden dataset

Gather 20–50 representative inputs with verified correct outputs. This is your test suite. Without it, you are guessing.

Step 3: Write the initial prompt

Start with the simplest possible prompt. State: role, task, constraints, output format. Be explicit about what the model should NOT do.

Step 4: Add examples (few-shot)

Provide 3–5 representative examples that demonstrate the correct behavior. Examples are more reliable than instructions for complex formatting tasks.

Step 5: Specify the output format

For structured outputs: require JSON or XML with a schema. Validate all outputs against the schema. Reject non-conforming outputs rather than guessing.

Step 6: Evaluate on the golden dataset

Run your prompt against all test cases. Score: accuracy, format compliance, latency, cost. Document the baseline.

Step 7: Iterate and track changes

Every change to a prompt is a code change. Version it. Track which version produced which score. Never overwrite a working prompt without knowing the delta.

Step 8: Safety and guardrails

Test for:

  • Prompt injection (user input that overwrites your instructions)
  • Jailbreaks (attempts to bypass role restrictions)
  • Harmful outputs (toxicity, bias, PII)
  • Hallucination (false factual claims)

Add a system prompt safety layer. Add output filtering. Document what is out of scope.

Step 9: Regression testing in CI

Prompts in production must have regression tests that run in CI. A prompt change that reduces golden-dataset score is a regression.

Anti-Rationalizations

"Prompt engineering is just natural language — I don't need to test it" Natural language is the interface. The behavior is a system. All systems need tests.

"LLM outputs are non-deterministic — tests don't work" Deterministic evaluation of non-deterministic outputs: use temperature=0 for tests, use majority-vote scoring, use LLM-as-judge with a stable judge model. Non-determinism is not an excuse.

"We'll monitor it in production" Production monitoring catches failures after users see them. Testing catches failures before users do.

Red Flags

  • Prompts edited directly in production
  • No golden dataset
  • Output format not validated
  • Prompt injection not tested
  • No versioning for prompts

Verification Requirements

  • Input/output contract defined
  • Golden dataset with 20+ examples exists
  • Output format specified and validated
  • Safety evaluation completed (injection, jailbreak, harmful output)
  • Regression tests run in CI
  • Prompt versioned in source control
  • Latency and cost documented

Gives 0 of the 12 instructions most prompt engineering skills give

Counted across 563 of the 626 authors here whose files we hold, read 2026-08-06

  • ask at most three clarifying questionsin 22 of 563, across 15 files
  • respond in the user input languagein 14 of 563, across 9 files
  • preserve the original intentin 13 of 563, across 11 files
  • Establish baseline metrics and collect representative examplesin 12 of 563, across 2 files
  • Identify failure modes and prioritize high-impact fixesin 12 of 563, across 2 files
  • Apply prompt and workflow improvements with measurable goalsin 12 of 563, across 2 files
  • Roll back quickly if quality or safety metrics regressin 12 of 563, across 2 files
  • validate changes with tests and roll out in controlled stagesin 12 of 563, across 2 files
  • generate quantitative baseline performance reportsin 12 of 563, across 2 files
  • create representative test scenariosin 12 of 563, across 2 files
  • treat prompts as codein 12 of 563, across 5 files
  • test prompts on diverse inputsin 12 of 563, across 8 files

Said here and by no other author read

  • define exact input and output contracts
  • collect a golden dataset of test cases
  • document baseline evaluation scores
  • version every prompt change in source control
  • test for prompt injection and jailbreaks
  • add output filtering and safety layers

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once.

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