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Ai Prompt Engineering

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vasilyu1983·vlatest·ai-ml·MIT

Crafts optimized prompts for various AI models to maximize output quality and achieve desired results.

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This skill no longer installs under the name ai-prompt-engineering. vasilyu1983/ai-agents-public is deleted or private, so it cannot be installed from.

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What we know about this skill

Publisher

vasilyu1983

Taken from the source listing. We haven't verified who controls this account.

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No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill failed 1 of 6 checks. A failure is not proof of bad intent; it means something in the code needs a human to look at it before you run it.

  • No undeclared network calls: failed

    Contacts 16 hosts that are neither well-known infrastructure nor declared in the skill's frontmatter: ai.google.dev, airc.nist.gov, arxiv.org, cheatsheetseries.owasp.org, developers.openai.com….

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

    Hosts contacted

    owasp.orgundeclaredcheatsheetseries.owasp.orgundeclaredgenai.owasp.orgundeclaredwww.nist.govundeclaredairc.nist.govundeclaredeur-lex.europa.euundeclaredjson-schema.orgstandardsopentelemetry.ioundeclaredarxiv.orgundeclareddevelopers.openai.comundeclaredplatform.claude.comundeclaredwww.anthropic.comundeclaredxgrammar.mlc.aiundeclareddspy.aiundeclaredai.google.devundeclaredwww.lennysnewsletter.comundeclaredgithub.comsource hostdocs.langchain.comundeclared

    What it found (3 of 37)

    • skills/universal/ai-prompt-engineering/data/sources.json:15contacts owasp.org
      "url": "https://owasp.org/www-project-top-10-for-large-language-model-applications/",
    • skills/universal/ai-prompt-engineering/data/sources.json:24contacts cheatsheetseries.owasp.org
      "url": "https://cheatsheetseries.owasp.org/cheatsheets/LLM_Prompt_Injection_Prevention_Cheat_Sheet.html",
    • skills/universal/ai-prompt-engineering/data/sources.json:33contacts genai.owasp.org
      "url": "https://genai.owasp.org/resource/owasp-top-10-for-agentic-applications-for-2026/",

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned vasilyu1983/ai-agents-public at 3ee3637d25.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
3ee3637d25
Manifest
skills/universal/ai-prompt-engineering/SKILL.md
Scanned
32 files, 8,614 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

This skill provides operational guidance for crafting optimized prompts for various AI models to maximize output quality and achieve desired results. It focuses on practical patterns, checklists, and templates for production-ready prompts across tasks like RAG workflows, agent orchestration, structured outputs, and multi-step planning. The skill includes optimizations specifically for Claude Code + Codex CLI and emphasizes best practices such as versioned prompts, explicit output contracts, regression tests, and safety threat modeling (aligned with OWASP LLM Top 10).

When to use it

  • When building API integrations requiring machine-parseable outputs like JSON.
  • For extracting specific fields from data sources like forms or invoices.
  • To leverage retrieved context from knowledge bases or documentation search using RAG workflows.
  • When complex decisions require internal reasoning without verbose explanations.
  • To orchestrate multi-step workflows involving API calls and tool usage through agent planning.

Key capabilities

  • Structured Output: Creates prompts for generating JSON outputs with schema validation.
  • Deterministic Extractor: Designs prompts to extract data precisely, handling missing values appropriately.
  • RAG Workflow: Guides prompt creation for effectively using retrieved context with citation checks.
  • Hidden Chain-of-Thought: Enables internal reasoning while only outputting the final answer.
  • Tool/Agent Planner: Facilitates planning and executing sequences of tool calls, one per turn.
  • Rewrite + Constrain: Allows transforming text while preserving meaning and adhering to specific formatting rules.
  • Style Matching: Influences the output style through prompt formatting.
  • Domain-Specific Patterns: Offers specialized guidance for frontend development, research, and agentic coding.

Example prompts

  • "Using the Structured Output pattern, create a prompt that extracts customer data from this text and outputs it as JSON according to this schema: [schema details]."
  • "Apply the RAG Workflow pattern to build a prompt that searches our documentation for information on [topic] and includes citations."
  • "Using the Tool/Agent Planner pattern, design a prompt that first checks the weather in London, then translates it into Spanish."

Tips & gotchas

  • Start with a template from the assets/ directory and fill in the TASK, INPUT, RULES, and OUTPUT FORMAT.
  • Prioritize "brief justification" over requesting chain-of-thought reasoning.
  • When using private reasoning patterns, instruct the AI to “think internally; output only the final answer.”