Agent skill

Prompt Engineering

by CodeAlive-AI in CodeAlive-AI/ai-driven-development

Universal prompt engineering techniques for any LLM. An agent skill from CodeAlive-AI/ai-driven-development.

MITAuto-check passedAI & LLM Engineering

Install Prompt Engineering

skills CLI
$ npx skills add CodeAlive-AI/ai-driven-development --skill prompt-engineering -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install CodeAlive-AI/ai-driven-development prompt-engineering --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/CodeAlive-AI/ai-driven-development.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/prompt-engineering .claude/skills/prompt-engineering && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
prompt-engineering
GitHub stars
155
Used in
1 other repo
Token cost
~4.1k tokens
SKILL.md length
1,220 words
Files
21 (incl. references)
Skills in repo
22
Repo updated
First seen
Licence
MIT

At a glance

Universal prompt engineering techniques for any LLM. An agent skill from CodeAlive-AI/ai-driven-development.

  • Works in 5 steps: Structure with XML Tags → Control Output Shape → Prevent Scope Drift → …
  • Reviewing prompts for AI models
  • SKILL.md covers Core Principles, Agentic Prompts, Structured Extraction and Web Research Prompts, plus 7 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Prompt Engineering is an agent skill from CodeAlive-AI/ai-driven-development. Universal prompt engineering techniques for any LLM. Use when crafting, optimizing, or reviewing prompts for AI models. Triggers on requests like "improve this prompt", "write a system prompt", "optimize my instructions", "help me prompt engineer", "audit this prompt", "review my prompt", or when building agentic systems that need structured prompts.

Its SKILL.md is about 4.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 21 other files, including reference files (for example `README.md`, `references/claude-fable5-prompting.md` and `references/claude-fable51-prompting.md`).

It sits in AI & LLM Engineering, covering Prompt engineering. The repository describes itself as: Practices, protocols, and skills for AI-driven software development. Skills and safety hooks for Claude Code, Codex, OpenCode, Cursor, Antigravity, and any agent supporting the… The licence is MIT.

When your agent uses it

  • Reviewing prompts for AI models
  • Requests like improve this prompt
  • Write a system prompt
  • Optimize my instructions

Example prompts

  • “improve this prompt”
  • “write a system prompt”
  • “optimize my instructions”
  • “/prompt-engineering”

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Structure with XML Tags
  2. Control Output Shape
  3. Prevent Scope Drift
  4. Handle Ambiguity Explicitly
  5. Long-Context Grounding

What it can do on your machine

Read from SKILL.md and the folder at commit 25b7b1d. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are xml).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Prompt Engineering loads about 4.1k tokens when it runs, and up to ~77k if it reads all its reference files. Until then it costs about 93 tokens; SKILL.md has 1,220 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~93
When it runs · the whole SKILL.md, loaded when a task matches
~4.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~77k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from CodeAlive-AI/ai-driven-development at commit 25b7b1d, republished under its MIT licence (© CodeAlive-AI). 1,220 words, ~4,085 tokens.

Download SKILL.mdSave it as .claude/skills/prompt-engineering/SKILL.md (or your agent's skills folder). This skill also uses 20 other files; get the full folder from GitHub.
name
prompt-engineering
description
Universal prompt engineering techniques for any LLM. Use when crafting, optimizing, or reviewing prompts for AI models. Triggers on requests like "improve this prompt", "write a system prompt", "optimize my instructions", "help me prompt engineer", "audit this prompt", "review my prompt", or when building agentic systems that need structured prompts.

Prompt Engineering

Universal techniques for crafting effective prompts across any LLM.

Core Principles

1. Structure with XML Tags

Use XML tags to create clear, parseable prompts:

xml
<context>Background information here</context>
<instructions>
1. First step
2. Second step
</instructions>
<examples>Sample inputs/outputs</examples>
<output_format>Expected structure</output_format>

Benefits:

  • Clarity: Separates context, instructions, and examples
  • Accuracy: Prevents model from mixing up sections
  • Flexibility: Easy to modify individual parts
  • Parseability: Enables structured output extraction

Best practices:

  • Use consistent tag names throughout (<instructions>, not sometimes <steps>)
  • Reference tags explicitly: "Using the data in <context> tags..."
  • Nest tags for hierarchy: <examples><example id="1">...</example></examples>
  • Combine with other techniques: <thinking> for chain-of-thought, <answer> for final output
2. Control Output Shape

Specify explicit constraints on length, format, and structure:

xml
<output_spec>
- Default: 3-6 sentences or ≤5 bullets
- Simple yes/no questions: ≤2 sentences
- Complex multi-step tasks:
  - 1 short overview paragraph
  - ≤5 bullets: What changed, Where, Risks, Next steps, Open questions
- Use Markdown with headers, bullets, tables when helpful
- Avoid long narrative paragraphs; prefer compact structure
</output_spec>
3. Prevent Scope Drift

Explicitly constrain what the model should NOT do:

xml
<constraints>
- Implement EXACTLY and ONLY what is requested
- No extra features, components, or embellishments
- If ambiguous, choose the simplest valid interpretation
- Do NOT invent values, make assumptions, or add unrequested elements
</constraints>
4. Handle Ambiguity Explicitly

Prevent hallucinations and overconfidence:

xml
<uncertainty_handling>
- If the question is ambiguous:
  - Ask 1-3 precise clarifying questions, OR
  - Present 2-3 plausible interpretations with labeled assumptions
- When facts may have changed: answer in general terms, state uncertainty
- Never fabricate exact figures or references when uncertain
- Prefer "Based on the provided context..." over absolute claims
</uncertainty_handling>
5. Long-Context Grounding

For inputs >10k tokens, add re-grounding instructions:

xml
<long_context_handling>
- First, produce a short internal outline of key sections relevant to the request
- Re-state user constraints explicitly before answering
- Anchor claims to sections ("In the 'Data Retention' section...")
- Quote or paraphrase fine details (dates, thresholds, clauses)
</long_context_handling>

Agentic Prompts

Tool Usage Rules
xml
<tool_usage>
- Prefer tools over internal knowledge for:
  - Fresh or user-specific data (tickets, orders, configs)
  - Specific IDs, URLs, or document references
- Parallelize independent reads when possible
- After write operations, restate: what changed, where, any validation performed
</tool_usage>
User Updates
xml
<user_updates>
- Send brief updates (1-2 sentences) only when:
  - Starting a new major phase
  - Discovering something that changes the plan
- Avoid narrating routine operations
- Each update must include a concrete outcome ("Found X", "Updated Y")
- Do not expand scope beyond what was asked
</user_updates>
Self-Check for High-Risk Outputs
xml
<self_check>
Before finalizing answers in sensitive contexts (legal, financial, safety):
- Re-scan for unstated assumptions
- Check for ungrounded numbers or claims
- Soften overly strong language ("always", "guaranteed")
- Explicitly state assumptions
</self_check>

Structured Extraction

For data extraction tasks, always provide a schema:

xml
<extraction_spec>
Extract data into this exact schema (no extra fields):
{
  "field_name": "string",
  "optional_field": "string | null",
  "numeric_field": "number | null"
}
- If a field is not present in source, set to null (don't guess)
- Re-scan source for missed fields before returning
</extraction_spec>

Web Research Prompts

xml
<research_guidelines>
- Browse the web for: time-sensitive topics, recommendations, navigational queries, ambiguous terms
- Include citations after paragraphs with web-derived claims
- Use multiple sources for key claims; prioritize primary sources
- Research until additional searching won't materially change the answer
- Structure output with Markdown: headers, bullets, tables for comparisons
</research_guidelines>

Example: Before/After

Without structure:

You're a financial analyst. Generate a Q2 report for investors. Include Revenue, Margins, Cash Flow. Use this data: {{DATA}}. Make it professional and concise.

With structure:

xml
You're a financial analyst at AcmeCorp generating a Q2 report for investors.

<context>
AcmeCorp is a B2B SaaS company. Investors value transparency and actionable insights.
</context>

<data>
{{DATA}}
</data>

<instructions>
1. Include sections: Revenue Growth, Profit Margins, Cash Flow
2. Highlight strengths and areas for improvement
3. Use concise, professional tone
</instructions>

<output_format>
- Use bullet points with metrics and YoY changes
- Include "Action:" items for areas needing improvement
- End with 2-3 bullet Outlook section
</output_format>

Prompt Migration Checklist

When adapting prompts across models or versions:

  1. Switch model, keep prompt identical — isolate the variable
  2. Pin reasoning/thinking depth to match prior model's profile
  3. Run evals — if results are good, ship
  4. If regressions, tune prompt — adjust verbosity/format/scope constraints
  5. Re-eval after each small change — one change at a time

Quick Reference

TechniqueTag PatternUse Case
Separate sections<context>, <instructions>, <data>Any complex prompt
Control length<output_spec> with word/bullet limitsPrevent verbosity
Prevent drift<constraints> with explicit "do NOT"Feature creep
Handle uncertainty<uncertainty_handling>Factual queries
Chain of thought<thinking>, <answer>Reasoning tasks
Extraction<schema> with JSON structureData parsing
Research<research_guidelines>Web-enabled agents
Self-check<self_check>High-risk domains
Tool usage<tool_usage_rules>Agentic systems
Eagerness control<persistence>, <context_gathering>Agent autonomy
Persona<role> + behavioral constraintsTone & style

Prompting Techniques Catalog

Comprehensive catalog of prompting techniques. Full details, examples, and academic references in references/prompting-techniques.md.

TechniqueUse Case
Zero-Shot PromptingDirect task execution without examples; classification, translation, summarization
Few-Shot PromptingIn-context learning via exemplars; format control, label calibration, style matching
Chain-of-Thought (CoT)Step-by-step reasoning; arithmetic, logic, commonsense reasoning tasks
Meta PromptingLLM as orchestrator delegating to specialized expert prompts; complex multi-domain tasks
Self-ConsistencySample multiple CoT paths, pick majority answer; boost accuracy on math & reasoning
Generated KnowledgeGenerate relevant knowledge first, then answer; commonsense & factual QA
Prompt ChainingBreak complex tasks into sequential subtasks; document analysis, multi-step workflows
Tree of Thoughts (ToT)Explore multiple reasoning branches with lookahead/backtracking; planning, puzzles
RAGRetrieve external documents before generating; knowledge-intensive tasks, fresh data
ART (Auto Reasoning + Tools)Auto-select and orchestrate tools with CoT; tasks requiring calculation, search, APIs
APE (Auto Prompt Engineer)LLM generates and scores candidate prompts; prompt optimization at scale
Active-PromptIdentify uncertain examples, annotate selectively for CoT; adaptive few-shot
Directional StimulusAdd a hint/keyword to guide generation direction; summarization, dialogue
PAL (Program-Aided LM)Generate code instead of text for reasoning; math, data manipulation, symbolic tasks
ReActInterleave reasoning traces with tool actions; search, QA, decision-making agents
ReflexionAgent self-reflects on failures with verbal feedback; iterative improvement, debugging
Multimodal CoTTwo-stage: rationale generation then answer with text+image; visual reasoning tasks
Graph PromptingStructured graph-based prompts; node classification, relation extraction, graph tasks
Prompting Fundamentals

LLM settings, prompt elements, formatting, and practical examples — see references/prompting-introduction.md. Covers:

  • LLM Settings — temperature, top-p, max length, stop sequences, frequency/presence penalties
  • Prompt Elements — instruction, context, input data, output indicator
  • Design Tips — start simple, be specific, avoid impreciseness, say what TO do (not what NOT to do)
  • Task Examples — summarization, extraction, QA, classification, conversation, code generation, reasoning
Risks & Misuses

Adversarial attacks, factuality issues, and bias mitigation — see references/prompting-risks.md. Covers:

  • Adversarial Prompting — prompt injection, prompt leaking, jailbreaking (DAN, Waluigi Effect), defense tactics
  • Factuality — ground truth grounding, calibrated confidence, admit-ignorance patterns
  • Biases — exemplar distribution skew, exemplar ordering effects, balanced few-shot design

Prompt Audit / Review

When asked to audit, review, or improve a prompt, follow this workflow. Full checklist with per-check references: prompt-audit-checklist.md.

Workflow
  1. Read the prompt fully — identify its purpose, target model, and deployment context (interactive chat, agentic system, batch pipeline, RAG-augmented)
  2. Walk 8 dimensions — check each, note issues with severity (Critical / Warning / Suggestion):
#DimensionWhat to Check
1Clarity & SpecificityTask definition, success criteria, audience, output format, conflicting constraints
2Structure & FormattingSection separation (XML tags), prompt smells (monolithic, mixed layers, negative bias)
3Safety & SecurityControl/data separation, secrets in prompt, injection resilience, tool permissions
4Hallucination & FactualityRole framing, grounding, citation-without-sources, uncertainty handling
5Context ManagementInfo placement (not buried in middle), context size, RAG doc count, re-grounding
6Maintainability & DebtHardcoded values, regenerated logic, model pinning, testability
7Model-Specific FitModel-specific params and gotchas (see Model-Specific Guides below)
8Evaluation ReadinessEval criteria, adversarial test cases, schema enforcement, monitoring
  1. Produce a report — issues table (dimension, check, severity, issue, fix) + rewritten prompt or targeted fix suggestions. Use the report template from the checklist reference.
  2. For each issue, cite the relevant reference file so the user can dive deeper.
Show full SKILL.md (405 more words)Show less
Quick Decision: Which Dimensions to Prioritize
  • User-facing chatbot → prioritize Safety (#3), Hallucination (#4), Clarity (#1)
  • Agentic system with tools → prioritize Safety (#3), Context (#5), Maintainability (#6)
  • Batch/pipeline → prioritize Structure (#2), Evaluation (#8), Maintainability (#6)
  • RAG-augmented → prioritize Context (#5), Safety (#3), Hallucination (#4)

Common Mistakes & Anti-Patterns

Three complementary layers — use the one matching your need:

Deep-dives by category — root causes, mechanisms, prevention checklists (from "The Architecture of Instruction", 2026):

Mistake CategoryKey IssuesReference
Hallucinations & LogicAmbiguity-induced confabulation, automation bias, overloaded prompts, logical failures in verification tasks, no role framingmistakes-hallucinations.md
Structural FragilityFormatting sensitivity (up to 76pp variance), reproducibility crisis, prompt smells catalog (6 anti-patterns), deliberation laddermistakes-structure.md
Context Rot"Lost in the middle" U-shaped attention, RAG over-retrieval, naive data loading, context engineering shiftmistakes-context.md
Prompt DebtToken tax of regenerative code, debt taxonomy (prompt/hyperparameter/framework/cost), multi-agent solutions, automated repairmistakes-debt.md
SecurityDirect/indirect injection, jailbreaking, system prompt leakage (OWASP LLM07:2025), RAG poisoning, multimodal injection, adversarial suffixesmistakes-security.md

Quick reference — 18-category taxonomy with MRPs, risk scores, case studies, action items: failure-taxonomy.md. Start here for an overview or to prioritize which categories to address first. Covers: control-plane vs data-plane model, heuristic risk scoring, real-world incidents (EchoLeak CVE-2025-32711, Mata v. Avianca, Samsung shadow AI).

How to measure & test — eval metrics, CI gating, red-teaming, tooling: evaluation-redteaming.md. Covers: TruthfulQA, FActScore, SelfCheckGPT, PromptBench, AILuminate, LLM-as-judge pitfalls, guardrail libraries, open research questions.

Model-Specific Guides

Each model family has unique parameters, gotchas, and patterns. Consult the reference for your target model:

  • Claude Family — Claude 4.x family defaults, parameters, tools, and migration patterns
  • Claude Fable 5.1 — five-level effort calibration, progress-update blocks, parallel tools, append-only history, completion and scope control, targeted edits, long-output budgeting, subagents, vision, and refusal handling
  • Claude Fable 5 — always-on adaptive thinking, lean instruction design, long-run progress grounding, action boundaries, memory, and migration notes
  • GPT-6 Astra — initiative, instruction hierarchy, writing style, subagent calibration, verification scope, async tools, mid-turn steering, effort changes, and migration constraints
  • GPT-5 Family — GPT-5 / 5.1 / 5.2 / 5.4 / 5.5: reasoning_effort, text.verbosity, named tools, agentic prompting, completeness/verification contracts, compaction, and migration paths
  • GPT-5.6 Sol — lean outcome-first prompts, autonomy boundaries, max effort and Pro mode, Programmatic Tool Calling, persisted reasoning, explicit caching, retrieval budgets, long-running state, frontend and visual verification, and migration workflow
  • Gemini 3 Family — Gemini 2.5/3/3.1: temperature MUST be 1.0, thinking_budget vs thinking_level, constraint placement (end of prompt), persona priority, function calling, structured output, multimodal, image generation
  • GPT-5.2 Specifics — Compaction API code examples, web research agent prompt, full XML specification blocks

© CodeAlive-AI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 20 other files (references) in skills/prompt-engineering of CodeAlive-AI/ai-driven-development.

  • SKILL.md
  • README.md
  • references/claude-fable5-prompting.md
  • references/claude-fable51-prompting.md
  • references/claude-family-prompting.md
  • references/evaluation-redteaming.md
  • references/failure-taxonomy.md
  • references/gemini3-family-prompting.md
  • references/gpt5-family-prompting.md
  • references/gpt5-prompting-guide.md
  • references/gpt56-sol-prompting.md
  • references/gpt6-astra-prompting.md
  • references/mistakes-context.md
  • references/mistakes-debt.md
  • references/mistakes-hallucinations.md
  • references/mistakes-security.md
  • references/mistakes-structure.md
  • references/prompt-audit-checklist.md
  • references/prompting-introduction.md
  • references/prompting-risks.md
  • … and 1 more

Open the folder on GitHubat commit 25b7b1d

Used in 1 other repository

We found 3 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in CodeAlive-AI/ai-driven-development, which our catalogue first saw on October 7, 2026.

Compare with similar skills

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Prompt Engineering Patternsynulihao/AgentSkillOS61714 repos~1.7kAutomated safety check: PassNone
Patch CreationPiebald-AI/tweakcc2.5k—~1.6kAutomated safety check: PassMIT
LLM Application DevMoizIbnYousaf/ai-agent-skills1.1k2 repos~1.3kAutomated safety check: PassMIT
Senior Prompt Engineermaslennikov-ig/claude-code-orchestrator-kit2594 repos~1.4kAutomated safety check: PassCustom licence

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Questions about Prompt Engineering

What does Prompt Engineering do?

Universal prompt engineering techniques for any LLM. An agent skill from CodeAlive-AI/ai-driven-development. Prompt Engineering is an agent skill from CodeAlive-AI/ai-driven-development. Universal prompt engineering techniques for any LLM.

When should I use Prompt Engineering?

Prompt Engineering fits situations like: reviewing prompts for AI models; requests like improve this prompt; write a system prompt; optimize my instructions.

How do I install Prompt Engineering in Claude Code?

Run `npx skills add CodeAlive-AI/ai-driven-development --skill prompt-engineering -a claude-code`. Or copy the skill folder (skills/prompt-engineering in CodeAlive-AI/ai-driven-development) into .claude/skills/prompt-engineering in your project. Claude Code loads it when a task matches its description.

How do I install Prompt Engineering in Codex?

Run `npx skills add CodeAlive-AI/ai-driven-development --skill prompt-engineering -a codex`. Or copy the skill folder (skills/prompt-engineering in CodeAlive-AI/ai-driven-development) into .agents/skills/prompt-engineering in your project. Codex loads it when a task matches its description.

Can I use Prompt Engineering in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add CodeAlive-AI/ai-driven-development --skill prompt-engineering -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/prompt-engineering, .gemini/skills/prompt-engineering, .github/skills/prompt-engineering and .opencode/skills/prompt-engineering in your project.

What does Prompt Engineering need to run?

SKILL.md names no scripts, command-line tools or credentials: Prompt Engineering is instructions for the agent only.

Does Prompt Engineering access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Prompt Engineering safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Prompt Engineering use?

Prompt Engineering is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Prompt Engineering use?

About 4.1k tokens (SKILL.md is roughly 16k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 73k tokens, read only when the agent opens those files.

What are the alternatives to Prompt Engineering?

Skills that share tags, products or a category with Prompt Engineering: Prompt Improver (severity1/claude-code-prompt-improver, 1.9k stars), Prompt Engineering Patterns (ynulihao/AgentSkillOS, 617 stars), Patch Creation (Piebald-AI/tweakcc, 2.5k stars) and LLM Application Dev (MoizIbnYousaf/ai-agent-skills, 1.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Prompt Engineering?

CodeAlive-AI (a GitHub organization) maintains it in CodeAlive-AI/ai-driven-development, which has 155 GitHub stars. The repository holds 22 skills in this directory. The repository was last updated on October 6, 2026.

Source: CodeAlive-AI/ai-driven-development on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.