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Agent skill

Prompt Engineering

by davila7 in davila7/claude-code-templates

Expert guide on prompt engineering patterns, best practices, and optimization techniques.

MITAuto-check passedAI & LLM Engineering

Install Prompt Engineering

skills CLI
$ npx skills add davila7/claude-code-templates --skill prompt-engineering -a claude-code

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

GitHub CLI
$ gh skill install davila7/claude-code-templates 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/davila7/claude-code-templates.git skills-src && mkdir -p .claude/skills && cp -r skills-src/cli-tool/components/skills/ai-research/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
32k
Used in
7 other repos
Token cost
~1.4k tokens
SKILL.md length
445 words
Files
1
Skills in repo
478
Repo updated
First seen
Licence
MIT

At a glance

Expert guide on prompt engineering patterns, best practices, and optimization techniques.

  • Works in 5 steps: Few-Shot Learning → Chain-of-Thought Prompting → Prompt Optimization → …
  • User wants to improve prompts
  • SKILL.md covers Core Capabilities, Key Patterns, Best Practices and Common Pitfalls
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Prompt Engineering is an agent skill from davila7/claude-code-templates. Expert guide on prompt engineering patterns, best practices, and optimization techniques. Use when user wants to improve prompts, learn prompting strategies, or debug agent behavior.

Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in AI & LLM Engineering, covering Prompt engineering. The repository describes itself as: CLI tool for configuring and monitoring Claude Code. The licence is MIT.

When your agent uses it

  • User wants to improve prompts
  • Learn prompting strategies
  • Debug agent behavior

Example prompts

  • “/prompt-engineering”

Requirements

  • Python 3

Workflow steps

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

  1. Few-Shot Learning
  2. Chain-of-Thought Prompting
  3. Prompt Optimization
  4. Template Systems
  5. System Prompt Design

What it can do on your machine

Read from SKILL.md and the folder at commit 46b4d8b. 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 markdown and python).

    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 1.4k tokens when it runs. Until then it costs about 50 tokens; SKILL.md has 445 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~50
When it runs · the whole SKILL.md, loaded when a task matches
~1.4k

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 davila7/claude-code-templates at commit 46b4d8b, republished under its MIT licence (© davila7). 445 words, ~1,411 tokens.

Download SKILL.mdSave it as .claude/skills/prompt-engineering/SKILL.md (or your agent's skills folder).
name
prompt-engineering
description
Expert guide on prompt engineering patterns, best practices, and optimization techniques. Use when user wants to improve prompts, learn prompting strategies, or debug agent behavior.

Prompt Engineering Patterns

Advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability.

Core Capabilities

1. Few-Shot Learning

Teach the model by showing examples instead of explaining rules. Include 2-5 input-output pairs that demonstrate the desired behavior. Use when you need consistent formatting, specific reasoning patterns, or handling of edge cases. More examples improve accuracy but consume tokens—balance based on task complexity.

Example:

markdown
Extract key information from support tickets:

Input: "My login doesn't work and I keep getting error 403"
Output: {"issue": "authentication", "error_code": "403", "priority": "high"}

Input: "Feature request: add dark mode to settings"
Output: {"issue": "feature_request", "error_code": null, "priority": "low"}

Now process: "Can't upload files larger than 10MB, getting timeout"
2. Chain-of-Thought Prompting

Request step-by-step reasoning before the final answer. Add "Let's think step by step" (zero-shot) or include example reasoning traces (few-shot). Use for complex problems requiring multi-step logic, mathematical reasoning, or when you need to verify the model's thought process. Improves accuracy on analytical tasks by 30-50%.

Example:

markdown
Analyze this bug report and determine root cause.

Think step by step:

1. What is the expected behavior?
2. What is the actual behavior?
3. What changed recently that could cause this?
4. What components are involved?
5. What is the most likely root cause?

Bug: "Users can't save drafts after the cache update deployed yesterday"
3. Prompt Optimization

Systematically improve prompts through testing and refinement. Start simple, measure performance (accuracy, consistency, token usage), then iterate. Test on diverse inputs including edge cases. Use A/B testing to compare variations. Critical for production prompts where consistency and cost matter.

Example:

markdown
Version 1 (Simple): "Summarize this article"
→ Result: Inconsistent length, misses key points

Version 2 (Add constraints): "Summarize in 3 bullet points"
→ Result: Better structure, but still misses nuance

Version 3 (Add reasoning): "Identify the 3 main findings, then summarize each"
→ Result: Consistent, accurate, captures key information
4. Template Systems

Build reusable prompt structures with variables, conditional sections, and modular components. Use for multi-turn conversations, role-based interactions, or when the same pattern applies to different inputs. Reduces duplication and ensures consistency across similar tasks.

Example:

python
# Reusable code review template
template = """
Review this {language} code for {focus_area}.

Code:
{code_block}

Provide feedback on:
{checklist}
"""

# Usage
prompt = template.format(
    language="Python",
    focus_area="security vulnerabilities",
    code_block=user_code,
    checklist="1. SQL injection\n2. XSS risks\n3. Authentication"
)
5. System Prompt Design

Set global behavior and constraints that persist across the conversation. Define the model's role, expertise level, output format, and safety guidelines. Use system prompts for stable instructions that shouldn't change turn-to-turn, freeing up user message tokens for variable content.

Example:

markdown
System: You are a senior backend engineer specializing in API design.

Rules:

- Always consider scalability and performance
- Suggest RESTful patterns by default
- Flag security concerns immediately
- Provide code examples in Python
- Use early return pattern

Format responses as:

1. Analysis
2. Recommendation
3. Code example
4. Trade-offs

Key Patterns

Show full SKILL.md (204 more words)Show less
Progressive Disclosure

Start with simple prompts, add complexity only when needed:

  1. Level 1: Direct instruction

    • "Summarize this article"

  2. Level 2: Add constraints

    • "Summarize this article in 3 bullet points, focusing on key findings"

  3. Level 3: Add reasoning

    • "Read this article, identify the main findings, then summarize in 3 bullet points"

  4. Level 4: Add examples

    • Include 2-3 example summaries with input-output pairs
Instruction Hierarchy
[System Context] → [Task Instruction] → [Examples] → [Input Data] → [Output Format]
Error Recovery

Build prompts that gracefully handle failures:

  • Include fallback instructions
  • Request confidence scores
  • Ask for alternative interpretations when uncertain
  • Specify how to indicate missing information

Best Practices

  1. Be Specific: Vague prompts produce inconsistent results
  2. Show, Don't Tell: Examples are more effective than descriptions
  3. Test Extensively: Evaluate on diverse, representative inputs
  4. Iterate Rapidly: Small changes can have large impacts
  5. Monitor Performance: Track metrics in production
  6. Version Control: Treat prompts as code with proper versioning
  7. Document Intent: Explain why prompts are structured as they are

Common Pitfalls

  • Over-engineering: Starting with complex prompts before trying simple ones
  • Example pollution: Using examples that don't match the target task
  • Context overflow: Exceeding token limits with excessive examples
  • Ambiguous instructions: Leaving room for multiple interpretations
  • Ignoring edge cases: Not testing on unusual or boundary inputs

© davila7, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in cli-tool/components/skills/ai-research/prompt-engineering of davila7/claude-code-templates.

Open the folder on GitHubat commit 46b4d8b

Used in 7 other repositories

We found 22 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 7 other GitHub owners. This page covers the copy in davila7/claude-code-templates, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Prompt Engineering next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Prompt Engineering compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Prompt Engineering this skilldavila7/claude-code-templates32k7 repos~1.4kAutomated safety check: PassMIT
Prompt Improverseverity1/claude-code-prompt-improver1.9k1 repos~1.7kAutomated safety check: PassMIT
Prompt Engineering Patternsynulihao/AgentSkillOS61714 repos~1.7kAutomated safety check: PassNone
Patch CreationPiebald-AI/tweakcc2.5k—~1.6kAutomated safety check: PassMIT
Senior Prompt Engineermaslennikov-ig/claude-code-orchestrator-kit2603 repos~1.4kAutomated safety check: PassCustom licence
Codex Fable5baskduf/FableCodex437—~1.6kAutomated safety check: PassAGPL-3.0

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  • Prompt Improver

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Categories

Questions about Prompt Engineering

What does Prompt Engineering do?

Expert guide on prompt engineering patterns, best practices, and optimization techniques. Prompt Engineering is an agent skill from davila7/claude-code-templates. Expert guide on prompt engineering patterns, best practices, and optimization techniques.

When should I use Prompt Engineering?

Prompt Engineering fits situations like: user wants to improve prompts; learn prompting strategies; debug agent behavior.

How do I install Prompt Engineering in Claude Code?

Run `npx skills add davila7/claude-code-templates --skill prompt-engineering -a claude-code`. Or copy the skill folder (cli-tool/components/skills/ai-research/prompt-engineering in davila7/claude-code-templates) 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 davila7/claude-code-templates --skill prompt-engineering -a codex`. Or copy the skill folder (cli-tool/components/skills/ai-research/prompt-engineering in davila7/claude-code-templates) 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 davila7/claude-code-templates --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. Our summary lists: Python 3.

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 1.4k tokens (SKILL.md is roughly 5.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

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 Senior Prompt Engineer (maslennikov-ig/claude-code-orchestrator-kit, 260 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Prompt Engineering?

davila7 (a GitHub user) maintains it in davila7/claude-code-templates, which has 32,483 GitHub stars. The repository holds 478 skills in this directory. The repository was last updated on October 9, 2026.

Source: davila7/claude-code-templates on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.