Canvas Templates
PostHog/code
How PostHog "canvas" dashboards work end-to-end — the two rendering tiers (json-render vs freeform React-in-iframe), the agent system prompts that steer each, and the RIGHT way to fetch PostHog data…
Expert in designing, optimizing, and evaluating prompts for Large Language Models.
Install Prompt Engineer
$ npx skills add majiayu000/claude-skill-registry --skill prompt-engineer -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install majiayu000/claude-skill-registry prompt-engineer --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/majiayu000/claude-skill-registry.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/ai-llm/prompt-engineer-skill .claude/skills/prompt-engineer && rm -rf skills-srcUse ~/.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/
Install the "prompt-engineer" agent skill from https://github.com/majiayu000/claude-skill-registry/tree/main/skills/ai-llm/prompt-engineer-skill into .claude/skills/prompt-engineer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "prompt-engineer", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/majiayu000/claude-skill-registry/tree/main/skills/ai-llm/prompt-engineer-skillType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add majiayu000/claude-skill-registry --skill prompt-engineer -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install majiayu000/claude-skill-registry prompt-engineer --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/majiayu000/claude-skill-registry.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/ai-llm/prompt-engineer-skill .agents/skills/prompt-engineer && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "prompt-engineer" agent skill from https://github.com/majiayu000/claude-skill-registry/tree/main/skills/ai-llm/prompt-engineer-skill into .agents/skills/prompt-engineer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "prompt-engineer", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add majiayu000/claude-skill-registry --skill prompt-engineer -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install majiayu000/claude-skill-registry prompt-engineer --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/majiayu000/claude-skill-registry.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/ai-llm/prompt-engineer-skill .cursor/skills/prompt-engineer && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "prompt-engineer" agent skill from https://github.com/majiayu000/claude-skill-registry/tree/main/skills/ai-llm/prompt-engineer-skill into .cursor/skills/prompt-engineer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "prompt-engineer", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/majiayu000/claude-skill-registry.git --path skills/ai-llm/prompt-engineer-skill--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add majiayu000/claude-skill-registry --skill prompt-engineer -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install majiayu000/claude-skill-registry prompt-engineer --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/majiayu000/claude-skill-registry.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/ai-llm/prompt-engineer-skill .gemini/skills/prompt-engineer && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "prompt-engineer" agent skill from https://github.com/majiayu000/claude-skill-registry/tree/main/skills/ai-llm/prompt-engineer-skill into .gemini/skills/prompt-engineer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "prompt-engineer", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install majiayu000/claude-skill-registry prompt-engineerInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add majiayu000/claude-skill-registry --skill prompt-engineer -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/majiayu000/claude-skill-registry.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/ai-llm/prompt-engineer-skill .github/skills/prompt-engineer && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "prompt-engineer" agent skill from https://github.com/majiayu000/claude-skill-registry/tree/main/skills/ai-llm/prompt-engineer-skill into .github/skills/prompt-engineer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "prompt-engineer", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add majiayu000/claude-skill-registry --skill prompt-engineer -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install majiayu000/claude-skill-registry prompt-engineer --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/majiayu000/claude-skill-registry.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/ai-llm/prompt-engineer-skill .opencode/skills/prompt-engineer && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "prompt-engineer" agent skill from https://github.com/majiayu000/claude-skill-registry/tree/main/skills/ai-llm/prompt-engineer-skill into .opencode/skills/prompt-engineer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "prompt-engineer", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
prompt-engineer- GitHub stars
- 666
- Used in
- 1 other repo
- Token cost
- ~794 tokens
- SKILL.md length
- 289 words
- Files
- 2
- Skills in repo
- 1,273
- Repo updated
- First seen
- Licence
- MIT
At a glance
Expert in designing, optimizing, and evaluating prompts for Large Language Models.
- Works in 3 steps: Prompt Design → Chain-of-Thought Implementation → Prompt Optimization
- Crafting prompts
- SKILL.md covers Purpose, When to Use, Quick Start and Decision Framework, plus 3 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Prompt Engineer is an agent skill from majiayu000/claude-skill-registry. Expert in designing, optimizing, and evaluating prompts for Large Language Models. Specializes in Chain-of-Thought, ReAct, few-shot learning, and production prompt management. Use when crafting prompts, optimizing LLM outputs, or building prompt systems. Triggers include "prompt engineering", "prompt optimization", "chain of thought", "few-shot", "prompt template", "LLM prompting".
Its SKILL.md is about 790 tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `metadata.json`).
It sits in AI & LLM Engineering, covering Prompt engineering. It works with React. The repository describes itself as: Searchable Claude Code skills catalog with source-linked guides and generated registry artifacts. The licence is MIT.
When your agent uses it
- Crafting prompts
- Optimizing LLM outputs
- Building prompt systems
- Include prompt engineering
Example prompts
- “prompt engineering”
- “prompt optimization”
- “chain of thought”
- “/prompt-engineer”
Workflow steps
3 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 2d14a69. 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.
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 Engineer loads about 794 tokens when it runs. Until then it costs about 100 tokens; SKILL.md has 289 words of instructions outside code blocks.
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 majiayu000/claude-skill-registry at commit 2d14a69, republished under its MIT licence (© majiayu000). 289 words, ~794 tokens.
Download SKILL.mdSave it as
.claude/skills/prompt-engineer/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.- name
- prompt-engineer
- description
- Expert in designing, optimizing, and evaluating prompts for Large Language Models. Specializes in Chain-of-Thought, ReAct, few-shot learning, and production prompt management. Use when crafting prompts, optimizing LLM outputs, or building prompt systems. Triggers include "prompt engineering", "prompt optimization", "chain of thought", "few-shot", "prompt template", "LLM prompting".
Prompt Engineer
Purpose
Provides expertise in designing, optimizing, and evaluating prompts for Large Language Models. Specializes in prompting techniques like Chain-of-Thought, ReAct, and few-shot learning, as well as production prompt management and evaluation.
When to Use
- Designing prompts for LLM applications
- Optimizing prompt performance
- Implementing Chain-of-Thought reasoning
- Creating few-shot examples
- Building prompt templates
- Evaluating prompt effectiveness
- Managing prompts in production
- Reducing hallucinations through prompting
Quick Start
Invoke this skill when:
- Crafting prompts for LLM applications
- Optimizing existing prompts
- Implementing advanced prompting techniques
- Building prompt management systems
- Evaluating prompt quality
Do NOT invoke when:
- LLM system architecture → use
/llm-architect - RAG implementation → use
/ai-engineer - NLP model training → use
/nlp-engineer - Agent performance monitoring → use
/performance-monitor
Decision Framework
Prompting Technique?
├── Reasoning Tasks
│ ├── Step-by-step → Chain-of-Thought
│ └── Tool use → ReAct
├── Classification/Extraction
│ ├── Clear categories → Zero-shot + examples
│ └── Complex → Few-shot with edge cases
├── Generation
│ └── Structured output → JSON mode + schema
└── Consistency
└── System prompt + temperature tuningCore Workflows
1. Prompt Design
- Define task clearly
- Choose prompting technique
- Write system prompt with context
- Add examples if few-shot
- Specify output format
- Test with diverse inputs
2. Chain-of-Thought Implementation
- Identify reasoning requirements
- Add "Let's think step by step" or equivalent
- Provide reasoning examples
- Structure expected reasoning steps
- Test reasoning quality
- Iterate on step guidance
3. Prompt Optimization
- Establish baseline metrics
- Identify failure patterns
- Adjust instructions for clarity
- Add/modify examples
- Tune output constraints
- Measure improvement
Best Practices
- Be specific and explicit in instructions
- Use structured output formats (JSON, XML)
- Include examples for complex tasks
- Test with edge cases and adversarial inputs
- Version control prompts
- Measure and track prompt performance
Anti-Patterns
| Anti-Pattern | Problem | Correct Approach |
|---|---|---|
| Vague instructions | Inconsistent output | Be specific and explicit |
| No examples | Poor performance on complex tasks | Add few-shot examples |
| Unstructured output | Hard to parse | Specify format clearly |
| No testing | Unknown failure modes | Test diverse inputs |
| Prompt in code | Hard to iterate | Separate prompt management |
© majiayu000, 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 1 other file in skills/ai-llm/prompt-engineer-skill of majiayu000/claude-skill-registry.
- SKILL.md
- metadata.json
Open the folder on GitHubat commit 2d14a69
Used in 1 other repository
We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in majiayu000/claude-skill-registry, which our catalogue first saw on October 7, 2026.
Compare with similar skills
Prompt Engineer 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Prompt Engineer this skillmajiayu000/claude-skill-registry | 666 | 1 repos | ~794 | Automated safety check: Pass | MIT | |
| Canvas TemplatesPostHog/code | 179 | — | ~2.2k | Automated safety check: Pass | MIT | |
| Thought Based ReasoningNeoLabHQ/context-engineering-kit | 1.7k | 1 repos | ~5.5k | Automated safety check: Pass | GPL-3.0 | |
| Building Agent Systemstelagod/code-abyss | 243 | — | ~691 | Automated safety check: Pass | MIT | |
| Prompt Engineering Patternslamm-mit/scienceclaw | 244 | — | ~522 | Automated safety check: Pass | Apache-2.0 | |
| Dspymagnus919/agent-skills | 113 | — | ~2k | Automated safety check: Pass | MIT |
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- Official
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NeoLabHQ/context-engineering-kit
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lamm-mit/scienceclaw
Generate optimized LLM prompts using chain-of-thought, ReAct, and other scientific reasoning patterns
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Questions about Prompt Engineer
What does Prompt Engineer do?
Expert in designing, optimizing, and evaluating prompts for Large Language Models. Prompt Engineer is an agent skill from majiayu000/claude-skill-registry. Expert in designing, optimizing, and evaluating prompts for Large Language Models.
When should I use Prompt Engineer?
Prompt Engineer fits situations like: crafting prompts; optimizing LLM outputs; building prompt systems; include prompt engineering.
How do I install Prompt Engineer in Claude Code?
Run `npx skills add majiayu000/claude-skill-registry --skill prompt-engineer -a claude-code`. Or copy the skill folder (skills/ai-llm/prompt-engineer-skill in majiayu000/claude-skill-registry) into .claude/skills/prompt-engineer in your project. Claude Code loads it when a task matches its description.
How do I install Prompt Engineer in Codex?
Run `npx skills add majiayu000/claude-skill-registry --skill prompt-engineer -a codex`. Or copy the skill folder (skills/ai-llm/prompt-engineer-skill in majiayu000/claude-skill-registry) into .agents/skills/prompt-engineer in your project. Codex loads it when a task matches its description.
Can I use Prompt Engineer 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 majiayu000/claude-skill-registry --skill prompt-engineer -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-engineer, .gemini/skills/prompt-engineer, .github/skills/prompt-engineer and .opencode/skills/prompt-engineer in your project.
What does Prompt Engineer need to run?
SKILL.md names no scripts, command-line tools or credentials: Prompt Engineer is instructions for the agent only.
Does Prompt Engineer 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 Engineer 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 Engineer use?
Prompt Engineer 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 Engineer use?
About 794 tokens (SKILL.md is roughly 3.2k 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 Engineer?
Skills that share tags, products or a category with Prompt Engineer: Canvas Templates (PostHog/code, 179 stars), Thought Based Reasoning (NeoLabHQ/context-engineering-kit, 1.7k stars), Building Agent Systems (telagod/code-abyss, 243 stars) and Prompt Engineering Patterns (lamm-mit/scienceclaw, 244 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Prompt Engineer?
majiayu000 (a GitHub user) maintains it in majiayu000/claude-skill-registry, which has 666 GitHub stars. The repository holds 1,273 skills in this directory. The repository was last updated on October 7, 2026.
Source: majiayu000/claude-skill-registry on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.