Agent Eval
ericrisco/rsc-harness
A skill your agent uses when measuring whether an LLM or agent system actually got better and gating merges on it: golden sets, fixing an inflated LLM-as-judge, scoring RAG (faithfulness, contextual…
Expert in building comprehensive AI systems, integrating LLMs, RAG architectures, and autonomous agents into production applications.
Install AI Engineer
$ npx skills add majiayu000/claude-skill-registry --skill ai-engineer -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install majiayu000/claude-skill-registry ai-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/ai-engineer-skill .claude/skills/ai-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 "ai-engineer" agent skill from https://github.com/majiayu000/claude-skill-registry/tree/main/skills/ai-llm/ai-engineer-skill into .claude/skills/ai-engineer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-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/ai-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 ai-engineer -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install majiayu000/claude-skill-registry ai-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/ai-engineer-skill .agents/skills/ai-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 "ai-engineer" agent skill from https://github.com/majiayu000/claude-skill-registry/tree/main/skills/ai-llm/ai-engineer-skill into .agents/skills/ai-engineer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-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 ai-engineer -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install majiayu000/claude-skill-registry ai-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/ai-engineer-skill .cursor/skills/ai-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 "ai-engineer" agent skill from https://github.com/majiayu000/claude-skill-registry/tree/main/skills/ai-llm/ai-engineer-skill into .cursor/skills/ai-engineer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-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/ai-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 ai-engineer -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install majiayu000/claude-skill-registry ai-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/ai-engineer-skill .gemini/skills/ai-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 "ai-engineer" agent skill from https://github.com/majiayu000/claude-skill-registry/tree/main/skills/ai-llm/ai-engineer-skill into .gemini/skills/ai-engineer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-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 ai-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 ai-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/ai-engineer-skill .github/skills/ai-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 "ai-engineer" agent skill from https://github.com/majiayu000/claude-skill-registry/tree/main/skills/ai-llm/ai-engineer-skill into .github/skills/ai-engineer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-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 ai-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 ai-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/ai-engineer-skill .opencode/skills/ai-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 "ai-engineer" agent skill from https://github.com/majiayu000/claude-skill-registry/tree/main/skills/ai-llm/ai-engineer-skill into .opencode/skills/ai-engineer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-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
ai-engineer- GitHub stars
- 666
- Used in
- 1 other repo
- Token cost
- ~848 tokens
- SKILL.md length
- 352 words
- Files
- 2
- Skills in repo
- 971
- Repo updated
- First seen
- Licence
- MIT
At a glance
Expert in building comprehensive AI systems, integrating LLMs, RAG architectures, and autonomous agents into production applications.
- Works in 3 steps: RAG Pipeline Implementation → LLM Integration → AI Agent Development
- Building AI-powered features
- 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
AI Engineer is an agent skill from majiayu000/claude-skill-registry. Expert in building comprehensive AI systems, integrating LLMs, RAG architectures, and autonomous agents into production applications. Use when building AI-powered features, implementing LLM integrations, designing RAG pipelines, or deploying AI systems.
Its SKILL.md is about 850 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 Retrieval-augmented generation and Autonomous loops. 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
- Building AI-powered features
- Implementing LLM integrations
- Designing RAG pipelines
- Deploying AI systems
Example prompts
- “/ai-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 000116a. 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
AI Engineer loads about 848 tokens when it runs. Until then it costs about 66 tokens; SKILL.md has 352 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 000116a, republished under its MIT licence (© majiayu000). 352 words, ~848 tokens.
Download SKILL.mdSave it as
.claude/skills/ai-engineer/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.- name
- ai-engineer
- description
- Expert in building comprehensive AI systems, integrating LLMs, RAG architectures, and autonomous agents into production applications. Use when building AI-powered features, implementing LLM integrations, designing RAG pipelines, or deploying AI systems.
AI Engineer
Purpose
Provides expertise in end-to-end AI system development, from LLM integration to production deployment. Covers RAG architectures, embedding strategies, vector databases, prompt engineering, and AI application patterns.
When to Use
- Building LLM-powered applications or features
- Implementing RAG (Retrieval-Augmented Generation) systems
- Integrating AI APIs (OpenAI, Anthropic, etc.)
- Designing embedding and vector search pipelines
- Building chatbots or conversational AI
- Implementing AI agents with tool use
- Optimizing AI system latency and cost
Quick Start
Invoke this skill when:
- Building LLM-powered applications or features
- Implementing RAG systems with vector databases
- Integrating AI APIs into applications
- Designing embedding and retrieval pipelines
- Building conversational AI or agents
Do NOT invoke when:
- Training custom ML models from scratch (use ml-engineer)
- Deploying ML models to production infrastructure (use mlops-engineer)
- Managing multi-agent coordination (use agent-organizer)
- Optimizing LLM serving infrastructure (use llm-architect)
Decision Framework
AI Feature Type:
├── Simple Q&A → Direct LLM API call
├── Knowledge-based answers → RAG pipeline
├── Multi-step reasoning → Chain-of-thought or agents
├── External actions needed → Tool-use agents
├── Real-time data → Streaming + function calling
└── Complex workflows → Multi-agent orchestrationCore Workflows
1. RAG Pipeline Implementation
- Chunk documents with appropriate strategy
- Generate embeddings using suitable model
- Store in vector database with metadata
- Implement semantic search with reranking
- Construct prompts with retrieved context
- Add evaluation and monitoring
2. LLM Integration
- Select appropriate model for use case
- Design prompt templates with versioning
- Implement structured output parsing
- Add retry logic and fallbacks
- Monitor token usage and costs
- Cache responses where appropriate
Show full SKILL.md (136 more words)Show less
3. AI Agent Development
- Define agent capabilities and tools
- Implement tool interfaces with validation
- Design agent loop with termination conditions
- Add guardrails and safety checks
- Implement logging and tracing
- Test edge cases and failure modes
Best Practices
- Version prompts alongside application code
- Use structured outputs (JSON mode) for reliability
- Implement semantic caching for common queries
- Add human-in-the-loop for critical decisions
- Monitor hallucination rates and retrieval quality
- Design for graceful degradation when AI fails
Anti-Patterns
| Anti-Pattern | Problem | Correct Approach |
|---|---|---|
| Prompt in code | Hard to iterate and test | Use prompt templates with versioning |
| No evaluation | Unknown quality in production | Implement eval pipelines |
| Synchronous LLM calls | Slow user experience | Use streaming responses |
| Unbounded context | Token limits and cost | Implement context windowing |
| No fallbacks | System fails on API errors | Add retry logic and alternatives |
© 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/ai-engineer-skill of majiayu000/claude-skill-registry.
- SKILL.md
- metadata.json
Open the folder on GitHubat commit 000116a
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
AI 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 |
|---|---|---|---|---|---|---|
| AI Engineer this skillmajiayu000/claude-skill-registry | 666 | 1 repos | ~848 | Automated safety check: Pass | MIT | |
| Agent Evalericrisco/rsc-harness | 156 | — | ~3.2k | Automated safety check: Pass | MIT | |
| RAG Architectalirezarezvani/claude-skills | 28k | — | ~1.1k | Automated safety check: Pass | MIT | |
| LangchainOrchestra-Research/AI-Research-SKILLs | 13k | 2 repos | ~3.2k | Automated safety check: Pass | MIT | |
| Amazon Bedrockaws/agent-toolkit-for-aws | 2.8k | — | ~8.6k | Automated safety check: Pass | Apache-2.0 | |
| Building Agentsericrisco/rsc-harness | 156 | — | ~5k | Automated safety check: Pass | MIT |
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Categories
Questions about AI Engineer
What does AI Engineer do?
Expert in building comprehensive AI systems, integrating LLMs, RAG architectures, and autonomous agents into production applications. AI Engineer is an agent skill from majiayu000/claude-skill-registry. Expert in building comprehensive AI systems, integrating LLMs, RAG architectures, and autonomous agents into production applications.
When should I use AI Engineer?
AI Engineer fits situations like: building AI-powered features; implementing LLM integrations; designing RAG pipelines; deploying AI systems.
How do I install AI Engineer in Claude Code?
Run `npx skills add majiayu000/claude-skill-registry --skill ai-engineer -a claude-code`. Or copy the skill folder (skills/ai-llm/ai-engineer-skill in majiayu000/claude-skill-registry) into .claude/skills/ai-engineer in your project. Claude Code loads it when a task matches its description.
How do I install AI Engineer in Codex?
Run `npx skills add majiayu000/claude-skill-registry --skill ai-engineer -a codex`. Or copy the skill folder (skills/ai-llm/ai-engineer-skill in majiayu000/claude-skill-registry) into .agents/skills/ai-engineer in your project. Codex loads it when a task matches its description.
Can I use AI 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 ai-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/ai-engineer, .gemini/skills/ai-engineer, .github/skills/ai-engineer and .opencode/skills/ai-engineer in your project.
What does AI Engineer need to run?
SKILL.md names no scripts, command-line tools or credentials: AI Engineer is instructions for the agent only.
Does AI 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 AI 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 AI Engineer use?
AI 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 AI Engineer use?
About 848 tokens (SKILL.md is roughly 3.4k 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 AI Engineer?
Skills that share tags, products or a category with AI Engineer: Agent Eval (ericrisco/rsc-harness, 156 stars), RAG Architect (alirezarezvani/claude-skills, 28k stars), Langchain (Orchestra-Research/AI-Research-SKILLs, 13k stars) and Amazon Bedrock (aws/agent-toolkit-for-aws, 2.8k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains AI Engineer?
majiayu000 (a GitHub user) maintains it in majiayu000/claude-skill-registry, which has 666 GitHub stars. The repository holds 971 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.