Using Agent Skills
addyosmani/agent-skills
Meta-skill for choosing which workflow skill fits the task at hand, plus always-on habits: surface assumptions, stop on confusion, push back, keep it simple and stay in scope.
A skill your agent uses when designing how one canonical agent core runs across Codex, Claude Code, Gemini CLI, Cursor, and AGENTS.md-compatible tools.
Install LLM Runtime Architecture
$ npx skills add agentlas-ai/Agentlas-OS --skill llm-runtime-architecture -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install agentlas-ai/Agentlas-OS llm-runtime-architecture --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/agentlas-ai/Agentlas-OS.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/llm-runtime-architecture .claude/skills/llm-runtime-architecture && 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 "llm-runtime-architecture" agent skill from https://github.com/agentlas-ai/Agentlas-OS/tree/main/skills/llm-runtime-architecture into .claude/skills/llm-runtime-architecture/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "llm-runtime-architecture", 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/agentlas-ai/Agentlas-OS/tree/main/skills/llm-runtime-architectureType 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 agentlas-ai/Agentlas-OS --skill llm-runtime-architecture -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install agentlas-ai/Agentlas-OS llm-runtime-architecture --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/agentlas-ai/Agentlas-OS.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/llm-runtime-architecture .agents/skills/llm-runtime-architecture && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "llm-runtime-architecture" agent skill from https://github.com/agentlas-ai/Agentlas-OS/tree/main/skills/llm-runtime-architecture into .agents/skills/llm-runtime-architecture/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "llm-runtime-architecture", 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 agentlas-ai/Agentlas-OS --skill llm-runtime-architecture -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install agentlas-ai/Agentlas-OS llm-runtime-architecture --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/agentlas-ai/Agentlas-OS.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/llm-runtime-architecture .cursor/skills/llm-runtime-architecture && 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 "llm-runtime-architecture" agent skill from https://github.com/agentlas-ai/Agentlas-OS/tree/main/skills/llm-runtime-architecture into .cursor/skills/llm-runtime-architecture/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "llm-runtime-architecture", 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/agentlas-ai/Agentlas-OS.git --path skills/llm-runtime-architecture--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 agentlas-ai/Agentlas-OS --skill llm-runtime-architecture -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install agentlas-ai/Agentlas-OS llm-runtime-architecture --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/agentlas-ai/Agentlas-OS.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/llm-runtime-architecture .gemini/skills/llm-runtime-architecture && 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 "llm-runtime-architecture" agent skill from https://github.com/agentlas-ai/Agentlas-OS/tree/main/skills/llm-runtime-architecture into .gemini/skills/llm-runtime-architecture/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "llm-runtime-architecture", 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 agentlas-ai/Agentlas-OS llm-runtime-architectureInstalls 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 agentlas-ai/Agentlas-OS --skill llm-runtime-architecture -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/agentlas-ai/Agentlas-OS.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/llm-runtime-architecture .github/skills/llm-runtime-architecture && 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 "llm-runtime-architecture" agent skill from https://github.com/agentlas-ai/Agentlas-OS/tree/main/skills/llm-runtime-architecture into .github/skills/llm-runtime-architecture/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "llm-runtime-architecture", 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 agentlas-ai/Agentlas-OS --skill llm-runtime-architecture -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install agentlas-ai/Agentlas-OS llm-runtime-architecture --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/agentlas-ai/Agentlas-OS.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/llm-runtime-architecture .opencode/skills/llm-runtime-architecture && 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 "llm-runtime-architecture" agent skill from https://github.com/agentlas-ai/Agentlas-OS/tree/main/skills/llm-runtime-architecture into .opencode/skills/llm-runtime-architecture/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "llm-runtime-architecture", 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
llm-runtime-architecture- GitHub stars
- 1.6k
- Token cost
- ~197 tokens
- SKILL.md length
- 73 words
- Files
- 1
- Skills in repo
- 53
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
A skill your agent uses when designing how one canonical agent core runs across Codex, Claude Code, Gemini CLI, Cursor, and AGENTS.md-compatible tools.
- Works in 5 steps: Keep AGENTS.md as the canonical behavior… → For each runtime, name entry point,… → Keep adapters thin and point them back… → …
- Designing how one canonical agent core runs across Codex
- SKILL.md covers Procedure and Output
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
LLM Runtime Architecture is an agent skill from agentlas-ai/Agentlas-OS. Use when designing how one canonical agent core runs across Codex, Claude Code, Gemini CLI, Cursor, and AGENTS.md-compatible tools.
Its SKILL.md is about 200 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 Agent Workflows, covering Agent instruction files. The repository describes itself as: Agent OS: keep specialist agents in a hub, spin up a temporary orchestrator per task. Local-first, works with any model. The licence is Apache-2.0.
When your agent uses it
- Designing how one canonical agent core runs across Codex
- AGENTS.md-compatible tools
Example prompts
- “/llm-runtime-architecture”
Workflow steps
5 steps, taken from the first numbered list in SKILL.md.
- Keep AGENTS.md as the canonical behavior contract.
- For each runtime, name entry point, global command, adapter files, available
- Keep adapters thin and point them back to the canonical core.
- Write or repair .agentlas/global-commands.json when creating or packaging
- State unsupported capabilities explicitly.
What it can do on your machine
Read from SKILL.md and the folder at commit cfdebf8. 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
LLM Runtime Architecture loads about 197 tokens when it runs. Until then it costs about 39 tokens; SKILL.md has 73 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 agentlas-ai/Agentlas-OS at commit cfdebf8, republished under its Apache-2.0 licence (© agentlas-ai). 73 words, ~197 tokens.
Download SKILL.mdSave it as
.claude/skills/llm-runtime-architecture/SKILL.md (or your agent's skills folder).- name
- llm-runtime-architecture
- description
- Use when designing how one canonical agent core runs across Codex, Claude Code, Gemini CLI, Cursor, and AGENTS.md-compatible tools.
LLM Runtime Architecture
Procedure
- Keep
AGENTS.mdas the canonical behavior contract. - For each runtime, name entry point, global command, adapter files, available tools, memory access, limitations, and verification command.
- Keep adapters thin and point them back to the canonical core.
- Write or repair
.agentlas/global-commands.jsonwhen creating or packaging an agent. - State unsupported capabilities explicitly.
Output
Return a runtime matrix with runtime, entry_point, global_command,
adapter_files, memory_access, limitations, and verification.
© agentlas-ai, Apache-2.0. 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 skills/llm-runtime-architecture of agentlas-ai/Agentlas-OS.
Open the folder on GitHubat commit cfdebf8
Compare with similar skills
LLM Runtime Architecture 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 |
|---|---|---|---|---|---|---|
| LLM Runtime Architecture this skillagentlas-ai/Agentlas-OS | 1.6k | — | ~197 | Automated safety check: Pass | Apache-2.0 | |
| Using Agent Skillsaddyosmani/agent-skills | 103k | 4 repos | ~2.4k | Automated safety check: Pass | MIT | |
| Claude ReflectBayramAnnakov/claude-reflect | 1.7k | 2 repos | ~627 | Automated safety check: Pass | MIT | |
| Writing For Agentsbestofjs/bestofjs | 3.1k | 19 repos | ~2.7k | Automated safety check: Pass | MIT | |
| Neat-Freak Knowledge CloseoutKKKKhazix/khazix-skills | 21k | — | ~1.9k | Automated safety check: Pass | MIT | |
| Task Observerrebelytics/one-skill-to-rule-them-all | 3.2k | 1 repos | ~12k | Automated safety check: Pass | CC-BY-4.0 |
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Related
Categories
Questions about LLM Runtime Architecture
What does LLM Runtime Architecture do?
A skill your agent uses when designing how one canonical agent core runs across Codex, Claude Code, Gemini CLI, Cursor, and AGENTS.md-compatible tools. LLM Runtime Architecture is an agent skill from agentlas-ai/Agentlas-OS.md-compatible tools.
When should I use LLM Runtime Architecture?
LLM Runtime Architecture fits situations like: designing how one canonical agent core runs across Codex; AGENTS.md-compatible tools.
How do I install LLM Runtime Architecture in Claude Code?
Run `npx skills add agentlas-ai/Agentlas-OS --skill llm-runtime-architecture -a claude-code`. Or copy the skill folder (skills/llm-runtime-architecture in agentlas-ai/Agentlas-OS) into .claude/skills/llm-runtime-architecture in your project. Claude Code loads it when a task matches its description.
How do I install LLM Runtime Architecture in Codex?
Run `npx skills add agentlas-ai/Agentlas-OS --skill llm-runtime-architecture -a codex`. Or copy the skill folder (skills/llm-runtime-architecture in agentlas-ai/Agentlas-OS) into .agents/skills/llm-runtime-architecture in your project. Codex loads it when a task matches its description.
Can I use LLM Runtime Architecture 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 agentlas-ai/Agentlas-OS --skill llm-runtime-architecture -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/llm-runtime-architecture, .gemini/skills/llm-runtime-architecture, .github/skills/llm-runtime-architecture and .opencode/skills/llm-runtime-architecture in your project.
What does LLM Runtime Architecture need to run?
SKILL.md names no scripts, command-line tools or credentials: LLM Runtime Architecture is instructions for the agent only.
Does LLM Runtime Architecture 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 LLM Runtime Architecture 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 LLM Runtime Architecture use?
LLM Runtime Architecture is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does LLM Runtime Architecture use?
About 197 tokens (SKILL.md is roughly 788 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 LLM Runtime Architecture?
Skills that share tags, products or a category with LLM Runtime Architecture: Using Agent Skills (addyosmani/agent-skills, 103k stars), Claude Reflect (BayramAnnakov/claude-reflect, 1.7k stars), Writing For Agents (bestofjs/bestofjs, 3.1k stars) and Neat-Freak Knowledge Closeout (KKKKhazix/khazix-skills, 21k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains LLM Runtime Architecture?
agentlas-ai (a GitHub organization) maintains it in agentlas-ai/Agentlas-OS, which has 1,575 GitHub stars. The repository holds 53 skills in this directory. The repository was last updated on October 6, 2026.
Source: agentlas-ai/Agentlas-OS on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.