Agent skill

Mode Classification

by agentlas-ai in agentlas-ai/Agentlas-OS

Use before routing a /meta-agent request to choose single-agent-creator, team-builder, or agentlas-packager from the user's wording and available files.

Apache-2.0Auto-check passedAI & LLM Engineering

Install Mode Classification

skills CLI

$ npx skills add agentlas-ai/Agentlas-OS --skill mode-classification -a claude-code

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

GitHub CLI

$ gh skill install agentlas-ai/Agentlas-OS mode-classification --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/agentlas-ai/Agentlas-OS.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/mode-classification .claude/skills/mode-classification && 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/

Paste into Claude Code

Install the "mode-classification" agent skill from https://github.com/agentlas-ai/Agentlas-OS/tree/main/skills/mode-classification into .claude/skills/mode-classification/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mode-classification", 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

$skill-installer install https://github.com/agentlas-ai/Agentlas-OS/tree/main/skills/mode-classification

Type 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.

skills CLI

$ npx skills add agentlas-ai/Agentlas-OS --skill mode-classification -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install agentlas-ai/Agentlas-OS mode-classification --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/agentlas-ai/Agentlas-OS.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/mode-classification .agents/skills/mode-classification && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "mode-classification" agent skill from https://github.com/agentlas-ai/Agentlas-OS/tree/main/skills/mode-classification into .agents/skills/mode-classification/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mode-classification", 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.

skills CLI

$ npx skills add agentlas-ai/Agentlas-OS --skill mode-classification -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install agentlas-ai/Agentlas-OS mode-classification --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/agentlas-ai/Agentlas-OS.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/mode-classification .cursor/skills/mode-classification && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "mode-classification" agent skill from https://github.com/agentlas-ai/Agentlas-OS/tree/main/skills/mode-classification into .cursor/skills/mode-classification/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mode-classification", 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

$ gemini skills install https://github.com/agentlas-ai/Agentlas-OS.git --path skills/mode-classification

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add agentlas-ai/Agentlas-OS --skill mode-classification -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install agentlas-ai/Agentlas-OS mode-classification --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/agentlas-ai/Agentlas-OS.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/mode-classification .gemini/skills/mode-classification && rm -rf skills-src

Use ~/.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/

Paste into Gemini CLI

Install the "mode-classification" agent skill from https://github.com/agentlas-ai/Agentlas-OS/tree/main/skills/mode-classification into .gemini/skills/mode-classification/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mode-classification", 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.

GitHub CLI

$ gh skill install agentlas-ai/Agentlas-OS mode-classification

Installs 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).

skills CLI

$ npx skills add agentlas-ai/Agentlas-OS --skill mode-classification -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/agentlas-ai/Agentlas-OS.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/mode-classification .github/skills/mode-classification && rm -rf skills-src

Use ~/.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/

Paste into GitHub Copilot

Install the "mode-classification" agent skill from https://github.com/agentlas-ai/Agentlas-OS/tree/main/skills/mode-classification into .github/skills/mode-classification/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mode-classification", 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.

skills CLI

$ npx skills add agentlas-ai/Agentlas-OS --skill mode-classification -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install agentlas-ai/Agentlas-OS mode-classification --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/agentlas-ai/Agentlas-OS.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/mode-classification .opencode/skills/mode-classification && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "mode-classification" agent skill from https://github.com/agentlas-ai/Agentlas-OS/tree/main/skills/mode-classification into .opencode/skills/mode-classification/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mode-classification", 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: mode-classification
GitHub stars: 1.6k
Token cost: ~745 tokens
SKILL.md length: 359 words
Files: 1
Skills in repo: 53
Repo updated: Oct 6, 2026
First seen: Oct 7, 2026
Licence: Apache-2.0

At a glance

Use before routing a /meta-agent request to choose single-agent-creator, team-builder, or agentlas-packager from the user's wording and available files.

Works in 9 steps: Inspect the user request and any… → Step 0 - existing material wins: if… → Step 1 - count independent ownership… → …
Tasks that involve Building AI agents
SKILL.md covers Procedure, Return and Reference
Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Mode Classification is an agent skill from agentlas-ai/Agentlas-OS. Use before routing a /meta-agent request to choose single-agent-creator, team-builder, or agentlas-packager from the user's wording and available files.

Its SKILL.md is about 750 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 Building AI agents. 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

Tasks that involve Building AI agents

Example prompts

“/mode-classification”

Workflow steps

9 steps, taken from the first numbered list in SKILL.md.

Inspect the user request and any provided path, repo, ZIP, prompt, or agent
Step 0 - existing material wins: if existing material is being converted,
Step 1 - count independent ownership boundaries. Ask how many roles must
Step 2 - check synthesis need for multi-boundary candidates. If those role
Step 3 - shape guard. single-agent-creator may have many skills/tools but
Use keyword signals only as hints after the ownership-boundary check
Overlay check: if the request depends on knowledge search over user
Loop policy: derive loop_policy from task purpose and risk using
If the choice changes the output and the request is ambiguous, run the

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

Mode Classification loads about 745 tokens when it runs. Until then it costs about 43 tokens; SKILL.md has 359 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~43
When it runs · the whole SKILL.md, loaded when a task matches: ~745

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). 359 words, ~745 tokens.

Download SKILL.mdSave it as .claude/skills/mode-classification/SKILL.md (or your agent's skills folder).

name: mode-classification
description: Use before routing a /meta-agent request to choose single-agent-creator, team-builder, or agentlas-packager from the user's wording and available files.

Mode Classification

Pick one Agentlas meta-agent mode before generating or repairing files.

Procedure

Inspect the user request and any provided path, repo, ZIP, prompt, or agent files.
Step 0 - existing material wins: if existing material is being converted, repaired, cleaned, imported, or released, choose agentlas-packager.
Step 1 - count independent ownership boundaries. Ask how many roles must independently own all three of:
- their own memory/context;
- their own tools/permissions;
- their own success criteria. One boundary means single-agent-creator. Two or more boundaries means a team-builder candidate. If the boundary count is unclear, run the clarify question loop before generating; do not infer from the word "team" alone.
Step 2 - check synthesis need for multi-boundary candidates. If those role outputs must be routed, reviewed, synthesized, or chained through produces/consumes dependencies, choose team-builder and require an orchestrator/HQ plus memory, policy, eval, and QA. If the roles are unrelated, create separate single-agent packages instead of one team.
Step 3 - shape guard. single-agent-creator may have many skills/tools but must not emit multiple loose worker agent.md files. team-builder may be small, but it must not omit the orchestrator/HQ.
Use keyword signals only as hints after the ownership-boundary check:
- MULTI hints: separate memory partitions, tools or permissions that must not be merged, role-to-role review/policy separation, and produces/consumes pipelines.
- SINGLE hints: one coherent job, many tools/skills owned by one worker, no routing or final synthesis requirement.
Overlay check: if the request depends on knowledge search over user documents, evidence-based or citation-attached generation, or a document corpus (HWPX/docx/pdf/제안서/계약서/견적서), additionally apply the ontology-backed-agent overlay (modes/ontology-backed-agent.md) with ontology_backed: true on the chosen base mode.
Loop policy: derive loop_policy from task purpose and risk using .agentlas/contract-injection-map.json risk tiers — none for simple one-shot tasks, self-correct for complex or long-running work, verified (separate-context verifier + side-effect gate) when the agent performs external writes or sends. Do not force loops onto simple tasks.
If the choice changes the output and the request is ambiguous, run the clarify question loop instead of guessing.

Show full SKILL.md (26 more words)Show less

Return

Return the selected mode, whether the ontology-backed-agent overlay applies, the derived loop_policy, and one short reason. Then route to the matching builder.

Reference

See docs/mode-classifier.md.

Files

Just SKILL.md in skills/mode-classification of agentlas-ai/Agentlas-OS.

Open the folder on GitHubat commit cfdebf8

Compare with similar skills

Mode Classification 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.

Mode Classification compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Mode Classification this skillagentlas-ai/Agentlas-OS	1.6k	—	~745	Automated safety check: Pass	Apache-2.0	yesterday
Agent BuildershareAI-lab/learn-claude-code	78k	6 repos	~1.2k	Automated safety check: Pass	MIT	9 days ago
Azure AI Projects Python SDKmicrosoft/skills	3.1k	6 repos	~2.8k	Automated safety check: Pass	MIT	yesterday
Paperclip Create Agentpaperclipai/paperclip	99k	1 repos	~2.1k	Automated safety check: Pass	MIT	today
Senior Prompt Engineermaslennikov-ig/claude-code-orchestrator-kit	260	4 repos	~1.4k	Automated safety check: Pass	Custom licence	7 mo ago
Create Agentgnekt/My-Brain-Is-Full-Crew	3.9k	—	~3.1k	Automated safety check: Pass	Custom licence	3 mo ago

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Hephaestus Upload
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Routing Card Authoring
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Team Builder Packaging
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Questions about Mode Classification

What does Mode Classification do?

Use before routing a /meta-agent request to choose single-agent-creator, team-builder, or agentlas-packager from the user's wording and available files. Mode Classification is an agent skill from agentlas-ai/Agentlas-OS. Use before routing a /meta-agent request to choose single-agent-creator, team-builder, or agentlas-packager from the user's wording and available files.

When should I use Mode Classification?

Mode Classification fits situations like: tasks that involve Building AI agents.

How do I install Mode Classification in Claude Code?

Run `npx skills add agentlas-ai/Agentlas-OS --skill mode-classification -a claude-code`. Or copy the skill folder (skills/mode-classification in agentlas-ai/Agentlas-OS) into .claude/skills/mode-classification in your project. Claude Code loads it when a task matches its description.

How do I install Mode Classification in Codex?

Run `npx skills add agentlas-ai/Agentlas-OS --skill mode-classification -a codex`. Or copy the skill folder (skills/mode-classification in agentlas-ai/Agentlas-OS) into .agents/skills/mode-classification in your project. Codex loads it when a task matches its description.

Can I use Mode Classification 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 mode-classification -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/mode-classification, .gemini/skills/mode-classification, .github/skills/mode-classification and .opencode/skills/mode-classification in your project.

What does Mode Classification need to run?

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

Does Mode Classification 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 Mode Classification 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 Mode Classification use?

Mode Classification 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 Mode Classification use?

About 745 tokens (SKILL.md is roughly 3k 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 Mode Classification?

Skills that share tags, products or a category with Mode Classification: Agent Builder (shareAI-lab/learn-claude-code, 78k stars), Azure AI Projects Python SDK (microsoft/skills, 3.1k stars), Paperclip Create Agent (paperclipai/paperclip, 99k 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 Mode Classification?

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.

Install Mode Classification

Facts

At a glance

What it does

When your agent uses it

Example prompts

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Mode Classification

Procedure

Return

Reference

Files

Compare with similar skills

Similar skills

More from agentlas-ai/Agentlas-OS

Related

Questions about Mode Classification