mode-classification

mode-classification is a skill for Claude Code, Codex from agentlas-ai/Agentlas-OS. It costs 35 tokens per session (684 once invoked), scanned A, original, Apache-2.0.

A decision process for choosing how to create or package an Agentlas agent from a request and its existing files.

In plain words
What is it for?
Routing requests to a single agent, a coordinated team, or a packaging workflow.
Why use it?
It prevents choosing the wrong setup by checking whether the work is new, based on existing material, or needs several independent roles working together.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/agentlas-ai/agentlas-os/mode-classification
Any agent
npx skills add agentlas-ai/Agentlas-OS --skill mode-classification
Clone the repo
git clone --depth 1 https://github.com/agentlas-ai/Agentlas-OS

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for mode-classification

README.md
[![agentmods](https://agentmods.dev/badge/skills/agentlas-ai/agentlas-os/mode-classification.svg)](https://agentmods.dev/skills/agentlas-ai/agentlas-os/mode-classification)
Your own site
<a href="https://agentmods.dev/skills/agentlas-ai/agentlas-os/mode-classification"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-os/mode-classification.svg" alt="Measured on agentmods" height="20"></a>
Per session 35 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 684 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00035 $0.00684
Opus 5 $0.00017 $0.00342
Sonnet 5 $0.00007 $0.00137
Haiku 4.5 $0.00003 $0.00068

Measured 4d ago against content hash 575f324bc0a7, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

mode-classification scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.agents/skills/mode-classification/SKILL.md · 60 lines

How it starts

The opening of the file, as written. The whole thing — 60 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Mode Classification

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

Procedure

  1. Inspect the user request and any provided path, repo, ZIP, prompt, or agent files.
  2. Step 0 - existing material wins: if existing material is being converted, repaired, cleaned, imported, or released, choose agentlas-packager.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. If the choice changes the output and the request is ambiguous, run the clarify question loop instead of guessing.

Read the full file on GitHub · 60 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 4d ago First seen · 60 lines · 35 tokens per session scan A 575f324bc0a7

Subscribe to this mod's changes

mode-classification is a skill published in the GitHub repository agentlas-ai/Agentlas-OS (1,099 stars, last pushed 2d ago), licensed Apache-2.0. It adds 35 tokens to every session and 684 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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