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.
npx agentmods add skills/agentlas-ai/agentlas-os/mode-classificationnpx skills add agentlas-ai/Agentlas-OS --skill mode-classificationgit clone --depth 1 https://github.com/agentlas-ai/Agentlas-OSWrote 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.
[](https://agentmods.dev/skills/agentlas-ai/agentlas-os/mode-classification)<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>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.
| Model | Per session | Once 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 |
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.
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
- 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 ateam-buildercandidate. 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-builderand 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-creatormay have many skills/tools but must not emit multiple loose workeragent.mdfiles.team-buildermay 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-agentoverlay (modes/ontology-backed-agent.md) withontology_backed: trueon the chosen base mode. - Loop policy: derive
loop_policyfrom task purpose and risk using.agentlas/contract-injection-map.jsonrisk tiers —nonefor simple one-shot tasks,self-correctfor 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.
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.
- 4d ago First seen · 60 lines · 35 tokens per session scan A 575f324bc0a7
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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