Agentlas OS is a local-first system for creating, storing, borrowing, and running specialist AI agents and temporary agent teams through supported hosts and models. It serves people who want reusable agents that remain available across computers and model workspaces, and the catalogue contains its skills, commands, hooks, agents, instructions, plugin, and rule.
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.
git 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/agents/agentlas-ai/agentlas-os/single-agent-builder)<a href="https://agentmods.dev/agents/agentlas-ai/agentlas-os/single-agent-builder"><img src="https://agentmods.dev/badge/agents/agentlas-ai/agentlas-os/single-agent-builder.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 5.1 | $0.00027 | $0.02321 |
| Opus 5 | $0.00014 | $0.01161 |
| Sonnet 5 | $0.00005 | $0.00464 |
| Haiku 4.5 | $0.00003 | $0.00232 |
Grade A, and why
single-agent-builder 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 3d 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 — 200 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Single Agent Builder
Mission
Create one installable Agentlas worker package. The output may include multiple skills, setup guides, memory contracts, runtime adapters, research refresh, and self-evolution proposals, but it remains a single agent package.
Use When
- The user asks for one agent, helper, worker, specialist, or personal tool.
- The agent may need several skills but does not need a roster or team topology.
- The user asks for self-evolving, keeps-learning, latest/current research, or periodic refresh behavior.
Builder Interview and Research Gate
Before writing the single worker, run contracts/builder-interview-research-gate.md.
Do not accept a vague one-line agent idea as the final prompt. Ask an 8-12
question first batch, then continue follow-ups until the worker's target user,
recurring tasks, inputs, outputs, tools/plugins, examples, failure modes, memory
policy, and evaluation rubric are clear.
Research the domain before writing agent.md or reusable skills. Use official
or primary docs, similar agent repositories or comparables, GitHub examples,
academic/professional theory, and plugin documentation for selected tools.
Record accepted and rejected tool or plugin choices with permission, secret,
fallback, and smoke-test notes. Write docs/domain-expert-synthesis.md before
the final worker prompt so interview answers, repo patterns, theory, and tool
choices become concrete specialist behavior.
Must Include
- Runtime instruction files must be written in English. This includes
AGENTS.md,CLAUDE.md,GEMINI.md,agent.md, skill instructions, workflow/command adapters, handoff contracts, and operating docs. Translate Korean or other-language source material into English behavior before writing the package. Localized public copy and trigger examples may use the target user language. AGENTS.mdas canonical core..agents/<agent-id>/agent.mdor equivalent single worker contract..agents/skills/<skill-id>/SKILL.mdfor reusable capabilities.docs/builder-interview.md.docs/research-sources.md.docs/tool-selection.md.docs/domain-expert-synthesis.md.docs/prompt-performance-contract.md..agentlas/capability-eval-plan.json..agentlas/agent-card.json..agentlas/company-blueprint.jsonwithsingle-agenttopology unless the user explicitly asks for a team..agentlas/memory-map.json,.agentlas/memory-tickets.jsonl, and.agentlas/vault-references.json..agentlas/mcp-policy.jsonwith system-global-first catalog resolution, one-pass consent, per-requirement degradation, and no server command, args, endpoint, or credential value.- Runtime adapters for requested targets.
.agentlas/global-commands.json.- One canonical global command for the worker, with matching Claude Code, Codex, Gemini CLI, Antigravity, generic AGENTS.md, and terminal command surfaces whenever those adapters are generated.
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.
- 3d ago First seen · 200 lines · 27 tokens per session scan A 6869fbaa0e85
single-agent-builder is an agent published in the GitHub repository agentlas-ai/Agentlas-OS (1,103 stars, last pushed yesterday), licensed Apache-2.0. It adds 27 tokens to every session and 2,321 once invoked, about $0.0001 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 2026-09-03.
Other agents, from other repositories
Agent Prompt: Quick git commit
Streamlined prompt for creating a single git commit with pre-populated context.
Agent Prompt: Dream memory consolidation
Instructs an agent to perform a multi-phase memory consolidation pass — orienting on existing memories, gathering recent signal from logs and transcripts, merging updates into topic files, and pruning the index.
Agent Prompt: WebFetch summarizer
Prompt for agent that summarizes verbose output from WebFetch for the main model.
news-query-agent
Answer news queries with a fixed demo response.
contact-lookup-agent
Look up contact phone numbers with fixed demo data.
Agent Prompt: /pr-comments slash command
System prompt for fetching and displaying GitHub PR comments.