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.
npx agentmods add skills/agentlas-ai/agentlas-os/self-evolving-single-agentnpx skills add agentlas-ai/Agentlas-OS --skill self-evolving-single-agentgit 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/self-evolving-single-agent)<a href="https://agentmods.dev/skills/agentlas-ai/agentlas-os/self-evolving-single-agent"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-os/self-evolving-single-agent.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 | $0.00040 | $0.00411 |
| Opus 5 | $0.00020 | $0.00205 |
| Sonnet 5 | $0.00008 | $0.00082 |
| Haiku 4.5 | $0.00004 | $0.00041 |
Grade A, and why
self-evolving-single-agent 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 6d 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.
What it actually says
Self-Evolving Single Agent
Procedure
- Keep the package as one worker unless the user asks for a team.
- Run
contracts/builder-interview-research-gate.mdbefore generation: ask an 8-12 question first batch, research official sources, similar agent repositories or comparables, academic/professional theory, and plugin docs, compare tool/plugin choices, and write the domain-expert synthesis plus prompt-performance contract before creating the worker prompt. - Add memory architecture even for the single worker:
.agentlas/memory-map.json;.agentlas/vault-references.json;- project memory owned by PM Soul/project owner;
- Memory Events and Memory Tickets for durable updates.
- If the task depends on current sources, add a research-refresh command, watchlist memory section, references, and optional scheduled workflow.
- Add
docs/builder-interview.md,docs/research-sources.md,docs/tool-selection.md,docs/domain-expert-synthesis.md,docs/prompt-performance-contract.md, and.agentlas/capability-eval-plan.jsonunless explicitly creating a minimal private scaffold. - Make self-evolution proposal-first: draft patches or repair kits, then wait for human approval before changing tools, connectors, secrets, or core instructions.
- Add
.agentlas/global-commands.jsonand one public global command for the worker across Claude Code, Codex, Gemini CLI, generic AGENTS.md, and terminal adapters.
Output
Return agent_package, skills, memory_contract, refresh_loop,
approval_gate, global_commands, and verification.
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.
- 6d ago First seen · 39 lines · 40 tokens per session scan A 700e84029b0e
self-evolving-single-agent is a skill published in the GitHub repository agentlas-ai/Agentlas-OS (1,103 stars, last pushed today), licensed Apache-2.0. It adds 40 tokens to every session and 411 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.
Other skills, from other repositories
agentfield-use
Whenever you have a discrete task to perform — one the user delegated, or one that arose inside your own work — check FIRST whether an installed AgentField agent covers it, and offload to it by default when one does. Coverage, not task size, is the test: even a small job goes to a covering agent. The check is cheap …
agent-optimization
Improve an Agent State through versioned scores and score-linked Traces from a frozen Benchmark.
quality-loop
Use this workflow recipe when a draft, plan, proposal, or other deliverable should be independently reviewed and revised until it satisfies explicit quality criteria.
cli-skill-design
Design a co CLI surface and its SKILL.md together so an agent can drive it without guessing — every command ends by naming the next one, --help lists everything, and every failure says what to run instead. Use when adding a new CLI command group, writing or rewriting a SKILL.md for one, or auditing an existing one.
plan_route
Plan a route and return distance + ETA (schema + deterministic result).
update-setup
One-time setup wizard for the memmy upgrade skill. Triggers: setup update, configure update, 切设置更新, 初始化更新.