mcpbeat

Self Evolving Single Agent

agentlas-ai/agentlas-os-self-evolving-single-agent

Use when generating a single installable agent that should keep learning, track sources, refresh research, propose repairs, or improve itself over time without becoming a multi-agent team.

466 tokens
context cost
the whole folder, loaded on every use
1
files
instructions only
0
copies elsewhere
how many repositories repackaged it
1165
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/agentlas-ai/Agentlas-OS --skill self-evolving-single-agent

The instruction itself

3 sections, as written by the author

Self-Evolving Single Agent

Procedure

  • Keep the package as one worker unless the user asks for a team.
  • Run docs/builder-interview-research-gate.md before 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.json unless 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.json and 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.

How to use it

Copy the folder

Take agentlas-ai/agentlas-os-self-evolving-single-agent from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.