Self Evolving Single Agent Agent Skill

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

files

instructions only

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.

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This skill should be used when designing autonomous agent harnesses: research loops, evaluation scaffolds, locked and editable surfaces, durable logs, novelty gates, pruning, rollback, PR preparation, and human approval boundaries.

3k tokens

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.