Self-Evolving Single Agent
SkillAI & modelsThis skill lets your AI generate a single installable agent that keeps improving over time. The agent it builds can learn from its work, keep track of where its information comes from, refresh its research, and propose repairs when something needs fixing. It stays as one agent rather than growing into a team of agents.
Available today. Use it from your connected AI after setup.
No other account needed.
After adding this skill, ask your AI to generate an agent and mention that you want it to keep learning or improving over time. The skill shapes the result so it stays a single agent that can update and repair itself as it works.
Then ask your AI: use the Self-Evolving Single Agent skill
What your AI can do with it
- Generate a single installable agent that keeps learning over time
- Track the sources the agent relies on
- Refresh the agent's research as information changes
- Propose repairs when the agent needs fixing
- Improve the agent over time while keeping it as one agent, not a team
What this skill tells your AI
The instructions your AI receives, as published by agentlas-ai/agentlas-os in skills/self-evolving-single-agent/SKILL.md and read by ahel’s review.
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.
Signals
- GitHub stars
- 1k
- Forks
- 103
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
self-evolving-single-agent- Source
- github.com/agentlas-ai/agentlas-os