CtrlK
BlogDocsLog inGet started
Tessl Logo
SkillPublic

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.

Quality

77%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide
SecuritybySnyk

Low

Low-risk findings worth noting

Fix and improve this skill with Tessl

tessl review fix ./claude/plugins/agentlas-core-engine-meta-agent/runtime-support/skills/self-evolving-single-agent/SKILL.md

The canonical home for this skill is self-evolving-single-agent in agentlas-ai/Agentlas-OS

SKILL.md
Quality
Evals
Security

Self-Evolving Single Agent

Procedure

  1. Keep the package as one worker unless the user asks for a team.
  2. Run contracts/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.
  3. 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.
  4. If the task depends on current sources, add a research-refresh command, watchlist memory section, references, and optional scheduled workflow.
  5. 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.
  6. Make self-evolution proposal-first: draft patches or repair kits, then wait for human approval before changing tools, connectors, secrets, or core instructions.
  7. Add .agentlas/global-commands.json and one public global command for the worker across Claude Code, Codex, Antigravity, generic AGENTS.md, and terminal adapters.

Output

Return agent_package, skills, memory_contract, refresh_loop, approval_gate, global_commands, and verification.

File revision approval

Private memory curation is autonomous. Every instruction, skill, tool, or manifest change requires a real staged multi-file diff, current source evidence, and an exact one-use owner approval receipt. Never treat a summary, low-risk label, Curator verdict, shell flag, or legacy chip as approval. Project memory requires a separately curated agent_repo candidate before proposal preparation. Use revision-v1/evolution-v2 host capabilities; block activation on older hosts.

Repository

agentlas-ai/Agentlas-OS

Path

claude/plugins/agentlas-core-engine-meta-agent/runtime-support/skills/self-evolving-single-agent/SKILL.md

Last updated
First committed

Canonical home

agentlas-ai/Agentlas-OS
In sync

since Jul 28, 2026

Also appears in

agentlas-ai/Agentlas-OS
In sync

since Oct 13, 2026

agentlas-ai/Agentlas-OS
In sync

since Aug 20, 2026

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.