agentsclimarketplace

Self evolving single agent

Skill agentlas-ai/Agentlas-OS/skills/self-evolving-single-agent

Agent OS: keep specialist agents in a hub, spin up a temporary orchestrator per task. Local-first, works with any model.

Install
npx -y skills add agentlas-ai/Agentlas-OS --skill self-evolving-single-agent

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

What its author says it does

Copied from the file, not written here

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.

SKILL.md

1.8 KB, as published. Nobody here has run it

Self-Evolving Single Agent

Procedure

  1. Keep the package as one worker unless the user asks for a team.
  2. 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.
  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, Gemini CLI, generic AGENTS.md, and terminal adapters.

Output

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

Gives 0 of the 12 instructions most context ai engineering skills give

Counted across 1,193 of the 1,976 authors here whose files we hold, read 2026-08-06

  • dispatch a fresh implementer subagent per taskin 48 of 1193, across 19 files
  • dispatch final reviewer after all tasksin 37 of 1193, across 11 files
  • provide full task text to the subagentin 31 of 1193, across 10 files
  • review spec compliance before code qualityin 27 of 1193, across 10 files
  • make the hook script executablein 26 of 1193, across 8 files
  • re-snapshot after navigation or DOM changesin 25 of 1193, across 17 files
  • answer subagent questions before proceedingin 22 of 1193, across 7 files
  • mark task complete in TodoWrite after approvalin 22 of 1193, across 6 files
  • merge hook into existing settingsin 21 of 1193, across 3 files
  • read files before editing themin 21 of 1193, across 9 files
  • ask if installation is global or projectin 20 of 1193, across 2 files
  • copy the hook script to target locationin 20 of 1193, across 2 files

Said here and by no other author read

  • Research official sources and comparables before changing behavior
  • keep the package as one worker
  • run the research gate before generation
  • ask an 8 to 12 question first batch
  • write the domain expert synthesis
  • add a memory architecture

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once.

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.