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AbdullahMalik17AbdullahMalik17/malikclaw/skills/autonomous-dev-team/SKILL.md

autonomous-dev-team

Skill16 starsChanged 8 months agoMITView on GitHub

Multi-agent team execution with DAG task scheduling, specialized roles, and consensus synthesis.

We split this skill into the rules it gives an agent

2 rules. About 0 are common, 0 are distinctive, and 2 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈0

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 2

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Use this skill when decomposing complex software goals into parallel, role-based workflows using MalikClaw's supervisor DAG engine.

    line 11
  2. Very specific:

    Call team_run with the high-level goal:

    line 23

≈ “Common” is an estimate: at least 1 in 5 of the 308,232 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

🤖 Autonomous Multi-Agent Team Skill

Use this skill when decomposing complex software goals into parallel, role-based workflows using MalikClaw's supervisor DAG engine.

Roles & Capabilities

  • Architect (architect): Decomposes high-level goals into modular DAG tasks (DependsOn).
  • Researcher (researcher): Gathers specs, API docs, web content, and context.
  • Engineer (engineer): Implements code, refactors functions, and writes unit tests.
  • QA & Auditor (qa): Runs lints, performs security checks, and audits edge cases.
  • Communicator (communicator): Formats technical summaries and user release notes.

Execution Flow

  1. Call team_run with the high-level goal:
{
  "goal": "Build a secure OAuth 2.0 PKCE authentication module in Go"
}
  1. The orchestrator generates subtasks with explicit dependencies and executes ready tasks concurrently.
  2. Outputs are aggregated into a unified multi-agent consensus report.

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