End-to-End Task Executor

SkillProductivity

End-to-end autonomous task executor. Takes a task description and handles the full lifecycle: research, plan, implement, review with specialist skills, iterative CodeRabbit review, and merge via PR. Use when given a task to execute end-to-end.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the End-to-End Task Executor skill

What this skill tells your AI

The instructions your AI receives, as published by raphaeltm/simple-agent-manager in .agents/skills/do/SKILL.md and read by ahel’s review.

Read the full workflow from .claude/commands/do.md and execute it.

Quick Summary

  1. Research — understand the request, search the codebase, read related docs
    • If the user explicitly asks for local subagent critique before implementation, gather bounded local subagent reviews and reconcile them before editing.
  2. Task file — create in tasks/backlog/, commit to main
  3. Worktree — create feature branch and worktree
  4. Implement — follow checklist, push frequently, run quality checks. For UI changes: run mandatory Playwright visual audit with mock data on mobile + desktop viewports (see .claude/rules/17-ui-visual-testing.md)
  5. Validate — full quality suite: lint, typecheck, test, build
  6. Review — invoke local specialist skills / local subagents ($go-specialist, $cloudflare-specialist, etc.)
  7. Staging — check for existing staging deploys (wait 5min if active), trigger manual deployment via gh workflow run deploy-staging.yml --ref <branch>. Use $CF_TOKEN to query D1/KV/DNS directly (see .claude/rules/32-cf-api-debugging.md) to verify migrations, data state, and feature flags — this is faster and more precise than UI-based checks. Then verify changed behavior end-to-end via Playwright. For infrastructure changes (cloud-init, VM agent, DNS, TLS, scripts/deploy): MUST provision a real VM and verify heartbeat arrives.
  8. PR — create with gh pr create, wait for CI, then apply the coderabbit-review label with gh pr edit <pr-number> --add-label coderabbit-review once the PR is otherwise good to go. Treat CodeRabbit as merge-blocking: implement or review/close all CodeRabbit feedback, rely on incremental CodeRabbit reviews after pushed fixes, and repeat until the agent and CodeRabbit agree there is no unresolved feedback. If the user requested draft PR / do-not-merge, stop at the draft PR and do not merge.
  9. Cleanup — remove worktree, pull main

⚠️ Anti-Compaction: State File

Long /do runs lose context to compaction. You MUST maintain .do-state.md (gitignored) as external memory. Re-read it before every phase. See .claude/rules/14-do-workflow-persistence.md.

Signals

GitHub stars
58
Forks
8
Last commit
Sep 2026
Advanced
Catalog kind
skill
Gateway key
do-raphaeltm
Source
github.com/raphaeltm/simple-agent-manager