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

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raphaeltm/simple-agent-manager68 stars(GitHub 68 + cccskills 0)

Run score Not tested

See how to install

Files included (2)

  • SKILL.md3.1 KB
  • agents/openai.yaml233 B

SKILL.md (original)

Review the instructions the agent will receive before you install.

End-to-End Task Executor

Read the full workflow from .claude/commands/do.md and execute it. Use .claude/rules/00-rule-routing.md to select only the rules needed for the changed paths before reading scoped .claude/rules/ files.

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 request CodeRabbit through the trusted GitHub Actions path: apply the coderabbit-review label with gh pr edit <pr-number> --add-label coderabbit-review; if the label run did not fire (it never fires on a draft PR), run gh workflow run coderabbit-bot-review.yml --ref main -f pr_number=<pr-number>. Do not post @coderabbitai review directly with an agent token. Then wait about 15 minutes. A CodeRabbit review is not required, but one that arrives blocks merge: implement or resolve every finding with a reason, and give incremental reviews after pushed fixes the same wait. If no review arrives (silence, Review skipped, rate limit), record that in the PR's CodeRabbit Review Evidence and continue. Do not re-trigger in a loop, and never add needs-human-review or wait for a waiver because CodeRabbit is silent (see .claude/rules/25-review-merge-gate.md). 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 (local-only, snapshot-visible; never commit) as external memory. Re-read it before every phase. See .claude/rules/14-do-workflow-persistence.md.

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