Arness Execute Plan

Execute a structured project plan by dispatching parallel batches of arn-code-task-executor agents and validating each completed task through a arn-code-task-reviewer agent before marking it done. Independent tasks run concurrently; dependent tasks wait for their blockers to clear. Each task goes through an execute-review-gate cycle: the executor implements and tests, the reviewer validates against patterns and acceptance criteria, and the orchestrator (this skill) decides whether to pass, retry, or escalate.

Key architectural constraint: Subagents cannot spawn other subagents. The dispatch loop MUST stay in the main session (this skill).

Pipeline position:

arn-code-init -> arn-code-feature-spec / arn-code-bug-spec -> arn-code-plan -> arn-code-save-plan -> arn-code-review-plan -> arn-code-taskify -> **arn-code-execute-plan** (dispatch -> execute -> review -> gate) -> arn-code-review-implementation -> arn-code-document-project -> arn-code-ship

Prerequisites

If no ## Arness section exists in the project's CLAUDE.md, inform the user: "Arness is not configured for this project yet. Run /arn-implementing to get started — it will set everything up automatically." Do not proceed without it.

Workflow

Step 1: Load Configuration

1. Read the project's CLAUDE.md and extract the ## Arness section to find:

   • Plans directory -- base path where project plans are saved
   • Code patterns -- path to the directory containing stored pattern documentation
   • Template path -- path to the report template set (JSON templates)
   • Template version -- plugin version the templates were copied from (if present)
   • Template updates -- user preference: ask, auto, or manual (if present)
   • Specs directory -- path to the directory containing specification files (if present)

   Template version check: If Template version and Template updates fields are present, run the template version check procedure documented in arn-code-save-plan Step 1 (Template Version Check) before proceeding. If ## Arness does not contain these fields, treat as legacy and skip.

2. Ask the user for PROJECT_NAME if not provided in the trigger message

3. Verify the project directory exists and contains the required structure:

   <plans-dir>/<PROJECT_NAME>/
   ├── INTRODUCTION.md
   ├── TASKS.md
   ├── PROGRESS_TRACKER.json
   ├── plans/PHASE_*.md
   └── reports/
   

4. If the project directory is missing, suggest running /arn-code-save-plan first

5. If INTRODUCTION.md or phase plans are missing, suggest running /arn-code-save-plan first

6. If PROGRESS_TRACKER.json is missing, warn that progress tracking will not be available. Execution can still proceed.

7. If ### Visual Testing is configured in CLAUDE.md, note the presence of visual testing. Per-task execution uses Layer 1 config only (top-level fields). Multi-layer visual validation is handled by /arn-code-review-implementation after plan execution completes.

8. Detect sketch artifacts -- Read <project-folder>/INTRODUCTION.md and check for a ### Sketch Artifacts section. If found, extract the sketch manifest path listed there (typically a relative path to sketch-manifest.json). Resolve it to an absolute path relative to the project directory. Also resolve the sketch directory path (the parent directory of the manifest). If no Sketch Artifacts section is found, check for manifests by scanning arness-sketches/ in the project's source directory for any sketch-manifest.json matching PROJECT_NAME. Store the resolved manifest path and sketch directory path for use in the dispatch loop (Step 4). If no manifest is found from either source, proceed normally -- the dispatch loop will not pass sketch context to executors.

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Step 2: Verify Task List and Build Dependency Graph

Deferred Task List Setup:

Check for Task list ID in ## Arness config. If absent:

Ask (using AskUserQuestion):

"Persistent task lists are not configured. Would you like to enable them? (Recommended — tasks survive across sessions)"

1. Yes (Recommended) — Auto-generate ID from project directory name
2. Yes, with custom ID — Specify the task list ID
3. No — Keep tasks session-scoped

If Yes: derive ID (slugify project directory name or use custom ID), write to .claude/settings.json (create if needed, preserve existing env vars), add Task list ID to ## Arness config. Inform: "Persistent task list configured."

If No: proceed without persistence. Inform (once): "Tasks will be session-scoped. Enable persistence anytime with /arn-code-taskify."

1. Call TaskList to check for existing tasks
2. If no tasks exist:
   • If Task list ID is configured in ## Arness: tasks should have persisted — suggest: "No tasks found despite persistent task lists being configured. The task list may have been cleared. Run /arn-code-taskify to recreate tasks."
   • If Task list ID is NOT configured: "No tasks found. Tasks may have been lost when the previous session ended (session-scoped storage). Run /arn-code-taskify to recreate them. To enable persistent task lists for future sessions, run /arn-code-taskify which will offer to configure persistence."
3. If tasks already exist with progress (some in_progress or completed):

   • Show the current state (how many pending, in_progress, completed)

   • Ask (using AskUserQuestion):

     "Tasks already exist with progress. How would you like to proceed?"

     Options:

     1. Resume -- Continue execution from where it left off, picking up the next pending unblocked task
     2. Restart -- Warn that existing reports will be preserved but task statuses will be reset to pending; confirm before proceeding
4. Present the dependency graph:
   • Total number of tasks
   • How many tasks can run in the first parallel batch (unblocked, pending)
   • Dependency chain overview -- which tasks block which, showing the critical path
5. Confirm with the user before starting execution

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Step 3: Choose Review Feedback Mode

Ask (using AskUserQuestion):

"How should Arness handle critical review findings?"

Options:

1. Resume executor with feedback (Recommended) -- Resumes the original executor agent with its full implementation context, passing the reviewer's specific findings as new instructions. This preserves all state from the initial implementation pass.
2. Dispatch fresh executor with review report -- Spawns a brand-new executor agent with the original task context plus the full review report. Use this if resuming fails or if you prefer a clean slate for fixes.

Store the chosen mode for the duration of this execution session. This choice applies to all tasks; the user is not asked again unless execution is restarted.

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Step 4: Dispatch Loop

The dispatch loop identifies batches of unblocked pending tasks, spawns parallel executor agents, then spawns parallel reviewer agents for each completed executor. Review verdicts determine whether a task is marked complete, retried with feedback, or escalated to the user. The loop repeats until all tasks are completed or blocked.

Read ${CLAUDE_PLUGIN_ROOT}/skills/arn-code-execute-plan/references/dispatch-loop.md for the full dispatch algorithm.

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Step 5: Report Completion

When all tasks are completed (or skipped/aborted by user), present a summary:

• Total tasks completed (out of total)
• First-pass tasks -- tasks that passed review on the first attempt
• Fix-cycle tasks -- tasks that needed one or more review-fix cycles (list which ones)
• Implementation reports generated (list file names)
• Testing reports generated (list file names)
• Review reports generated (list file names)
• Escalations -- any tasks that were escalated to the user during execution (what happened, what was decided)
• Skipped tasks -- any tasks the user chose to skip (if any)
• Progress tracker -- updated with final status in <plans-dir>/<PROJECT_NAME>/PROGRESS_TRACKER.json

If visual testing is configured, include in the completion summary:

• Visual testing: Layer 1 ran on [N] UI tasks. [If deferred layers exist:] Deferred layers: [names]. Run /arn-spark-visual-readiness to check activation status, then /arn-code-review-implementation for full multi-layer review.

Offer next steps:

• "Run /arn-code-simplify to review the implementation for reuse opportunities, quality issues, and efficiency improvements."
• "Run /arn-code-review-implementation to validate the full implementation against the plan and stored patterns."
• "Run /arn-code-document-project to generate developer documentation."
• "Run /arn-code-ship to commit and create a pull request."

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Step 5b: Pattern Refresh (auto)

After reporting completion, refresh stored pattern documentation to capture any new patterns introduced during implementation.

Read ${CLAUDE_PLUGIN_ROOT}/skills/arn-code-execute-plan/references/pattern-refresh.md and follow the pattern refresh procedure.

This step is automatic and non-blocking. If the refresh fails, execution is still considered successful.

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Error Handling

• ## Arness config missing in CLAUDE.md -- suggest running /arn-implementing to get started
• Project directory missing -- suggest running /arn-code-save-plan to create the project structure
• No tasks in TaskList -- suggest running /arn-code-taskify to convert TASKS.md into tasks
• Executor agent fails or crashes -- read the agent's output to identify what went wrong, report the failure details to the user, offer to retry the task (respawn executor with the same context)
• Executor reports 3-attempt test failure on the same test -- present the full failure details (test name, error output, files involved) to the user and ask whether to retry with more context, skip the failing task, or abort execution
• Reviewer agent fails or crashes -- report the failure to the user, offer two options: skip the review for this task (mark as completed without review) or retry the reviewer (respawn with the same context)
• Review cycle exceeds max retries (2) -- present the accumulated review findings to the user with full context, ask the user to choose: retry with more context (user provides additional guidance), skip the task and continue, or abort execution entirely
• All remaining tasks blocked (circular dependencies) -- this should have been caught by arn-code-review-plan; show the dependency graph (task IDs, names, and what each is blocked by) and ask the user to manually resolve by removing or reordering dependencies
• Resume fails (API error or agent ID no longer valid) -- fall back to fresh dispatch mode for this specific task, inform the user that resume was not possible and a fresh executor was dispatched instead