simple-run

name	simple-run
description	Orchestrate full feature implementation: loop through a feature's issues.json, invoking simple-implement for each task until all are done or a blocker is hit. Use when the user wants to auto-complete a feature, run through all tasks, or execute the task queue without manual intervention. Triggers: "run all tasks", "auto-implement", "complete the feature", "implement everything", "execute the task list".
disable-model-invocation	true

Simple Run — Orchestrate Feature Implementation

You are an orchestrator. Your job is to drive a feature from tasks_ready to done by repeatedly invoking simple-implement in a sequential loop — one task at a time — until the work queue is empty or a stopping condition is met.

You do NOT implement code yourself. You delegate each task to a sub-agent that follows the simple-implement skill, then inspect the result and decide whether to continue.

Folder convention

Same as all simple-* skills:

docs/
  index.json              ← feature manifest
  visual.md               ← app-level visual design (optional)
  <feature-name>/
    spec.md
    spec/                 ← optional detail files (if spec.md was split)
    design.md
    design/               ← optional detail files (if design.md was split)
    issues.json           ← work queue
    progress-log.md       ← updated by each sub-agent

Workflow

1. Initialize

• Require a clean git worktree before changing state. If uncommitted changes exist, stop and ask the user how to handle them.
• Identify the feature to work on. If the user specifies a feature name, use it. Otherwise, check docs/index.json for features with status "tasks_ready" or "in_progress". If multiple features qualify, ask the user which one to run.
• Read docs/<feature-name>/issues.json to understand the full scope.
• Count total tasks, completed tasks, and remaining tasks. Report this to the user as a starting summary (e.g., "Feature auth has 12 tasks: 4 done, 8 remaining.").
• Update docs/index.json to set the feature status to "in_progress" if not already.
• Capture a test baseline. Run the full test suite and record pass/fail results. This lets review sub-agents distinguish pre-existing failures from new regressions. Refresh the baseline after each successful commit.

2. Loop

For each iteration:

a) Check for the next available task:

• Read issues.json (fresh each iteration — the sub-agent may have added tasks or changed priorities and dependencies).
• Apply the same task-selection algorithm as simple-implement:
  1. Filter to status: "todo".
  2. Exclude tasks with unmet dependencies (any depends_on task that is not "done").
  3. Pick the lowest priority number. Break ties by lowest ID.
• If unfinished tasks remain but none is available, stop and report the blocked, missing, or cyclic dependency state.

b) If a task is available, spawn a sub-agent:

• Launch a sub-agent — a fresh, stateless session with no memory of prior iterations — with access to the full project codebase and the docs folder.
• Instruct the sub-agent to follow the simple-implement skill for the specific feature and task ID.
• The sub-agent prompt should include:
  • The feature name and task ID.
  • The path to the docs folder.
  • An instruction to follow simple-implement's workflow (orient → pick → understand → implement → verify → update state → summarize).
  • The flag [INVOCATION_MODE: automated] — this is the contract signal that tells simple-implement it is running in automated mode and should not pause for user confirmation under any circumstances.

c) After the sub-agent completes:

• Read the updated issues.json to confirm the task status changed.
• Read the latest entry in progress-log.md to understand what happened.
• If the task is "blocked", log the blocker and decide:
  • If other non-blocked tasks remain, skip to the next one.
  • If all remaining tasks are blocked, stop and report to the user.
• If the task is "done", proceed to the review step (d).
• If the task is neither "done" nor "blocked", stop and report the invalid sub-agent result.

d) Review and commit:

Spawn a review sub-agent with the test baseline (from step 1) and the git diff. The reviewer evaluates:

• Correctness — bugs, edge cases, side effects, collateral impact on related code.
• Tests — coverage added/updated, suite passes locally. Compare failures against baseline to separate regressions from pre-existing issues. Re-run any non-baseline failure once before counting it (handles flaky tests).
• Architecture hygiene — adherence to project conventions (AGENTS.md, CLAUDE.md). No violations of KISS/YAGNI/SOLID/DRY (unnecessary abstractions, duplicated logic, god-objects, etc.). File/folder organization consistent with existing structure.
• Scope — flag unexpected file changes outside the task's expected footprint.

Reviewers report reproducible correctness, security, test, scope, or architecture findings — not preferences, speculative improvements, or unrelated cleanup. Triage findings as:

• Defects: Fix findings that affect the current task or feature in the current review cycle. This includes pre-existing findings that must be corrected for the current task or feature to work correctly. Send all defects to one fix sub-agent, then re-review. Allow 2 fix-agent attempts after the initial review; if a defect remains, abandon the task.
• Execution decisions: The orchestrator resolves scoped, reversible choices using the spec, existing conventions, and the simplest safe option. Include the decision in the fix instructions when code changes are needed.
• Principal decisions: Surface choices that are cross-cutting, long-lasting, expensive to reverse, or materially affect permissions, data, public behavior, business rules, or multiple user journeys. Block the task only when it cannot safely proceed; otherwise choose a conservative, reversible default and retain the decision for the final report.
• Pre-existing or unrelated findings: Stop and escalate critical issues such as security or data-loss risks. Retain other material findings for the final report; ignore minor observations.

Review findings do not create tasks in issues.json. If review requires abandoning a task, restore only that task's changes to the last committed state before marking it "blocked" and logging the outcome. Once no defects or blocking decisions remain, stage only the reviewed task and state files, inspect the staged diff, then commit referencing the task ID (e.g., feat(auth): TASK-auth-003 — add session refresh logic). Refresh the test baseline and continue.

e) Report progress:

• After each completed task, briefly log progress (e.g., "✓ TASK-auth-003 done. 6/12 complete.").

3. Stopping conditions

Stop the loop when any of these conditions is met:

1. All tasks are "done". The feature is complete.
2. All remaining tasks are "blocked". Human intervention is needed.
3. A sub-agent fails catastrophically (crashes, produces no output, or leaves the codebase in a broken state). Stop and report.
4. A critical out-of-scope issue is discovered. Stop and escalate it to the user.
5. The queue is invalid. No task is selectable because dependencies are missing or cyclic, or a sub-agent returns an invalid task status.
6. The user intervenes. If running interactively, the user can stop the loop at any time.

4. Finalize

When the loop ends:

• Read the final state of issues.json and progress-log.md.
• Update docs/index.json:
  • If all tasks are done, set feature status to "done".
  • Otherwise, leave feature status as "in_progress".
• Provide a final summary to the user:
  • Total tasks completed in this run.
  • Any tasks that were blocked and why.
  • Any new tasks that were discovered during implementation.
  • Deduplicated principal decisions needing attention, including the default taken and impact of changing it.
  • Material pre-existing or unrelated findings that warrant engineering follow-up.
  • Overall feature status.
• If the feature is now complete and a simple-simplify skill is available, remind the user they can run it to audit the completed implementation for unnecessary complexity. This is a manual, report-only follow-up — never invoke it automatically.
• If the feature is now complete and a simple-distill skill is available, remind the user they can run it to promote the feature's durable decisions and behavior into app-level docs and archive the feature folder. This is a reminder only — never invoke it automatically.

Important notes

• Strictly sequential. Run one sub-agent at a time — each needs to see the file and state changes from the previous one. Parallelism would require a locking/merge strategy for issues.json and progress-log.md; it's a future extension.

• You are the orchestrator, not the implementer. Don't write production code. You may inspect repository state, run orchestration-level verification, manage reviewed commits, and update docs/index.json; all implementation happens inside sub-agents.

• File size guard. After each completed task, check whether issues.json has ≥ 15 done tasks or progress-log.md exceeds 300 lines. If either threshold is hit, notify the user and suggest running /simple-cleanup before continuing. Don't hard-stop the loop — the user decides whether to clean up now or later.