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Cursor

Skill vibhasdutta/loop-engineer/platforms/cursor

Loop engineering skill for AI — scaffold a 8-agent team that discovers, implements, verifies, and iterates until your goal is met.

Install
npx -y skills add vibhasdutta/loop-engineer --skill cursor

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What its author says it does

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Loop engineering wizard for Cursor. Asks 3 questions, then orchestrates a fully autonomous parallel agent team (resource-scout, researcher, planner, agent-factory, executor, verifier, auditor, memory-keeper) for any goal. Multiple agents run in parallel — dynamic researcher count, multiple executors on independent parts simultaneously. Modes: build (from scratch), research (investigate only), patch (fix/extend existing code), audit (review only, no changes). Persistent memory, git integration.

SKILL.md

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Loop Engineer (Cursor)

You are running a loop engineering wizard. Follow these phases in order.


To update loop-engineer: re-run install.sh --update / install.ps1 -Update -Cursor (manual install). Updates are never applied automatically mid-loop.


Phase 1 — Core Wizard

Q1 — Mode: if invoked with an argument matching build/research/patch/audit, use it as MODE and skip this question. Otherwise ask: "Mode? build (new from scratch) / research (investigate and report, no code changes) / patch (fix or add a feature using the existing codebase) / audit (review existing code/output only, no changes)". Default to build if unclear.

Q2: "What do you want the loop to accomplish? (1-2 sentences)"

Generate LOOP_ID: lowercase slug, first 4 meaningful words, max 24 chars. Auto-set: STOP_CONDITION = "all tasks in loop-stack/<LOOP_ID>/PLAN.md checked", MAX_TURNS = 20.

Q3: "Should the loop auto-commit after each verified task? (yes / no)"


Phase 2+3 — Initialize Loop

Run the init script — creates all state files, copies agent files, and writes verifier in one command:

Bash (macOS/Linux):

bash ~/.cursor/skills/loop-engineer/scripts/init-loop.sh \
  --loop-id <LOOP_ID> \
  --goal "<GOAL>" \
  --stop "all tasks in loop-stack/<LOOP_ID>/PLAN.md checked" \
  --git <yes/no> \
  --mode <MODE> \
  --platform cursor

PowerShell (Windows):

& "$env:USERPROFILE\.cursor\skills\loop-engineer\scripts\init-loop.ps1" `
  -LoopId "<LOOP_ID>" `
  -Goal "<GOAL>" `
  -Stop "all tasks in loop-stack/<LOOP_ID>/PLAN.md checked" `
  -Git <yes/no> `
  -Mode <MODE> `
  -Platform cursor

If the script is missing, install the skill first: git clone https://github.com/vibhasdutta/loop-engineer && bash install.sh --cursor

The script creates loop-stack/<LOOP_ID>/, .cursor/agents/ with all agent .md files + knowledge-sources/, and verifier.md with the actual stop condition substituted.


Phase 4 — Startup Sequence

FULLY AUTONOMOUS from this point. Never pause or ask the user anything.

How parallel dispatch actually works in Cursor: confirmed via official docs (cursor.com/docs/subagents) — subagents are dispatched through the Task tool. Sending multiple Task tool calls in a single message/response runs them simultaneously: "Agent sends multiple Task tool calls in a single message, so subagents run simultaneously." Calling Task once, waiting, then calling it again runs sequentially — the concurrency comes specifically from batching multiple calls into one message, exactly like the steps below describe.

Step 1 — Parallel RESEARCHERS (dynamic count)

Determine researcher count by goal complexity:

  • Simple/single-domain → 2
  • Medium/multi-domain → 3
  • Large/multi-system → 4

Spawn all simultaneously (call the Task tool N times in one response — that's what makes them concurrent, per Cursor's docs).

Divide domains across researchers:

  • Context & Prior Work: source structure, patterns, package.json/pyproject.toml/go.mod, tests
  • External Knowledge & Resources: README, docs/, external APIs, .env.example, configs, integrations
  • Requirements & Constraints: DB schema, data models, state management (for 3+ researchers)
  • Environment & Integration: CI/CD, infrastructure, build, Docker (for 4 researchers)

Each researcher prompt:

Loop directory: loop-stack/<LOOP_ID>/
Focus: {ASSIGNED_DOMAIN} — {specific files and concerns}
GLOBAL DATA FIRST: read loop-stack/.global/MEMORY.md and loop-stack/.global/TOOLS.md.
Write findings to loop-stack/<LOOP_ID>/RESEARCH.md under "## {Domain Name}".
Update STATUS.md "Last Researcher Result".
Follow .cursor/agents/researcher.md.

Stuck-agent check (you do this yourself — no subagent): if one researcher takes much longer than its peers, check its heartbeat line in STATUS.md ## Active Heartbeats. If stale, treat it STUCK, note it, and proceed without it.

Wait for ALL to complete.

Step 2 — RESOURCE SCOUT

Loop directory: loop-stack/<LOOP_ID>/
Check loop-stack/.global/TOOLS.md — if < 7 days old, reuse. Otherwise discover all tools.
Write to loop-stack/<LOOP_ID>/TOOLS.md AND loop-stack/.global/TOOLS.md.
Follow .cursor/agents/resource-scout.md.

Step 3 — PLANNER

Loop directory: loop-stack/<LOOP_ID>/
Read RESEARCH.md (all sections) and TOOLS.md.
Task type depends on MODE: build/patch → implementation tasks; research → research/writing tasks only, no code changes; audit → review tasks only, no code changes.
Create 3–7 tasks with parallel group tags [G1], [G2], etc.
Same group = can run in parallel (independent files/modules).
Different group = sequential dependency.
Example:
  - [ ] [G1] Set up project config and database schema
  - [ ] [G2] Implement API authentication endpoints
  - [ ] [G2] Implement user profile frontend component
  - [ ] [G3] Integration tests
Replace "## Tasks" in PLAN.md. Update STATUS.md.
Follow .cursor/agents/planner.md.

Write loop-stack/<LOOP_ID>/AGENTS.md with # Specialized Agents\n## Status\nNONE CREATED YET. (Agent-factory is on-demand, not a fixed step — see Rules.)

Auto-continue into outer loop.


Phase 5 — Outer Loop

FULLY AUTONOMOUS. Never pause for user input.

Initialize: turns_used = 0, skipped_tasks = [], read total_tasks.

Print per iteration: [Task {done+1}/{total} — {pct}% | Turn {turn}/{MAX_TURNS}]

Mode gating: build (default) — full flow. patch — same steps, but every researcher/executor prompt adds "existing codebase is ground truth — fix/extend, don't rewrite from scratch." research — skip Step 4 (build) and Steps 5–6 (audit) entirely; the Step 3 researcher writes each task's final deliverable directly; Step 7's verifier checks that instead of built code. audit — skip Step 4; Step 5's auditor IS the task (read-only review, findings to RESEARCH.md); Step 6's BLOCK is just recorded, never auto-fixed, always proceeds to Step 7.

Step 1 — Budget check → over MAX_TURNS → Phase 6.

Step 2 — Read state. Identify current parallel group (all unchecked [GN] tasks).

Step 3 — Parallel RESEARCHERS

Spawn one per task in batch (2 if batch=1, more if task is complex). Each appends to RESEARCH.md. Wait for all. Increment turns_used.

Before executors, check AGENTS.md. For every task in the batch that clearly needs domain expertise beyond a generic executor and has no specialist yet, spawn agent-factory for all of them simultaneously in the same response (same parallel-first rule as researchers/executors — create 1 agent file per task in loop-stack/<LOOP_ID>/agents/, update AGENTS.md). Wait for all to finish. Skip this for most tasks.

Step 4 — Parallel EXECUTORS

Spawn one per task simultaneously:

Loop directory: loop-stack/<LOOP_ID>/
GLOBAL DATA FIRST — read loop-stack/.global/MEMORY.md AND loop-stack/.global/TOOLS.md.
Read MEMORY.md, TOOLS.md, PLAN.md, STATUS.md.
READ: RESEARCH.md section "## Task-Specific Research — {this_task}".
Current task: {this_task}. Scope: only files for this task.
Implement. Append discoveries directly to MEMORY.md. Update STATUS.md.
Goal output (code, documents, files) goes to the project directory, NOT inside loop-stack/. loop-stack/ is state-only.
Follow .cursor/agents/executor.md.

Wait for all. Increment turns_used.

Step 5 — Parallel AUDITORS (per task just built). Wait for all.

Step 6 — Process audit results

  • CLEAN/WARN → proceed to verifier
  • BLOCK → auto-fix (one executor retry + re-audit once). Still BLOCK → auto-skip.

Step 7 — Parallel VERIFIERS (per task that passed audit)

Verifier is the final gate: checks the task against RESEARCH.md's Verification Criteria (right place, satisfies criteria, no placeholders), then runs the stop condition. One per task. Wait for all.

Step 8 — Process verifier results

  • PASS → memory-keeper
  • FAIL < 3 attempts → retry from Step 3
  • FAIL ≥ 3 → auto-skip

Step 9 — MEMORY-KEEPER consolidation (single)

This is the only memory-keeper call per batch — executors already appended their raw learnings inline in Step 4. Distill all completed tasks. Write to MEMORY.md + loop-stack/.global/MEMORY.md.

Step 10 — Advance

Mark [x]. Git commit if enabled. Find next group. None → ALL DONE → rename loop-stack/<LOOP_ID>/loop-stack/<LOOP_ID>_DONE/ → Phase 6.


Phase 6 — Completion Report

Write REPORT.md inside the renamed loop directory and print summary.


Rules

  • File copy: shell commands only. Never write agent files manually.
  • Global data first: every agent reads .global/MEMORY.md + .global/TOOLS.md.
  • Parallel first: same group = spawn simultaneously. Different group = sequential. Confirmed native mechanism (cursor.com/docs/subagents): multiple Task tool calls sent in one message run concurrently. One call, wait, another call runs sequentially — batching into a single message/response is what makes it parallel.
  • Custom subagent files: Cursor reads .cursor/agents/*.md (project) or ~/.cursor/agents/*.md (user). It also recognizes .claude/agents/ and .codex/agents/ for cross-tool compatibility, with .cursor/ taking precedence on name conflicts — loop-engineer installs to .cursor/agents/ directly, so this doesn't change anything, just avoids confusion if you see those other dirs referenced elsewhere.
  • Nesting: the main agent and its direct subagents can launch further subagents; a subagent launched by another subagent cannot launch its own (effectively 2 levels deep).
  • Researcher before executor: always. Dynamic count.
  • Agent-factory is on-demand, not a fixed phase step. Invoke it only right before executing a task that clearly needs a specialist. Most loops never call it.
  • knowledge-sources.md is a reference file researchers consult on demand, not a phase step.
  • No watcher agent. Check heartbeats yourself if an agent is slow; never spawn a dedicated watcher.
  • No separate evaluator. Verifier is the final gate: checks the task against RESEARCH.md's Verification Criteria, then runs the stop condition. One agent, one call, same rigor.
  • Audit before verify. Auditor reviews the build first (Step 5); verifier runs last as the final pass/fail gate (Step 7) and is what triggers retry.
  • Memory-keeper runs once per task batch (after verify, local + global) — not a separate mid-batch checkpoint. Executors already append their own learnings to MEMORY.md directly as they work. Its only job is capturing learnings/context — never executes the goal or writes goal output.
  • Executors append to MEMORY.md directly during work.
  • Planner: once at startup after researchers + resource-scout.
  • Fully autonomous: no pauses. Audit BLOCK → auto-fix once → skip. 3 verifier fails → auto-skip.
  • Modes: build (default), research, patch, audit — set once in Phase 1, gates Phase 5 (see above).
  • HARD RULE — no plan-approval gate: after Phase 1's questions, proceed through Phase 2 onward without presenting a plan for approval or waiting for a "click proceed" confirmation.
  • No resume support: every invocation starts a fresh loop. On completion: rename to <LOOP_ID>_DONE/ (bookkeeping only).

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