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SKILL verified MIT Self-run

Loop Engineer

skill-fltman-loop-engineer-loop-engineer · by fltman

>-

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Install

$ agentstack add skill-fltman-loop-engineer-loop-engineer

✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets No
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

View the full security report →

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[![AgentStack Verified](https://agentstack.voostack.com/badges/verified.svg)](https://agentstack.voostack.com/security/report/skill-fltman-loop-engineer-loop-engineer)

Reliability & compatibility

Security review passed
0 installs to date
no reviews yet
2mo ago

Declared compatibility

Claude CodeClaude Desktop

Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.

Preview Execution monitoring

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About

Loop Engineer

Loops are the third object of attention in coding: source code → agent → loop. Your job with this skill is to turn a user's goal into the right loop pattern, scaffolded with guardrails so it can't "loopmaxx" (run forever against a vague objective and burn money).

The one rule that prevents most disasters

Refuse to build a loop without a binary, verifiable exit condition. "Improve the UX" has no pass/fail and produces infinite loops + large API bills. "Make npm test exit 0" does. If the user's goal isn't binary, your first job is to help them make it binary — not to scaffold the loop.

Decision tree — pick the pattern

Is the goal one binary check the agent iterates toward (tests pass, lint clean)?
│
├─ YES, one agent is enough .......................... HEADLESS WHILE-LOOP
│        (templates/headless-loop.sh)                  or first-party /goal
│
├─ Quality matters & "done" is a judgment call ...... EVALUATOR-OPTIMIZER
│        (writing, code that must meet a bar)          (templates/evaluator-optimizer.sh)
│        → ALWAYS a separate critic agent
│
├─ You're improving the PROMPT itself, not the output  META / PROMPT-REFINEMENT
│        (a loop that rewrites the prompt another      (templates/meta-prompt-refine.sh)
│         loop runs, scored on a test set)             → needs holdout + anchor set
│
├─ Subtasks can't be predicted up front ............. ORCHESTRATOR FAN-OUT
│        (delegate dynamically, then verify each)      (templates/fanout-orchestrator.sh
│                                                        or the Workflow tool)
│
└─ Just run something on a schedule / interval ...... /loop or cloud Routines
         (poll, babysit PRs, recurring checks)         (reference/primitives.md)

Nest these: an orchestrator loop can spawn evaluator-optimizer inner loops; a meta loop wraps a headless loop and rewrites its prompt between runs.

Non-negotiable guardrail checklist

Before scaffolding ANY loop, confirm all five. See reference/guardrails.md for the why.

  1. Verifiable exit condition — a command/check that returns binary done/not-done.
  2. Max-iteration cap — a hard for bound, enforced in code, not in the prompt.
  3. Budget cap in code — sum total_cost_usd from --output-format json; stop

before the next call when over budget. Alerts are not enforcement.

  1. Sandbox — loops that edit files/run commands run in a worktree, container,

or branch — never unattended on main.

  1. Human checkpoint — for anything outward-facing (push, deploy, send), the

loop stops and asks, or only proposes.

A loop missing #1 or #2 is a bug, not a loop. Don't ship it.

How to use this skill

  1. Read the user's goal; map it to a pattern via the decision tree.
  2. If the goal isn't binary/verifiable, fix that first (with the user).
  3. Copy the matching template from templates/, fill the config block, and wire

the real exit check. Keep the guardrails.

  1. Walk the user through the five-point checklist for their specific loop.
  2. Tell them how to run it, how to stop it, and what it costs per iteration.

Reference (load as needed)

  • reference/primitives.md — every Claude Code loop primitive: claude -p,

--continue/--resume, --output-format json (cost), /loop, /goal, Tasks (~/.claude/tasks, CLAUDE_CODE_TASK_LIST_ID), cloud Routines.

  • reference/taxonomy.md — the five patterns in depth + when to use each.
  • reference/guardrails.md — failure modes (loopmaxxing, cost blowups, evaluator

collusion/drift) and the mitigations, with the cautionary numbers.

  • templates/*.sh, templates/goal-loop.md — runnable scaffolds.

What NOT to claim

Anthropic's Building Effective Agents is the canonical taxonomy source, but it does not officially endorse "shell fan-out loops" or "headless-in-CI with Task tracking" — those are community patterns. Attribute them as such.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

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Versions

  • v0.1.0 Imported from the upstream source.