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Loop Architect

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fabricioctelles
loop-architect

Design well-structured agent loops with best-practice coaching and cross-model review gates before you run them. Use when the user wants to design, build, or set up an agent loop, iterative agent workflow, self-review loop, LLM-as-judge loop, multi-model council, reviewer/judge gate, or goal-driven looping process. Guides goal refinement, typed verification criteria, reviewer/judge selection, privacy boundaries, termination guards, and observability, then emits a RUN_IN_SESSION.md handoff prompt plus portable loop.yaml, loop.resolved.json, LOOP.md, and run-loop.py.

Overview

Publisherfabricioctelles
Repositoryskills
Skill nameloop-architect
Stars
87
Forks
7
Bundled files
16
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 16 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by fabricioctelles on GitHub. Read the source before you install it.

Installation

Install the Loop Architect AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/fabricioctelles/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/loop-architect .claude/skills/loop-architect
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Loop Architect in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Loop Architect on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Loop Architect is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Loop Architect

A loop design coach for Kiro CLI. Interviews you, critiques your design against built-in best-practice rubrics, wires in cross-model reviewers or judges, shows the loop as an ASCII flow preview, and writes portable artifacts you can run immediately with /goal or later with the Python runner.

Based on Looper by Kevin Simback, MIT License. Adapted for Kiro CLI by ft.ia.br.

Why This Exists

Kiro CLI ships /goal (autonomous loop with self-verification) and subagents (parallel pipelines with review loops). These execute a loop. Loop Architect helps you design one worth executing — with a coached goal, typed verification, a cross-model gate, and explicit termination guards.

/goalSubagent pipelineLoop Architect
Layerexecutionexecutiondesign (pre-flight)
Coaches your goalnonoyes
Typed verificationnonoyes (programmatic / judge / human)
Reviewer modelsame modelconfigurabledifferent model, by default
Portable artifactnonoloop.yaml + resolved spec
Runs the loopyesyesyes, via handoff

Workflow

  1. Resolve the target path from the user. Default: ./loop-architect-output. If the target contains an existing loop.yaml, treat as edit/resume.

  2. Load the relevant rubric only when entering that stage:

    • Goal stage: references/goal-rubric.md
    • Verification stage: references/verification-rubric.md
    • Council stage: references/council-rubric.md
    • Control stage: references/control-rubric.md
    • Model detection: references/model-detection.md
  3. Interview in seven stages: goal, verification, host model, council, gates/control, confirmation flow preview, emit/run option. In the control stage, cover execution boundary, isolation, no-progress signals, state, and run logging.

  4. Critique each stage before accepting it. Prefer concrete alternatives over vague warnings. Push weak goals toward outcome, scope, context, and done state. Push weak verification toward programmatic checks first, then judge rubrics, then human signoff.

  5. Keep reviewer and judge roles distinct. A reviewer writes notes. A judge returns a structured verdict. revise_until_clean must name a judge member or human as verdict_source.

  6. Require multiple termination guards: max_iterations, a revision cap on each gate, a no-progress stop, and either a budget cap or an explicit human stop point.

  7. Before any cross-vendor council member is selected, state what context will leave the user's machine, which CLI receives it, which redaction globs apply, and that both execution paths require first-send consent.

  8. Show an ASCII flow preview and ask for confirmation before final emission.

  9. Emit these files into the target:

    • loop.yaml
    • loop.resolved.json
    • LOOP.md
    • RUN_IN_SESSION.md
    • run-loop.py
    • loop-workspace/
    • README.md
  10. After writing loop.yaml, compile it:

    bash
    python3 ~/.kiro/skills/loop-architect/scripts/looper.py compile \
      <target>/loop.yaml \
      --out <target>/loop.resolved.json \
      --render <target>/LOOP.md \
      --session-prompt <target>/RUN_IN_SESSION.md
  11. Ask whether the user wants to run the loop now. If yes:

    • Easy path: Follow RUN_IN_SESSION.md directly, or suggest a /goal one-liner derived from the definition_of_done.
    • Subagent path: If the council uses a model with review_loop capability, offer to execute via a subagent pipeline with native review loops.
    • External path: Explain that run-loop.py is available for running later or outside the session.

Execution Paths

Path 1: /goal (simplest)

When the loop is straightforward and the host is the current Kiro session:

/goal --max 12 <definition_of_done from loop.yaml>

This uses Kiro's native self-verification loop. No cross-model review, but fast and zero-config.

Path 2: Subagent review pipeline (recommended)

When a cross-model reviewer is needed and the host has subagent capability:

Implement the loop following RUN_IN_SESSION.md. Use a subagent as reviewer
with trigger "NEEDS_CHANGES" and max 3 iterations per gate.

This leverages Kiro's native loop_to mechanism for the plan and delivery gates.

Path 3: External Python runner (advanced)

bash
python3 ./loop-architect-output/run-loop.py

For scheduled runs, CI integration, or when you need strict budget enforcement.

File Rules

  • Write argv arrays, never shell command strings, for all model invocations.
  • Do not write API keys, tokens, or credentials into any emitted file.
  • Default redaction globs: .env, .env.*, secrets/**, **/*.key.
  • Keep loop.yaml human-readable and commented.
  • Keep RUN_IN_SESSION.md as the default/easy execution handoff.
  • Copy templates/run-loop.py exactly unless the user asks to edit it.

Helper Scripts

Detect model CLIs:

bash
python3 ~/.kiro/skills/loop-architect/scripts/looper.py detect-models --write

Register a custom CLI:

bash
python3 ~/.kiro/skills/loop-architect/scripts/looper.py register-model <id> \
  --invoke kiro-cli chat --trust-all-tools -p --authed

Compile and render:

bash
python3 ~/.kiro/skills/loop-architect/scripts/looper.py compile <target>/loop.yaml \
  --out <target>/loop.resolved.json \
  --render <target>/LOOP.md \
  --session-prompt <target>/RUN_IN_SESSION.md

Confirmation Flow Preview

text
+--------------------------------+
| 1. Goal + context              |
|    read sources                |
+--------------------------------+
              |
              v
+--------------------------------+
| 2. Draft plan.md               |
|    state -> state.json         |
+--------------------------------+
              |
              v
+--------------------------------+
| 3. Plan gate                   |
|    verdict: reviewer-1         |
+--------------------------------+
  | needs work -> revise <= 3 -> step 2
  | pass
              v
+--------------------------------+
| 4. Write delivery-N.md         |
|    log -> run-log.md           |
+--------------------------------+
              |
              v
+--------------------------------+
| 5. Delivery gate               |
|    verdict: reviewer-1         |
+--------------------------------+
  | needs work -> revise <= 3 -> step 4
  | pass
              v
+--------------------------------+
| 6. Final output                |
|    all gates clean             |
+--------------------------------+

Stops: pass gates | max 12 iterations | no progress x2 | budget 30m, $5.0

Emit Checklist

  • The goal has a clear outcome, scope boundary, context sources, and done state.
  • Verification criteria are typed as programmatic, judge, or human.
  • At least one criterion is not purely vibe-based.
  • Each revise_until_clean gate has a valid verdict_source.
  • Every external invocation is an argv array with a timeout.
  • Cross-vendor egress is scoped, redacted, and consent-gated.
  • loop_control has iteration, revision, no-progress, and budget caps.
  • Execution boundary and isolation are explicit.
  • Observability names a run-log.md and state.json path.
  • Compiled artifacts (loop.resolved.json, LOOP.md, RUN_IN_SESSION.md) pass validation before handoff.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Loop Architect AI skill do?

Design well-structured agent loops with best-practice coaching and cross-model review gates before you run them. Use when the user wants to design, build, or set up an agent loop, iterative agent workflow, self-review loop, LLM-as-judge loop, multi-model council, reviewer/judge gate, or goal-driven looping process. Guides goal refinement, typed verification criteria, reviewer/judge selection, privacy boundaries, termination guards, and observability, then emits a RUN_IN_SESSION.md handoff prompt plus portable loop.yaml, loop.resolved.json, LOOP.md, and run-loop.py.

Why use Loop Architect on TypingMind?

Because you install it once and use it with any model. Loop Architect is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Loop Architect in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/fabricioctelles/skills/tree/main/skills/loop-architect. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Loop Architect?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Loop Architect?

As many as you like. As long as a model supports skills, you can use Loop Architect with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Loop Architect AI skill free?

Yes. It is published on GitHub by fabricioctelles under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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