--- name: loop-architect description: > 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. metadata: author: https://ft.ia.br version: "1.0" date: 2026-06-25 repository: https://github.com/fabricioctelles/skills license: MIT original_project: https://github.com/ksimback/looper original_author: Kevin Simback (@ksimback) attribution: > Reinterpretation of Looper (MIT License) by Kevin Simback, adapted for Kiro CLI with native /goal, subagent, and review loop integration. category: code-scaffolding-and-templates --- # 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](https://github.com/ksimback/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. | | `/goal` | Subagent pipeline | **Loop Architect** | |---|---|---|---| | Layer | execution | execution | **design (pre-flight)** | | Coaches your goal | no | no | **yes** | | Typed verification | no | no | **yes (programmatic / judge / human)** | | Reviewer model | same model | configurable | **different model, by default** | | Portable artifact | no | no | **loop.yaml + resolved spec** | | Runs the loop | **yes** | **yes** | **yes, 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 \ /loop.yaml \ --out /loop.resolved.json \ --render /LOOP.md \ --session-prompt /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 ``` 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 \ --invoke kiro-cli chat --trust-all-tools -p --authed ``` Compile and render: ```bash python3 ~/.kiro/skills/loop-architect/scripts/looper.py compile /loop.yaml \ --out /loop.resolved.json \ --render /LOOP.md \ --session-prompt /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.