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---
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name: loop-architect
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description: >
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Design well-structured agent loops with best-practice coaching and cross-model
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review gates before you run them. Use when the user wants to design, build, or
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set up an agent loop, iterative agent workflow, self-review loop, LLM-as-judge
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loop, multi-model council, reviewer/judge gate, or goal-driven looping process.
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Guides goal refinement, typed verification criteria, reviewer/judge selection,
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privacy boundaries, termination guards, and observability, then emits a
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RUN_IN_SESSION.md handoff prompt plus portable loop.yaml, loop.resolved.json,
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LOOP.md, and run-loop.py.
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metadata:
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author: https://ft.ia.br
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version: "1.0"
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date: 2026-06-25
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repository: https://github.com/fabricioctelles/skills
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license: MIT
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original_project: https://github.com/ksimback/looper
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original_author: Kevin Simback (@ksimback)
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attribution: >
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Reinterpretation of Looper (MIT License) by Kevin Simback, adapted for
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Kiro CLI with native /goal, subagent, and review loop integration.
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category: code-scaffolding-and-templates
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---
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# Loop Architect
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A loop design coach for Kiro CLI. Interviews you, critiques your design against
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built-in best-practice rubrics, wires in cross-model reviewers or judges, shows
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the loop as an ASCII flow preview, and writes portable artifacts you can run
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immediately with `/goal` or later with the Python runner.
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> Based on [Looper](https://github.com/ksimback/looper) by Kevin Simback, MIT License.
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> Adapted for Kiro CLI by ft.ia.br.
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## Why This Exists
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Kiro CLI ships `/goal` (autonomous loop with self-verification) and subagents
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(parallel pipelines with review loops). These **execute** a loop. Loop Architect
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helps you **design** one worth executing — with a coached goal, typed
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verification, a cross-model gate, and explicit termination guards.
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| | `/goal` | Subagent pipeline | **Loop Architect** |
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|---|---|---|---|
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| Layer | execution | execution | **design (pre-flight)** |
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| Coaches your goal | no | no | **yes** |
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| Typed verification | no | no | **yes (programmatic / judge / human)** |
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| Reviewer model | same model | configurable | **different model, by default** |
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| Portable artifact | no | no | **loop.yaml + resolved spec** |
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| Runs the loop | **yes** | **yes** | **yes, via handoff** |
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## Workflow
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1. Resolve the target path from the user. Default: `./loop-architect-output`. If
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the target contains an existing `loop.yaml`, treat as edit/resume.
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2. Load the relevant rubric only when entering that stage:
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- Goal stage: `references/goal-rubric.md`
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- Verification stage: `references/verification-rubric.md`
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- Council stage: `references/council-rubric.md`
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- Control stage: `references/control-rubric.md`
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- Model detection: `references/model-detection.md`
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3. Interview in seven stages: goal, verification, host model, council,
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gates/control, confirmation flow preview, emit/run option. In the control
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stage, cover execution boundary, isolation, no-progress signals, state, and
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run logging.
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4. Critique each stage before accepting it. Prefer concrete alternatives over
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vague warnings. Push weak goals toward outcome, scope, context, and done
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state. Push weak verification toward programmatic checks first, then judge
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rubrics, then human signoff.
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5. Keep reviewer and judge roles distinct. A reviewer writes notes. A judge
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returns a structured verdict. `revise_until_clean` must name a judge member
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or `human` as `verdict_source`.
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6. Require multiple termination guards: `max_iterations`, a revision cap on
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each gate, a no-progress stop, and either a budget cap or an explicit human
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stop point.
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7. Before any cross-vendor council member is selected, state what context will
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leave the user's machine, which CLI receives it, which redaction globs apply,
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and that both execution paths require first-send consent.
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8. Show an ASCII flow preview and ask for confirmation before final emission.
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9. Emit these files into the target:
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- `loop.yaml`
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- `loop.resolved.json`
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- `LOOP.md`
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- `RUN_IN_SESSION.md`
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- `run-loop.py`
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- `loop-workspace/`
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- `README.md`
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10. After writing `loop.yaml`, compile it:
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```bash
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python3 ~/.kiro/skills/loop-architect/scripts/looper.py compile \
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<target>/loop.yaml \
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--out <target>/loop.resolved.json \
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--render <target>/LOOP.md \
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--session-prompt <target>/RUN_IN_SESSION.md
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```
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11. Ask whether the user wants to run the loop now. If yes:
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- **Easy path**: Follow `RUN_IN_SESSION.md` directly, or suggest a `/goal`
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one-liner derived from the `definition_of_done`.
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- **Subagent path**: If the council uses a model with `review_loop`
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capability, offer to execute via a subagent pipeline with native review
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loops.
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- **External path**: Explain that `run-loop.py` is available for running
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later or outside the session.
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## Execution Paths
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### Path 1: `/goal` (simplest)
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When the loop is straightforward and the host is the current Kiro session:
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```
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/goal --max 12 <definition_of_done from loop.yaml>
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```
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This uses Kiro's native self-verification loop. No cross-model review, but
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fast and zero-config.
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### Path 2: Subagent review pipeline (recommended)
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When a cross-model reviewer is needed and the host has `subagent` capability:
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```
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Implement the loop following RUN_IN_SESSION.md. Use a subagent as reviewer
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with trigger "NEEDS_CHANGES" and max 3 iterations per gate.
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```
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This leverages Kiro's native `loop_to` mechanism for the plan and delivery
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gates.
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### Path 3: External Python runner (advanced)
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```bash
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python3 ./loop-architect-output/run-loop.py
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```
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For scheduled runs, CI integration, or when you need strict budget enforcement.
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## File Rules
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- Write argv arrays, never shell command strings, for all model invocations.
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- Do not write API keys, tokens, or credentials into any emitted file.
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- Default redaction globs: `.env`, `.env.*`, `secrets/**`, `**/*.key`.
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- Keep `loop.yaml` human-readable and commented.
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- Keep `RUN_IN_SESSION.md` as the default/easy execution handoff.
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- Copy `templates/run-loop.py` exactly unless the user asks to edit it.
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## Helper Scripts
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Detect model CLIs:
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```bash
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python3 ~/.kiro/skills/loop-architect/scripts/looper.py detect-models --write
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```
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Register a custom CLI:
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```bash
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python3 ~/.kiro/skills/loop-architect/scripts/looper.py register-model <id> \
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--invoke kiro-cli chat --trust-all-tools -p --authed
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```
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Compile and render:
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```bash
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python3 ~/.kiro/skills/loop-architect/scripts/looper.py compile <target>/loop.yaml \
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--out <target>/loop.resolved.json \
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--render <target>/LOOP.md \
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--session-prompt <target>/RUN_IN_SESSION.md
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```
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## Confirmation Flow Preview
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```text
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+--------------------------------+
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| 1. Goal + context |
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| read sources |
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+--------------------------------+
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v
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+--------------------------------+
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| 2. Draft plan.md |
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| state -> state.json |
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+--------------------------------+
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v
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+--------------------------------+
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| 3. Plan gate |
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| verdict: reviewer-1 |
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+--------------------------------+
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| needs work -> revise <= 3 -> step 2
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| pass
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v
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+--------------------------------+
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| 4. Write delivery-N.md |
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| log -> run-log.md |
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+--------------------------------+
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v
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+--------------------------------+
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| 5. Delivery gate |
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| verdict: reviewer-1 |
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+--------------------------------+
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| needs work -> revise <= 3 -> step 4
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| pass
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v
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+--------------------------------+
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| 6. Final output |
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| all gates clean |
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+--------------------------------+
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Stops: pass gates | max 12 iterations | no progress x2 | budget 30m, $5.0
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```
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## Emit Checklist
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- The goal has a clear outcome, scope boundary, context sources, and done state.
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- Verification criteria are typed as `programmatic`, `judge`, or `human`.
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- At least one criterion is not purely vibe-based.
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- Each `revise_until_clean` gate has a valid `verdict_source`.
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- Every external invocation is an argv array with a timeout.
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- Cross-vendor egress is scoped, redacted, and consent-gated.
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- `loop_control` has iteration, revision, no-progress, and budget caps.
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- Execution boundary and isolation are explicit.
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- Observability names a `run-log.md` and `state.json` path.
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- Compiled artifacts (`loop.resolved.json`, `LOOP.md`, `RUN_IN_SESSION.md`)
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pass validation before handoff.
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