---
name: aif-loop
description: Run a strict multi-iteration Reflex Loop with phases (PLAN, PRODUCE||PREPARE, EVALUATE, CRITIQUE, REFINE) to improve an artifact until quality gates pass or iteration limits are reached. Use when user asks for iterative refinement, quality-gated generation, or "generate -> critique -> refine" loops.
argument-hint: "[new|resume|status|stop|list|history|clean] [task or alias]"
allowed-tools: Read Write Edit Glob Grep Bash Task AskUserQuestion Questions
disable-model-invocation: true
---

# Loop - Reflex Iteration Workflow

Run a result-focused iterative loop with strict phase contracts, evaluation rules, and persistent state between sessions.

Terminology:

- **loop** = one full execution for a task alias (stored in `run.json`, identified by `run_id`)
- **iteration** = one cycle inside that loop

## Core Idea

Each iteration executes 6 phases with parallel execution where possible:

1. `PLAN` - short plan for current iteration
2. `PRODUCE` - produce one `artifact.md` ← **runs in parallel with PREPARE**
3. `PREPARE` - generate check scripts and test definitions from rules ← **runs in parallel with PRODUCE**
4. `EVALUATE` - run prepared checks + content rules against artifact, score result. Uses parallel `Task` agents for independent check groups
5. `CRITIQUE` - precise issues + fixes (only if fail)
6. `REFINE` - rewrite artifact using critique (only if fail)

```text
         PLAN
           │
    ┌──────┴──────┐
    ↓             ↓          ← parallel (Task tool)
 PRODUCE      PREPARE
 (artifact)   (checks)
    ↓             ↓
    └──────┬──────┘
           ↓
       EVALUATE              ← parallel check execution (Task tool)
       ┌───┼───┐
       ↓   ↓   ↓
      exec content aggregate
       └───┼───┘
           ↓
       CRITIQUE (if fail)
           ↓
       REFINE (if fail)
```

Stop when quality is good enough, no major issues remain, or iteration limit is reached.

## Persistence Contract

Use exactly 3+1 files for state (where `current.json` exists only while a loop is active):

```text
.ai-factory/evolution/current.json
.ai-factory/evolution/<task-alias>/run.json
.ai-factory/evolution/<task-alias>/history.jsonl
.ai-factory/evolution/<task-alias>/artifact.md
```

Do not create extra index files or per-iteration folder trees unless user explicitly asks.

### File Roles

- `current.json`: pointer to active loop only; delete it when loop becomes `completed`/`stopped`/`failed`
- `run.json`: single source of truth for current loop state
- `history.jsonl`: append-only event log (one JSON object per line)
- `artifact.md`: single source of truth for artifact content (written after PRODUCE and REFINE phases, never duplicated in `run.json`)

## Command Modes

Parse `$ARGUMENTS`:

- `status` - show active loop status from `current.json` and stop
- `resume [alias]` - continue active loop or loop by alias
- `stop [reason]` - stop active loop with reason (`user_stop` if omitted)
- `new <task>` or no mode + task text - start new loop
- `list` - list all task aliases with status (running/stopped/completed/failed)
- `history [alias]` - show event history for a loop (default: active loop)
- `clean [alias|--all]` - remove loop files for a stopped/completed/failed loop (requires user confirmation, always confirm before deleting)

If no task and no active loop exists, ask user for task prompt.

## Step 0: Load Context

Read these files if present:

- `.ai-factory/DESCRIPTION.md`
- `.ai-factory/ARCHITECTURE.md`
- `.ai-factory/RULES.md`

Use them to keep outputs aligned with project conventions.

## Step 0.1: Handle Non-Iteration Commands

If command is `status`, `stop`, `list`, `history`, or `clean`, execute and stop:

- **`status`**: read `current.json`; if file exists, read pointed `run.json` and display `alias | status | iteration | phase | current_step | last_score | updated_at`; if file is missing, report that no loop is active
- **`stop [reason]`**: stop active running loop only; set `run.json.status = "stopped"` and `run.json.stop.reason = <reason or "user_stop">`, append `stopped` event to `history.jsonl`, then delete `current.json` (active pointer cleared) and exit
- **`list`**: scan `.ai-factory/evolution/` directories, read each `run.json`, display table of `alias | status | iteration | last_score | updated_at`
- **`history [alias]`**: read `history.jsonl` for the alias (or active loop), display formatted event timeline
- **`clean [alias|--all]`**: show what will be deleted, ask for explicit user confirmation via `AskUserQuestion`, then delete loop directory. Only clean stopped/completed/failed loops — refuse to clean running loops. Update `current.json` if needed.

## Step 1: Initialize or Resume Loop

### 1.1 Ensure directories

```bash
mkdir -p .ai-factory/evolution
```

### 1.2 Alias and IDs (new loop)

Generate:

- `task_alias`: lowercase hyphen slug (3-64 chars)
- `run_id`: `<task_alias>-<yyyyMMdd-HHmmss>`

### 1.3 Write `current.json`

```json
{
  "active_run_id": "courses-api-ddd-20260218-120000",
  "task_alias": "courses-api-ddd",
  "status": "running",
  "updated_at": "2026-02-18T12:00:00Z"
}
```

### 1.4 Write initial `run.json`

```json
{
  "run_id": "courses-api-ddd-20260218-120000",
  "task_alias": "courses-api-ddd",
  "status": "running",
  "iteration": 1,
  "max_iterations": 4,
  "phase": "A",
  "current_step": "PLAN",
  "task": {
    "prompt": "OpenAPI 3.1 spec + DDD notes + JSON examples",
    "ideal_result": "..."
  },
  "criteria": {
    "name": "loop_default_v1",
    "version": 1,
    "phase": {
      "A": { "threshold": 0.8, "active_levels": ["A"] },
      "B": { "threshold": 0.9, "active_levels": ["A", "B"] }
    },
    "rules": []
  },
  "plan": [],
  "prepared_checks": null,
  "evaluation": null,
  "critique": null,
  "stop": { "passed": false, "reason": "" },
  "last_score": 0,
  "stagnation_count": 0,
  "created_at": "2026-02-18T12:00:00Z",
  "updated_at": "2026-02-18T12:00:00Z"
}
```

### 1.5 Resume Logic

When resuming a loop:

1. Read `run.json` to get `current_step` and `iteration`
2. Read last event from `history.jsonl` to confirm consistency
3. If `run.json.current_step` indicates a phase was interrupted:
   - Re-execute from that phase (do not skip)
   - `PRODUCE_PREPARE`: always re-run both PRODUCE and PREPARE (idempotent — artifact overwrites, checks regenerate)
4. If `run.json.status` is `stopped`, `completed`, or `failed`, inform user and suggest `new` (for `failed` runs, also show the last `phase_error` event from `history.jsonl` so user understands what went wrong)

## Step 2: Interactive Setup (new loop)

### Quick mode (default, confirmation-first)

If the task prompt contains enough context to infer task type and ideal result:

1. Auto-detect task type from prompt (API spec, code, docs, config)
2. Load matching template from `references/CRITERIA-TEMPLATES.md`
3. Draft inferred rules, phase thresholds (fallback: A=0.8, B=0.9), and max iterations (default: `4`)
4. Show inferred settings as a draft summary
5. **Always ask explicit confirmation of success criteria** (rules/thresholds) via `AskUserQuestion`, even if criteria were already present in the task text
6. **Always ask explicit confirmation of max iterations** via `AskUserQuestion`, even if iteration count was already present in the task text
7. If user changes either criteria or max iterations, update the draft and re-confirm both fields
8. Start iteration 1 only after both confirmations are explicit
9. If task type cannot be auto-detected (ambiguous or mixed prompt), fall through to full setup immediately

### Full setup

Critical guardrail:

- Always re-ask and explicitly confirm success criteria and max iterations, even if both are already written in the task prompt.

Ask concise setup questions before first iteration:

1. **Task type** - what kind of artifact? (API spec, code, docs, config, other) - used to load template from `references/CRITERIA-TEMPLATES.md`
2. **Ideal result** definition
3. **Mandatory checks** (tests, schema/contract, specific requirements)
4. **Quality threshold** (A/B phases)
5. **Max iterations** (default: `4`)
6. **What counts as a major issue**
7. Explicit confirmation: "Confirm these success criteria?"
8. Explicit confirmation: "Confirm max iterations = N?"

Generate evaluation rules from answers:

- Load matching template from `references/CRITERIA-TEMPLATES.md` as starting point
- Add task-specific rules based on ideal result and mandatory checks
- Let user review and adjust rules before starting

Persist answers and generated rules inside `run.json.criteria` (snapshot for reproducibility).

Never treat criteria or iteration limits parsed from task text as final until the user explicitly confirms both.

Normalization rules before persisting:

- `run.json.max_iterations` is the single source of truth for iteration limit
- every rule must be expanded to full RULE-SCHEMA format (`id`, `description`, `severity`, `weight`, `phase`, `check`)
- if template shorthand omitted `weight`, derive from severity (`fail`=2, `warn`=1, `info`=0)

## Step 3: Phase Contracts

Before running phases, load:

- `references/PHASE-CONTRACTS.md` - strict I/O contracts for each phase
- `references/RULE-SCHEMA.md` - rule format and score calculation

### 3.1 Phases

- `PLAN` - generates iteration plan (sequential)
- `PRODUCE` - generates artifact (parallel with PREPARE)
- `PREPARE` - generates check scripts/definitions from rules + task prompt (parallel with PRODUCE)
- `EVALUATE` - runs prepared checks + content rules, aggregates score (parallel check groups via `Task`)
- `CRITIQUE` - identifies issues with fix instructions (sequential, only on fail)
- `REFINE` - applies fixes to artifact (sequential, only on fail)

### 3.2 Parallel Execution Model

Two levels of parallelism via `Task` tool:

1. **Inter-phase**: PRODUCE and PREPARE run as parallel `Task` agents after PLAN completes. Both depend only on PLAN output.
2. **Intra-phase**: EVALUATE spawns parallel `Task` agents for independent check groups (executable checks via Bash, content rules via Read/Grep). Aggregates results into final score.

### 3.3 Phase Output Format

Each phase produces its defined output (see PHASE-CONTRACTS.md). No envelope wrapping. No router output.

## Step 4: Iteration Execution

For each iteration:

1. Set `run.json.current_step = "PLAN"`, run PLAN phase
2. Set `run.json.current_step = "PRODUCE_PREPARE"`, launch both as parallel `Task` agents:
   - Task A (PRODUCE): generates artifact → writes to `artifact.md`
   - Task B (PREPARE): generates check scripts/definitions from rules + plan
   - Wait for both to complete
3. Set `run.json.current_step = "EVALUATE"`, run EVALUATE phase:
   - Spawn parallel `Task` agents for independent check groups:
     - Executable checks (compile, lint, tests) → `Task` with `Bash`
     - Content rules (structure, completeness, style) → `Task` with `Read`/`Grep`
   - Aggregate results into score
4. If `passed=false`:
   - Set `run.json.current_step = "CRITIQUE"`, run CRITIQUE phase
   - Set `run.json.current_step = "REFINE"`, run REFINE phase
   - Write updated artifact to `artifact.md`
   - Increment iteration and continue
5. If `phase=A` and `passed=true`:
   - Switch to `phase=B`, activate B-level rules
   - Set `run.json.current_step = "PREPARE"`, re-run PREPARE with `phase=B` to materialize B-level checks (no PLAN/PRODUCE — artifact already passed A)
   - Set `run.json.current_step = "EVALUATE"`, run EVALUATE against the same artifact with B-level prepared checks
   - If B evaluation also passes → stop with success (`threshold_reached`)
   - If B evaluation fails → continue to CRITIQUE → REFINE, then increment iteration
6. If `phase=B` and `passed=true`:
   - Stop with success (`threshold_reached`)

### Fallback to Sequential

If `Task` tool is unavailable or returns errors, fall back to sequential execution: PLAN → PRODUCE → PREPARE → EVALUATE → CRITIQUE → REFINE. The loop must work without parallelism.

## Step 5: Stop Conditions

Stop when any condition is met:

1. `phase=B` and `passed=true` (`reason=threshold_reached`)
2. no `fail`-severity rules failed in current evaluation (`reason=no_major_issues`) — even if score is below threshold, the artifact has no blocking issues and only `warn`/`info` remain
3. `iteration >= run.max_iterations` (`reason=iteration_limit`)
4. explicit user stop (`reason=user_stop`)
5. stagnation detected (`reason=stagnation`)

### Stagnation rule

Track score progress:

- `delta = score - last_score`
- if `delta < 0.02` and there are no severity `fail` blockers, increment `stagnation_count`
- if `stagnation_count >= 2`, stop with `stagnation`

## Step 6: Persistence Writes (every step)

After each phase output:

1. Update `run.json` (including `current_step`)
2. Append event to `history.jsonl`
3. Update `current.json.updated_at`
4. Write `artifact.md` to disk after PRODUCE and REFINE phases
5. Before REFINE overwrites `artifact.md`, save a SHA-256 hash of the previous artifact in the `refinement_done` event payload as `"previous_artifact_hash"` (enables integrity verification without bloating history)

Event names:

- `run_started`
- `plan_created`
- `artifact_created`
- `checks_prepared`
- `evaluation_done`
- `critique_done`
- `refinement_done`
- `phase_switched`
- `iteration_advanced`
- `phase_error`
- `stopped`
- `failed`

`history.jsonl` example line:

```json
{"ts":"2026-02-18T12:01:10Z","run_id":"courses-api-ddd-20260218-120000","iteration":1,"phase":"A","step":"EVALUATE","event":"evaluation_done","status":"ok","payload":{"score":0.72,"passed":false}}
```

## Step 7: Post-Loop

After the loop stops (any reason):

1. Display final state summary (`iteration`, `max_iterations`, `phase`, `final score`, `stop reason`)
2. If `stop reason = iteration_limit` and latest evaluation has `passed=false`, include mandatory **distance-to-success** details:
   - active phase threshold and final score
   - numeric gap to threshold (`threshold - score`, floor at `0`)
   - remaining failed `fail`-severity rule count + blocking rule IDs
   - rules progress (`passed_rules / total_rules`)
3. Ask user where to save the final artifact (default: keep in `.ai-factory/evolution/<alias>/artifact.md`)
4. Offer to copy artifact to a user-specified path
5. Suggest next skills based on artifact type:
   - API spec -> `/aif-plan` to implement it
   - Code -> `/aif-verify` to check it
   - Docs -> `/aif-docs` to integrate it
6. Update `run.json.status` based on stop reason, and if `current.json` points to this loop, delete `current.json` (no active loop remains):

| Stop reason | Status |
|-------------|--------|
| `threshold_reached` | `completed` |
| `no_major_issues` | `completed` |
| `user_stop` | `stopped` |
| `iteration_limit` | `stopped` |
| `stagnation` | `stopped` |
| `phase_error` | `failed` |

## Step 8: Response Format to User

Show a compact summary after each iteration — do NOT dump full `run.json` or `artifact.md` content into the conversation. The artifact is already on disk; duplicating it wastes context.

### Iteration summary format

```text
── Iteration {N}/{max} | Phase {A|B} | Score: {score} | {PASS|FAIL} ──
Plan: {1-line summary of plan focus}
Hash: {first 8 chars of artifact SHA-256}
Changed: {list of added/modified sections, or "initial generation"}
Failed: {comma-separated rule IDs, or "none"}
Warnings: {comma-separated rule IDs, or "none"}
Artifact: .ai-factory/evolution/<alias>/artifact.md
```

- `Hash` — lets the user verify which version they're looking at without reading the full artifact
- `Changed` — shows what actually moved between iterations so regressions are visible from the summary alone

If `passed=false`, append a compact critique summary (rule ID + 1-line fix instruction per issue). Do not repeat the full artifact or full evaluation object.

When the loop terminates with `reason=iteration_limit` and `passed=false`, append a compact `distance_to_success` block to the final response.

### Full output exceptions

Show the **full artifact content** (not just summary) in these cases only:

1. **Loop termination** — the final iteration always outputs the complete artifact
2. **Phase A → B transition** — show the phase-A-passing artifact in full once at the transition boundary for visibility (B-level evaluation still runs immediately per Step 4)
3. **Explicit user request** — user asks to see the full artifact mid-loop

## Step 9: Context Management

The loop generates significant context per iteration (subagent results, evaluation data, critique). After several iterations the conversation context grows large, degrading LLM quality.

All loop state is persisted to disk — clearing context loses nothing. The `resume` command fully reconstructs state from files.

### When to recommend context clear

Recommend clearing context to the user in these situations:

1. **After iteration 2** — the midpoint of a default 4-iteration loop
2. **On Phase A → B transition** — natural boundary, new evaluation scope begins
3. **After any iteration where `iteration >= 3`** — context is already heavy

### How to recommend

After the iteration summary, append:

```text
💡 Context is growing. Recommended: /clear then /aif-loop resume
   All state is saved on disk — nothing will be lost.
```

Do not force or auto-clear. The user decides. If the user ignores the recommendation, continue normally.

## Error Recovery

### Invalid phase output

If a phase produces output that does not match its contract:

1. Log the error to `history.jsonl` with event `phase_error`
2. Retry the phase once with the same inputs
3. If retry also fails, stop the loop with `reason=phase_error` and display the error

### Corrupted `run.json`

If `run.json` is missing or unparseable:

1. Read `history.jsonl` to reconstruct the last known state
2. Rebuild `run.json` from the most recent events (last iteration, phase, score, etc.)
3. If `history.jsonl` is also missing/empty, inform user and suggest starting a new loop

## Important Rules

1. `run.json` is the only source of current state truth (does NOT store artifact content)
2. `artifact.md` on disk is the single source of truth for artifact content — never duplicate it in `run.json`
3. `history.jsonl` is append-only; do not edit old events
4. Keep loop fast: short plans, targeted critique, minimal rewrites
5. Do not create extra files beyond the 3+1 persistence files
6. Evaluator must remain strict and non-creative
7. Refiner changes only what is needed to pass failed rules
8. Start simple and add complexity only when metrics show need
9. Retry failed phases exactly once before stopping
10. Use compact iteration summaries by default (Step 8). Full artifact output is allowed only in Step 8 exceptions; never dump full `run.json` into conversation.
11. Recommend context clear at strategic points (Step 9) — after iteration 2, on phase transition, or when iteration >= 3

## Examples

```text
/aif-loop new OpenAPI 3.1 spec + DDD notes + JSON examples
/aif-loop resume
/aif-loop resume courses-api-ddd
/aif-loop status
/aif-loop stop
/aif-loop list
/aif-loop history
/aif-loop history courses-api-ddd
/aif-loop clean courses-api-ddd
/aif-loop clean --all
```