Iterate

What the source does

• FilesystemNone observedNo reads or writes in the source
• NetworkNone observedNo outbound calls in the source
• ShellNone observedNo commands run in the source
• DependenciesPinnedNothing floats
• SecretsNone foundNo keys in source

Five audit layers

Gates applied: no behavioural pass.

Audit 0.4.1 · source sha 4e163a7a8095 · 19 Oct 2026Report an issue or request a re-scan

• Publishersharpdeveye GitHub userOwner of the repository this skill is read from
• Source foldersharpdeveye/maestro/source/skills/iterateThe exact folder we audited
• LicenceMITAs the repository states it
• Commit pinned for this verdict4e163a7a8095A new audit runs when the source changes
• Repository statusNot archivedLast push 173 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its description: Use when the workflow needs to self-correct, improve over time, or establish feedback loops and evaluation cycles.

---
name: iterate
description: "Use when the workflow needs to self-correct, improve over time, or establish feedback loops and evaluation cycles."
argument-hint: "[target area]"
category: enhancement
version: 2.0.0
user-invocable: true
---

## MANDATORY PREPARATION

Invoke /agent-workflow — it contains workflow principles, anti-patterns, and the **Context Gathering Protocol**. Follow the protocol before proceeding — if no workflow context exists yet, you MUST run /teach-maestro first.

Consult the feedback-loops reference in the agent-workflow skill for evaluation patterns and self-correction strategies.

---

Set up feedback loops that make workflows self-correcting and continuously improving. Iteration transforms one-shot gambles into convergent, reliable systems.

### Feedback Loop Design

### Step 1: Define Quality Criteria

What does "good output" look like? Score dimensions:

| Dimension | Weight | Threshold | Measurement |
|-----------|--------|-----------|-------------|
| Accuracy | 0.4 | ≥ 0.8 | Factual correctness check |
| Completeness | 0.3 | ≥ 0.7 | Required fields present |
| Format | 0.2 | ≥ 0.9 | Schema compliance |
| Tone | 0.1 | ≥ 0.6 | Appropriate for audience |

### Step 2: Choose Evaluator Type

Match evaluator to requirements:

- **Rule-based**: Schema validation, field presence, value ranges (fast, free)
- **Self-check**: Same model evaluates own output (fast, cheap, less reliable)
- **Cross-model**: Different model evaluates (slower, more reliable)
- **Human-in-the-loop**: Human review (slowest, most reliable, doesn't scale)
- **Hybrid**: Rules first, then model check for what rules can't catch

### Step 3: Design the Correction Loop

```text
generate(input) → evaluate(output) → score
  if score ≥ threshold → return output
  if score < threshold AND attempts < max →
    enrich input with evaluator feedback
    generate again (with feedback)
  if attempts ≥ max → fallback or escalate
```

**Critical**: The retry input MUST be different from the original. Include:

- The evaluator's specific feedback
- What was wrong and why
- A suggestion for how to fix it

### Step 4: Set Up Regression Detection

When changing prompts, models, or tools:

1. Run golden test set with OLD config → baseline scores
2. Run golden test set with NEW config → new scores
3. Compare: improvement ≥ 5% → accept; regression ≥ 5% → reject

### Step 5: Continuous Monitoring

For production workflows:

- Sample 1-5% of outputs for automated evaluation
- Track quality scores over time
- Alert on downward trends
- A/B test changes before full rollout

### Iteration Checklist

- [ ] Quality criteria defined with weights and thresholds
- [ ] Evaluator selected and configured
- [ ] Correction loop has max attempts limit
- [ ] Feedback is injected into retries (not identical retry)
- [ ] Golden test set exists with ≥ 10 cases
- [ ] Regression detection configured for changes
- [ ] Production monitoring in place

### Recommended Next Step

After setting up feedback loops, run `/evaluate` to validate the loop with real scenarios, then `/refine` for final polish.

**NEVER**:

- Retry with the exact same input (definition of insanity)
- Use the same weak model to both generate and evaluate
- Skip the max attempts limit (infinite loops are real)
- Deploy changes without regression testing against golden set
- Monitor only errors — track quality scores over time

Excerpt, first 3,403 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

git clone https://github.com/sharpdeveye/maestro
git -C maestro checkout 4e163a7a8095

Works in

1. 2026-10-19
   ✓SAFE B 89 / 100

   first audit · source 4e163a7a8095

Questions about Iterate

• github.com/sharpdeveye/maestro/tree/main/source/skills/iterate — the audited source at commit 4e163a7a8095 and every finding aboveObserved · 19 Oct 2026

Spotted something wrong? Report a correction · Methodology