skill-iterative-loop

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Iterative Loop Execution

Overview

Systematic iterative execution with clear goals, exit conditions, and progress tracking.

Core principle: Define goal → Set max iterations → Execute → Evaluate → Loop or complete.

When to Use

Use this skill when user wants to:

• Execute a task multiple times with refinements
• Loop until a condition is met
• Iteratively improve something (code, tests, performance)
• Retry operations with modifications
• Progressive enhancement in rounds

Do NOT use for:

• Single execution ("run tests once")
• Manual step-by-step work
• Infinite loops without bounds
• Simple retry logic (use skill-debug)

The Process

Phase 1: Loop Setup

Step 1: Understand the Intent

**Loop Intent:**

Goal: [what should be achieved]
Success criteria: [how do we know we're done]
Max iterations: [safety limit]
Per-iteration tasks: [what to do each loop]

Step 2: Clarify Parameters

Use AskUserQuestion if unclear:

• Max iterations: How many times maximum?
• Success condition: What indicates we can stop early?
• Per-iteration actions: What exactly to do each round?
• Failure handling: What if it never succeeds?

Step 3: Safety Checks

**Safety Validation:**

- [ ] Max iterations defined (no infinite loops)
- [ ] Success condition is measurable
- [ ] Each iteration makes progress
- [ ] Failure exit strategy exists
- [ ] User aware of potential duration

Never proceed without max iterations defined.

Phase 2: Loop Execution

Step 1: Initialize Loop

**Starting Iterative Loop**

Goal: [description]
Max iterations: [N]
Success criteria: [condition]


### Iteration 1 / [N]

Step 2: Execute Iteration

For each iteration:

**Iteration [current] / [max]**

**Actions:**
1. [Action 1]
   → [result/output]
2. [Action 2]
   → [result/output]
3. [Action 3]
   → [result/output]

**Evaluation:**
- Success criteria met? [Yes/No]
- Progress made? [Yes/No]
- Issues found: [list any issues]

**Status:** [Continue/Success/Need intervention]

Step 3: Progress Tracking

Use task plan tool to track iterations:

Iteration Progress:
✓ Iteration 1 - [what was done]
✓ Iteration 2 - [what was done]
⚙️ Iteration 3 - [in progress]
- Iteration 4 - [pending]
- Iteration 5 - [pending]

Phase 3: Exit Conditions

Exit Condition 1: Success

🎉 **Success! Loop complete.**

**Goal achieved:** [description]
**Iterations used:** [N] / [max]

**Final state:**
[description of what was achieved]

**Summary of iterations:**
1. Iteration 1: [what happened]
2. Iteration 2: [what happened]
...
N. Iteration N: [what happened] ✓ Success

Exit Condition 2: Max Iterations Reached

⚠️ **Max iterations reached without full success**

**Iterations completed:** [max]
**Goal:** [description]
**Current state:** [how close we got]

**Progress made:**
- [Improvement 1]
- [Improvement 2]
- [Improvement 3]

**Remaining issues:**
- [Issue 1]
- [Issue 2]

**Options:**
1. Accept current state (substantial progress made)
2. Continue with [N] more iterations
3. Change approach (current method may not work)

What would you like to do?

Exit Condition 3: No Progress Detected

🛑 **Stopping early: No progress detected**

**Iteration:** [N] / [max]
**Reason:** Last [M] iterations showed no improvement

**Analysis:**
This suggests the current approach may be fundamentally flawed.

**Recommendation:**
Rather than continue looping, let's:
1. Analyze why no progress is being made
2. Consider alternative approaches
3. Re-evaluate the goal or success criteria

Shall we pause and reassess?

Common Patterns

Pattern 1: Loop with Testing

User: "Loop around 5 times auditing, enhancing, testing, until it's done"

Implementation:

**Loop Goal:** Code passes all quality gates
**Max Iterations:** 5
**Per-iteration:**
1. Audit code for issues
2. Enhance/fix identified issues
3. Run tests
4. Check if all pass

**Success:** All tests pass + no issues found

Execute:
Iteration 1:
- Audit → Found 8 issues
- Fix → Fixed 8 issues
- Test → 2 tests still failing
- Continue

Iteration 2:
- Audit → Found 2 new issues from fixes
- Fix → Fixed 2 issues
- Test → All tests pass ✓
- Success! Stopping early (2/5 iterations used)

Pattern 2: Performance Optimization Loop

User: "Keep trying optimizations until we hit < 100ms response time"

Implementation:

**Loop Goal:** Response time < 100ms
**Max Iterations:** 10
**Per-iteration:**
1. Measure current performance
2. Identify bottleneck
3. Apply optimization
4. Re-measure

**Success:** Response time < 100ms

Execute:
Iteration 1: 450ms → Cache database queries → 280ms (Continue)
Iteration 2: 280ms → Add index to frequent query → 150ms (Continue)
Iteration 3: 150ms → Implement response compression → 85ms (Success!)

Pattern 3: Retry with Backoff

User: "Try deploying, retry up to 3 times if it fails"

Implementation:

**Loop Goal:** Successful deployment
**Max Iterations:** 3
**Per-iteration:**
1. Attempt deployment
2. Check status
3. If failed, wait before retry

**Success:** Deployment succeeds

Execute:
Iteration 1: Deploy → Failed (API timeout) → Wait 10s
Iteration 2: Deploy → Failed (API timeout) → Wait 20s
Iteration 3: Deploy → Success ✓

Pattern 4: Incremental Refinement

User: "Iterate 4 times improving the error messages based on user feedback"

Implementation:

**Loop Goal:** Error messages meet clarity standard
**Max Iterations:** 4
**Per-iteration:**
1. Review current error messages
2. Identify confusing ones
3. Rewrite for clarity
4. Evaluate against criteria

**Success:** All messages rated 8+/10 for clarity

Execute each iteration with progressive improvement

Integration with Other Skills

With skill-debug

Loop for debugging:
"Keep debugging until all tests pass, max 5 tries"

Each iteration:
- Use skill-debug to investigate failure
- Apply fix
- Re-run tests
- Evaluate

With skill-audit

Loop for comprehensive checking:
"Loop 3 times auditing different aspects"

Iteration 1: Audit security
Iteration 2: Audit performance
Iteration 3: Audit accessibility

With skill-tdd

Loop for TDD cycles:
"Do 5 red-green-refactor cycles"

Each iteration:
- Write failing test (red)
- Make it pass (green)
- Refactor (refactor)
- Evaluate and continue

Best Practices

1. Always Define Max Iterations

Good:

Loop max 5 times trying to fix the issue

Dangerous:

Keep trying until it works
(What if it never works? Infinite loop!)

2. Measurable Success Criteria

Good:

Success: All 15 tests pass AND code coverage > 80%

Poor:

Success: Code looks better
(Too subjective)

3. Make Progress Visible

**Progress Tracking:**

Iteration 1: 5/15 tests passing
Iteration 2: 10/15 tests passing
Iteration 3: 13/15 tests passing
Iteration 4: 15/15 tests passing ✓

4. Early Exit on Success

Don't continue looping if goal is achieved:

**Iteration 2/5:** All tests pass!

Stopping early - goal achieved.
No need to continue to iteration 3.

5. Detect Stalls

Iteration 4: 10/15 tests passing
Iteration 5: 10/15 tests passing
Iteration 6: 10/15 tests passing

⚠️ No progress in 3 iterations - stopping to reassess approach

Red Flags - Don't Do This

Action	Why It's Dangerous
No max iterations	Could loop forever
Vague success criteria	Don't know when to stop
No progress tracking	Can't tell if making progress
Ignoring stalls	Waste time on ineffective approach
Same action each loop	If not working, need different approach

Strategy Rotation

If the strategy-rotation hook fires, immediately change approach. Do not retry the same approach. Explain what you'll do differently before the next attempt. The hook fires after consecutive failures of the same tool — this is a strong signal that the current approach is fundamentally wrong, not just slightly off.

Self-Regulation (MANDATORY)

Every iterative loop MUST track a Self-Regulation Score that accumulates danger signals. This prevents runaway loops where the agent keeps "fixing" things without real progress.

Sliding-Window Stuck Detection

Maintain a mental window of the last 10 iterations (or fewer if less than 10 have run). After each iteration, check for repeated patterns:

Single-state repetition: Did the same outcome/error occur 3+ times consecutively?

• Same test failure, same error message, same files modified → STUCK

Multi-step cycle detection: Is there an A→B→A→B oscillation?

• Iteration N touches file X, N+1 touches file Y, N+2 touches file X again, N+3 touches Y again → CYCLE DETECTED
• Compare the files modified and error messages across iterations, not just success/failure

On first detection: Announce the pattern to the user. Attempt ONE diagnostic retry with explicit acknowledgment: "This pattern has repeated — here's what I'll do differently: [specific change]."

On second detection: HALT immediately. Display the detected cycle and ask the user whether to continue with a completely different approach or stop.

WTF-Likelihood Score

Track a cumulative score starting at 0%. Each event adds to the score.

Default weights (override via ~/.claude-octopus/loop-config.conf):

Event	Score Impact
Revert (git revert, undo, roll back)	+15%
Touching files unrelated to the stated goal	+20%
A fix that requires changing >3 files	+5%
After the 15th fix attempt	+1% per additional fix
All remaining issues are Low severity	+10%

If WTF score exceeds 20%: STOP immediately. Show:

1. The current WTF score and what contributed to it
2. Work completed so far
3. Ask the user: "Continue with a different approach, or stop here?"

Hard cap: 50 iterations regardless of score or progress. No exceptions.

Configurable Weights

At loop start, check for ~/.claude-octopus/loop-config.conf. If it exists, read the key=value pairs and use them instead of defaults. Format:

# Loop Self-Regulation Configuration
WINDOW_SIZE=10
REVERT_PENALTY=15
UNRELATED_FILES_PENALTY=20
LARGE_FIX_PENALTY=5
AFTER_FIX_15_PENALTY=1
ALL_LOW_SEVERITY_PENALTY=10
WTF_THRESHOLD=20
HARD_CAP=50
STUCK_THRESHOLD=3

If the file does not exist, use the defaults shown above. Users can create this file to tune sensitivity for their workflow.

How to Track

You do NOT need external tools for this. Track mentally during the loop:

• After each iteration, mentally note: files touched, outcome, whether a revert happened
• Compare against the sliding window of recent iterations
• Accumulate the WTF score
• Report both the iteration count AND the self-regulation score in each iteration summary:

Iteration 5/20 | Self-regulation: 10% (1 revert, 0 unrelated files)

Interaction with Strategy Rotation

The strategy-rotation hook and self-regulation are complementary:

• Strategy rotation fires on consecutive tool failures (same tool, same error)
• Self-regulation fires on outcome patterns (cycles, reverts, scope creep)
• Both can fire independently. If both fire, HALT — the loop is definitely stuck.

Safety Mechanisms

1. Iteration Limit

MAX_ITERATIONS = user_specified or 10  # Always have a limit
HARD_CAP = 50  # Absolute maximum regardless of user setting

2. Self-Regulation Score

Track WTF score across iterations.
If score > 20%: STOP and ask user.

3. Sliding-Window Detection

Track last 10 iterations.
If repeated pattern detected twice: STOP and ask user.

4. Progress Detection

If last 3 iterations show same result:
  → Stop and ask user

5. Time Limit (for long operations)

If total time > 30 minutes:
  → Checkpoint progress
  → Ask user if should continue

6. User Checkpoints

Every N iterations:
  → Show progress
  → Ask if should continue or adjust approach

Quick Reference

Pattern	Max Iterations	Success Criteria	Early Exit
Test until pass	5-10	All tests pass	Yes
Performance optimization	10-20	Metric < target	Yes
Retry with backoff	3-5	Operation succeeds	Yes
Incremental refinement	3-7	Quality threshold met	Maybe
Comprehensive audit	3-5	All areas covered	No

Metric Verification Mode

When the user specifies a Metric command, switch to mechanical metric verification mode. This replaces subjective evaluation with automated measurement, git-backed experiments, and automatic rollback on regression.

Falls back to standard loop behavior (above) when no metric is specified.

Key Principles

• One change per iteration (atomic) — never combine multiple unrelated changes
• Mechanical verification only — no subjective "looks good"; the metric command decides
• Automatic rollback on regression — git revert HEAD --no-edit if metric worsens
• Simplicity wins — equal metric results + less code = KEEP the simpler version
• Git is memory — every experiment is committed with experiment: prefix before verification
• Guard commands must also pass — even if metric improves, a failing guard reverts the change

Parameters

Parameter	Format	Required	Description
Metric	Metric: <shell command>	Yes (for this mode)	Command whose stdout is a number (the metric value)
Direction	Direction: higher|lower	Yes	Whether higher or lower metric values are better
Guard	Guard: <shell command>	No	Must exit 0 for a change to be kept; run after metric
Iterations	Iterations: N	No	Max iterations (default: unbounded, runs until interrupted)

Experiment Log

All results are logged as JSONL to .claude-octopus/experiments/<YYYY-MM-DD>.jsonl.

Each line is a JSON object:

{"iteration": 1, "timestamp": "2026-03-21T14:30:00Z", "metric": 72.5, "best": 72.5, "status": "kept", "description": "Add index to users table", "commit": "abc1234"}

Fields:

• iteration — iteration number (starting from 1; iteration 0 is baseline)
• timestamp — ISO 8601 timestamp
• metric — measured value from the metric command
• best — best metric value seen so far
• status — "kept" (improvement), "reverted" (regression), or "error" (metric/guard crashed)
• description — one-line summary of what was changed
• commit — short git SHA of the experiment commit (before potential revert)

Execution Contract

You MUST follow this exact sequence for each iteration. No steps may be skipped or reordered.

Iteration 0: Establish Baseline

1. Create the experiment log directory if it does not exist:

   mkdir -p .claude-octopus/experiments
   

2. Check for existing experiment log — if .claude-octopus/experiments/<today>.jsonl exists, read it to determine the current best metric value and iteration count. Resume from the next iteration number.
3. Run the metric command and capture the output number. This is the baseline.
4. Log the baseline:

   {"iteration": 0, "timestamp": "...", "metric": <baseline>, "best": <baseline>, "status": "baseline", "description": "Baseline measurement", "commit": "<current HEAD short SHA>"}
   

5. Report the baseline value to the user.

Each Subsequent Iteration (1..N)

Step 1: Review state. Read the experiment log (.claude-octopus/experiments/<today>.jsonl), review git history (git log --oneline -10), and identify what has been tried, what worked, and what failed.

Step 2: Pick the next change. Based on what worked/failed/is untried, decide on ONE focused change. Do NOT combine multiple unrelated changes.

Step 3: Make the change. Implement exactly one atomic change.

Step 4: Git commit BEFORE verification. Commit with the experiment: prefix:

git add -A && git commit -m "experiment: <one-line description of the change>"

This ensures every experiment is recorded in git history regardless of outcome.

Step 5: Run mechanical verification. Execute the metric command and capture the numeric result.

Step 6: Evaluate and act.

• If metric improved (higher when Direction=higher, lower when Direction=lower):

  • If a Guard command is specified, run it now.
    • If guard passes (exit 0) → KEEP the commit. Update best metric.
    • If guard fails (exit non-zero) → REVERT: git revert HEAD --no-edit. Log status as "reverted".
  • If no guard → KEEP the commit. Update best metric.

• If metric stayed the same:

  • Check if the change reduces code complexity or size. If simpler → KEEP (simplicity wins).
  • Otherwise → REVERT: git revert HEAD --no-edit. Log status as "reverted".

• If metric worsened:

  • REVERT: git revert HEAD --no-edit. Log status as "reverted".

• If metric command crashed (non-zero exit, no numeric output):

  • Attempt a quick fix (one try only). If fix works, re-measure.
  • If still broken → REVERT: git revert HEAD --no-edit. Log status as "error".

Step 7: Log the result. Append a JSONL entry to .claude-octopus/experiments/<today>.jsonl.

Step 8: Report iteration summary. Display:

Iteration N: <description>
  Metric: <value> (best: <best>) — <kept|reverted|error>

Step 9: Repeat — go to Step 1 of the next iteration, unless:

• Iterations limit reached → stop and report final summary
• User interrupts → stop and report final summary

Resume Behavior

If an experiment log already exists for today:

1. Read the log to determine the last iteration number and current best metric value
2. Resume from the next iteration number
3. Use the recorded best value as the comparison baseline
4. This allows stopping and resuming experiments across sessions

Final Summary

When the loop completes (iterations exhausted or user stops), report:

Experiment Complete
  Iterations: N
  Baseline: <initial metric>
  Final best: <best metric>
  Improvement: <delta> (<percentage>%)
  Kept: K changes, Reverted: R changes, Errors: E

Example

/octo:loop Metric: npm test -- --coverage | grep 'All files' | awk '{print $10}' Direction: higher Guard: npm test Iterations: 20

This will:

1. Measure baseline code coverage percentage
2. Each iteration: make one change, commit as experiment: ..., measure coverage
3. If coverage improves AND npm test passes → keep
4. If coverage drops OR tests fail → git revert HEAD --no-edit
5. After 20 iterations, report total improvement

The Bottom Line

Iterative loop → Clear goal + Max iterations + Progress tracking + Exit strategy
Otherwise → Infinite loops + Wasted effort + Unclear when done

Define the goal. Set the limit. Track progress. Know when to stop.