/skill-iterative-loop
Run tasks in a loop until goals are met — use for iterative refinement, polling, or convergence
Claude Code
$ npx -y skills add nyldn/claude-octopus --skill skill-iterative-loop --agent claude-code
Codex
$ npx skills add nyldn/claude-octopus --skill skill-iterative-loop -a codex -y
Cursor
$ npx skills add nyldn/claude-octopus --skill skill-iterative-loop -a cursor -y
Gemini CLI
$ npx skills add nyldn/claude-octopus --skill skill-iterative-loop -a gemini-cli -y
GitHub Copilot
$ npx skills add nyldn/claude-octopus --skill skill-iterative-loop -a github-copilot -y
OpenCode
$ npx skills add nyldn/claude-octopus --skill skill-iterative-loop -a opencode -y
Windsurf
$ npx skills add nyldn/claude-octopus --skill skill-iterative-loop -a windsurf -y
SKILL.md
skill-iterative-loop.SKILL.mdname: skill-iterative-loop
description: "Run tasks in a loop until goals are met — use for iterative refinement, polling, or convergence"
disable-model-invocation: true
> **Host: Codex CLI** — This skill was designed for Claude Code and adapted for Codex. > Cross-reference commands use installed skill names in Codex rather than `/octo:*` slash commands. > Use the active Codex shell and subagent tools. Do not claim a provider, model, or host subagent is available until the current session exposes it. > For host tool equivalents, see `skills/blocks/codex-host-adapter.md`.
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: "Iter
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name: skill-iterative-loop description: "Run tasks in a loop until goals are met — use for iterative refinement, polling, or convergence" disable-model-invocation: true
> **Host: Codex CLI** — This skill was designed for Claude Code and adapted for Codex. > Cross-reference commands use installed skill names in Codex rather than `/octo:*` slash commands. > Use the active Codex shell and subagent tools. Do not claim a provider, model, or host subagent is available until the current session exposes it. > For host tool equivalents, see `skills/blocks/codex-host-adapter.md`.
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: "Iter
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Questions about skill-iterative-loop.
- Which coding agents can use skill-iterative-loop?
- skill-iterative-loop installs in Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode and Windsurf with the skills CLI, using each agent's flag shown on this page. Flowy's auto-invocation, which fires a skill when your prompt matches, runs in Claude Code only.

