Agent skill

Skill Iterative Loop

by nyldn in nyldn/claude-octopus

Run tasks in a loop until goals are met — use for iterative refinement, polling, or convergence

MITAuto-check passedTesting & QA

Install Skill Iterative Loop

skills CLI
$ npx skills add nyldn/claude-octopus --skill skill-iterative-loop -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install nyldn/claude-octopus skill-iterative-loop --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/nyldn/claude-octopus.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/skill-iterative-loop .claude/skills/skill-iterative-loop && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
skill-iterative-loop
GitHub stars
4.2k
Used in
1 other repo
Token cost
~4.9k tokens
SKILL.md length
1,624 words
Files
2
Skills in repo
62
Repo updated
First seen
Licence
MIT

At a glance

Run tasks in a loop until goals are met — use for iterative refinement, polling, or convergence

  • Works in 12 steps: Loop Setup → Loop Execution → Exit Conditions → …
  • Iterative refinement
  • SKILL.md covers Overview, When to Use, The Process and Common Patterns, plus 5 more sections
  • Calls git and npm

What it does

Skill Iterative Loop is an agent skill from nyldn/claude-octopus. Run tasks in a loop until goals are met — use for iterative refinement, polling, or convergence

Its SKILL.md is about 4.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files (for example `agents/openai.yaml`).

It sits in Testing & QA. The repository describes itself as: Run multiple AI models against the same research, design, or coding task. Surface disagreements before you ship. The licence is MIT.

When your agent uses it

  • Iterative refinement

Example prompts

  • “/skill-iterative-loop”

Workflow steps

12 steps, taken from the step headings in SKILL.md.

  1. Loop Setup
  2. Loop Execution
  3. Exit Conditions
  4. Always Define Max Iterations
  5. Measurable Success Criteria
  6. Make Progress Visible
  7. Early Exit on Success
  8. Detect Stalls
  9. Iteration Limit
  10. Self-Regulation Score
  11. Sliding-Window Detection
  12. Progress Detection

What it can do on your machine

Read from SKILL.md and the folder at commit 18b66ca. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • git
    • npm

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use git and npm, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Skill Iterative Loop loads about 4.9k tokens when it runs. Until then it costs about 29 tokens; SKILL.md has 1,624 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~29
When it runs · the whole SKILL.md, loaded when a task matches
~4.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from nyldn/claude-octopus at commit 18b66ca, republished under its MIT licence (© nyldn). 1,624 words, ~4,917 tokens.

Download SKILL.mdSave it as .claude/skills/skill-iterative-loop/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
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
markdown
**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
markdown
**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
markdown
**Starting Iterative Loop**

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


### Iteration 1 / [N]
Step 2: Execute Iteration

For each iteration:

markdown
**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
markdown
🎉 **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
markdown
⚠️ **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
markdown
🛑 **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

ActionWhy It's Dangerous
No max iterationsCould loop forever
Vague success criteriaDon't know when to stop
No progress trackingCan't tell if making progress
Ignoring stallsWaste time on ineffective approach
Same action each loopIf 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):

EventScore 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:

conf
# 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
python
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

PatternMax IterationsSuccess CriteriaEarly Exit
Test until pass5-10All tests passYes
Performance optimization10-20Metric < targetYes
Retry with backoff3-5Operation succeedsYes
Incremental refinement3-7Quality threshold metMaybe
Comprehensive audit3-5All areas coveredNo

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
Show full SKILL.md (641 more words)Show less
Parameters
ParameterFormatRequiredDescription
MetricMetric: <shell command>Yes (for this mode)Command whose stdout is a number (the metric value)
DirectionDirection: higher|lowerYesWhether higher or lower metric values are better
GuardGuard: <shell command>NoMust exit 0 for a change to be kept; run after metric
IterationsIterations: NNoMax 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:

json
{"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:
    bash
    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:
    json
    {"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:

bash
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.

© nyldn, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in skills/skill-iterative-loop of nyldn/claude-octopus.

  • SKILL.md
  • agents/openai.yaml

Open the folder on GitHubat commit 18b66ca

Used in 1 other repository

We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in nyldn/claude-octopus, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Skill Iterative Loop next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Skill Iterative Loop compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Skill Iterative Loop this skillnyldn/claude-octopus4.2k1 repos~4.9kAutomated safety check: PassMIT
Web Application Testinganthropics/skills180k51 repos~966Automated safety check: PassApache-2.0
Diagnosing Bugsfossasia/eventyay-interpretation1.6k31 repos~2.1kAutomated safety check: PassApache-2.0
TDDfossasia/eventyay-interpretation1.6k28 repos~1.1kAutomated safety check: PassApache-2.0
TDD WorkflowhellangleZ/burn-in-cceverywhere-ralph11211 repos~2.4kAutomated safety check: PassNone
TDDsanity-io/sanity6.4k20 repos~1kAutomated safety check: PassMIT

Similar skills

  • Web Application Testing

    anthropics/skills

    Official

    Tests local web applications with Python Playwright scripts, checking frontend behavior, capturing screenshots and reading browser console logs.

    180k GitHub starsUsed in 51 repos~966 tokens
    Testing & QAAuto-check passed
  • Diagnosing Bugs

    fossasia/eventyay-interpretation

    Diagnosis loop for hard bugs and performance regressions. An agent skill from fossasia/eventyay-interpretation.

    1.6k GitHub starsUsed in 31 repos~2.1k tokens
    Testing & QAAuto-check passed
  • TDD

    fossasia/eventyay-interpretation

    Test-driven development. An agent skill from fossasia/eventyay-interpretation.

    1.6k GitHub starsUsed in 28 repos~1.1k tokens
    Testing & QAAuto-check passed
  • TDD Workflow

    hellangleZ/burn-in-cceverywhere-ralph

    A skill your agent uses when writing new features, fixing bugs, or refactoring code.

    112 GitHub starsUsed in 11 repos~2.4k tokens
    Testing & QAAuto-check passed
  • TDD

    sanity-io/sanity

    Official

    Test-driven development with red-green-refactor loop. An agent skill from sanity-io/sanity.

    6.4k GitHub starsUsed in 20 repos~1k tokens
    Testing & QAAuto-check passed
  • Context Driven Development

    Ibrahim-3d/orchestrator-supaconductor

    A skill your agent uses when working with Conductor's context-driven development methodology, managing project context artifacts, or understanding the relationship between product.md, tech-stack.md…

    380 GitHub starsUsed in 8 repos~2.9k tokens
    Testing & QAAuto-check passed

More from nyldn/claude-octopus

All 62 skills in this repo
  • Octopus Quick

    nyldn/claude-octopus

    Quick execution for ad-hoc tasks without full workflow overhead — use for small, self-contained requests

    4.2k GitHub starsUsed in 1 repo~2.2k tokens
    Auto-check passed
  • Octopus Research

    nyldn/claude-octopus

    Thorough research across multiple sources — use for complex topics needing broad synthesis

    4.2k GitHub starsUsed in 1 repo~1.9k tokens
    Auto-check passed
  • Octopus Security Audit

    nyldn/claude-octopus

    OWASP compliance, vulnerability scanning, and adversarial red team testing — use for security reviews

    4.2k GitHub starsUsed in 1 repo~2.3k tokens
    Auto-check passed
  • Skill Audit

    nyldn/claude-octopus

    Audit codebases for quality, consistency, and broken patterns — use for pre-release or tech debt review

    4.2k GitHub starsUsed in 1 repo~3.2k tokens
    Auto-check passed
  • Skill Content Pipeline

    nyldn/claude-octopus

    Extract patterns and anatomy from URLs — use to reverse-engineer content strategies from live pages

    4.2k GitHub starsUsed in 1 repo~3.9k tokens
    Auto-check passed
  • Skill Context Detection

    nyldn/claude-octopus

    Auto-detect work context (Dev vs Knowledge) — use to tailor workflows based on current task type

    4.2k GitHub starsUsed in 1 repo~2.6k tokens
    Auto-check passed

Categories

Questions about Skill Iterative Loop

What does Skill Iterative Loop do?

Run tasks in a loop until goals are met — use for iterative refinement, polling, or convergence. Skill Iterative Loop is an agent skill from nyldn/claude-octopus.

When should I use Skill Iterative Loop?

Skill Iterative Loop fits situations like: iterative refinement.

How do I install Skill Iterative Loop in Claude Code?

Run `npx skills add nyldn/claude-octopus --skill skill-iterative-loop -a claude-code`. Or copy the skill folder (skills/skill-iterative-loop in nyldn/claude-octopus) into .claude/skills/skill-iterative-loop in your project. Claude Code loads it when a task matches its description.

How do I install Skill Iterative Loop in Codex?

Run `npx skills add nyldn/claude-octopus --skill skill-iterative-loop -a codex`. Or copy the skill folder (skills/skill-iterative-loop in nyldn/claude-octopus) into .agents/skills/skill-iterative-loop in your project. Codex loads it when a task matches its description.

Can I use Skill Iterative Loop in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add nyldn/claude-octopus --skill skill-iterative-loop -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/skill-iterative-loop, .gemini/skills/skill-iterative-loop, .github/skills/skill-iterative-loop and .opencode/skills/skill-iterative-loop in your project.

What does Skill Iterative Loop need to run?

Going by SKILL.md and its folder, Skill Iterative Loop needs the command-line tools its instructions call (git and npm).

Does Skill Iterative Loop access the network?

SKILL.md contains no URLs. Its commands use git and npm, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Skill Iterative Loop safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Skill Iterative Loop use?

Skill Iterative Loop is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Skill Iterative Loop use?

About 4.9k tokens (SKILL.md is roughly 20k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Skill Iterative Loop?

Skills that share tags, products or a category with Skill Iterative Loop: Web Application Testing (anthropics/skills, 180k stars), Diagnosing Bugs (fossasia/eventyay-interpretation, 1.6k stars), TDD (fossasia/eventyay-interpretation, 1.6k stars) and TDD Workflow (hellangleZ/burn-in-cceverywhere-ralph, 112 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Skill Iterative Loop?

nyldn (a GitHub user) maintains it in nyldn/claude-octopus, which has 4,173 GitHub stars. The repository holds 62 skills in this directory. The repository was last updated on October 7, 2026.

Source: nyldn/claude-octopus on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.