Web Application Testing
anthropics/skills
Tests local web applications with Python Playwright scripts, checking frontend behavior, capturing screenshots and reading browser console logs.
Run tasks in a loop until goals are met — use for iterative refinement, polling, or convergence
$ npx skills add nyldn/claude-octopus --skill skill-iterative-loop -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install nyldn/claude-octopus skill-iterative-loop --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ 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-srcUse ~/.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/
Install the "skill-iterative-loop" agent skill from https://github.com/nyldn/claude-octopus/tree/main/skills/skill-iterative-loop into .claude/skills/skill-iterative-loop/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "skill-iterative-loop", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/nyldn/claude-octopus/tree/main/skills/skill-iterative-loopType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add nyldn/claude-octopus --skill skill-iterative-loop -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install nyldn/claude-octopus skill-iterative-loop --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/nyldn/claude-octopus.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/skill-iterative-loop .agents/skills/skill-iterative-loop && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "skill-iterative-loop" agent skill from https://github.com/nyldn/claude-octopus/tree/main/skills/skill-iterative-loop into .agents/skills/skill-iterative-loop/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "skill-iterative-loop", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add nyldn/claude-octopus --skill skill-iterative-loop -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install nyldn/claude-octopus skill-iterative-loop --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/nyldn/claude-octopus.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/skill-iterative-loop .cursor/skills/skill-iterative-loop && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "skill-iterative-loop" agent skill from https://github.com/nyldn/claude-octopus/tree/main/skills/skill-iterative-loop into .cursor/skills/skill-iterative-loop/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "skill-iterative-loop", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/nyldn/claude-octopus.git --path skills/skill-iterative-loop--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add nyldn/claude-octopus --skill skill-iterative-loop -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install nyldn/claude-octopus skill-iterative-loop --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/nyldn/claude-octopus.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/skill-iterative-loop .gemini/skills/skill-iterative-loop && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "skill-iterative-loop" agent skill from https://github.com/nyldn/claude-octopus/tree/main/skills/skill-iterative-loop into .gemini/skills/skill-iterative-loop/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "skill-iterative-loop", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install nyldn/claude-octopus skill-iterative-loopInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add nyldn/claude-octopus --skill skill-iterative-loop -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/nyldn/claude-octopus.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/skill-iterative-loop .github/skills/skill-iterative-loop && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "skill-iterative-loop" agent skill from https://github.com/nyldn/claude-octopus/tree/main/skills/skill-iterative-loop into .github/skills/skill-iterative-loop/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "skill-iterative-loop", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add nyldn/claude-octopus --skill skill-iterative-loop -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install nyldn/claude-octopus skill-iterative-loop --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/nyldn/claude-octopus.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/skill-iterative-loop .opencode/skills/skill-iterative-loop && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "skill-iterative-loop" agent skill from https://github.com/nyldn/claude-octopus/tree/main/skills/skill-iterative-loop into .opencode/skills/skill-iterative-loop/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "skill-iterative-loop", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
skill-iterative-loopRun 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. 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.
12 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 18b66ca. It shows what the files ask for, not the result of running them.
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.
Shell commands in SKILL.md call:
gitnpmFrom the folder's file list and the shell code blocks in SKILL.md.
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.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
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.
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.
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.
The full file from nyldn/claude-octopus at commit 18b66ca, republished under its MIT licence (© nyldn). 1,624 words, ~4,917 tokens.
.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.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, seeskills/blocks/codex-host-adapter.md.
Systematic iterative execution with clear goals, exit conditions, and progress tracking.
Core principle: Define goal → Set max iterations → Execute → Evaluate → Loop or complete.
Use this skill when user wants to:
Do NOT use for:
**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]Use AskUserQuestion if unclear:
**Safety Validation:**
- [ ] Max iterations defined (no infinite loops)
- [ ] Success condition is measurable
- [ ] Each iteration makes progress
- [ ] Failure exit strategy exists
- [ ] User aware of potential durationNever proceed without max iterations defined.
**Starting Iterative Loop**
Goal: [description]
Max iterations: [N]
Success criteria: [condition]
### Iteration 1 / [N]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]
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]🎉 **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⚠️ **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?🛑 **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?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)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!)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 ✓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 improvementLoop for debugging:
"Keep debugging until all tests pass, max 5 tries"
Each iteration:
- Use skill-debug to investigate failure
- Apply fix
- Re-run tests
- EvaluateLoop for comprehensive checking:
"Loop 3 times auditing different aspects"
Iteration 1: Audit security
Iteration 2: Audit performance
Iteration 3: Audit accessibilityLoop for TDD cycles:
"Do 5 red-green-refactor cycles"
Each iteration:
- Write failing test (red)
- Make it pass (green)
- Refactor (refactor)
- Evaluate and continueGood:
Loop max 5 times trying to fix the issueDangerous:
Keep trying until it works
(What if it never works? Infinite loop!)Good:
Success: All 15 tests pass AND code coverage > 80%Poor:
Success: Code looks better
(Too subjective)**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 ✓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.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| 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 |
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.
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.
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?
Multi-step cycle detection: Is there an A→B→A→B oscillation?
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.
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:
Hard cap: 50 iterations regardless of score or progress. No exceptions.
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=3If the file does not exist, use the defaults shown above. Users can create this file to tune sensitivity for their workflow.
You do NOT need external tools for this. Track mentally during the loop:
Iteration 5/20 | Self-regulation: 10% (1 revert, 0 unrelated files)The strategy-rotation hook and self-regulation are complementary:
MAX_ITERATIONS = user_specified or 10 # Always have a limit
HARD_CAP = 50 # Absolute maximum regardless of user settingTrack WTF score across iterations.
If score > 20%: STOP and ask user.Track last 10 iterations.
If repeated pattern detected twice: STOP and ask user.If last 3 iterations show same result:
→ Stop and ask userIf total time > 30 minutes:
→ Checkpoint progress
→ Ask user if should continueEvery N iterations:
→ Show progress
→ Ask if should continue or adjust approach| 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 |
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.
git revert HEAD --no-edit if metric worsensexperiment: prefix before verification| 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) |
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 timestampmetric — measured value from the metric commandbest — best metric value seen so farstatus — "kept" (improvement), "reverted" (regression), or "error" (metric/guard crashed)description — one-line summary of what was changedcommit — short git SHA of the experiment commit (before potential revert)You MUST follow this exact sequence for each iteration. No steps may be skipped or reordered.
mkdir -p .claude-octopus/experiments.claude-octopus/experiments/<today>.jsonl exists, read it to determine the current best metric value and iteration count. Resume from the next iteration number.{"iteration": 0, "timestamp": "...", "metric": <baseline>, "best": <baseline>, "status": "baseline", "description": "Baseline measurement", "commit": "<current HEAD short SHA>"}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):
git revert HEAD --no-edit. Log status as "reverted".If metric stayed the same:
git revert HEAD --no-edit. Log status as "reverted".If metric worsened:
git revert HEAD --no-edit. Log status as "reverted".If metric command crashed (non-zero exit, no numeric output):
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:
If an experiment log already exists for today:
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/octo:loop Metric: npm test -- --coverage | grep 'All files' | awk '{print $(10)}' Direction: higher Guard: npm test Iterations: 20This will:
experiment: ..., measure coveragenpm test passes → keepgit revert HEAD --no-editIterative loop → Clear goal + Max iterations + Progress tracking + Exit strategy
Otherwise → Infinite loops + Wasted effort + Unclear when doneDefine 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
SKILL.md and 1 other file in skills/skill-iterative-loop of nyldn/claude-octopus.
Open the folder on GitHubat commit 18b66ca
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.
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 | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Skill Iterative Loop this skillnyldn/claude-octopus | 4.2k | 1 repos | ~4.9k | Automated safety check: Pass | MIT | |
| Web Application Testinganthropics/skills | 180k | 51 repos | ~966 | Automated safety check: Pass | Apache-2.0 | |
| Diagnosing Bugsfossasia/eventyay-interpretation | 1.6k | 31 repos | ~2.1k | Automated safety check: Pass | Apache-2.0 | |
| TDDfossasia/eventyay-interpretation | 1.6k | 28 repos | ~1.1k | Automated safety check: Pass | Apache-2.0 | |
| TDD WorkflowhellangleZ/burn-in-cceverywhere-ralph | 112 | 11 repos | ~2.4k | Automated safety check: Pass | None | |
| TDDsanity-io/sanity | 6.4k | 20 repos | ~1k | Automated safety check: Pass | MIT |
anthropics/skills
Tests local web applications with Python Playwright scripts, checking frontend behavior, capturing screenshots and reading browser console logs.
fossasia/eventyay-interpretation
Diagnosis loop for hard bugs and performance regressions. An agent skill from fossasia/eventyay-interpretation.
fossasia/eventyay-interpretation
Test-driven development. An agent skill from fossasia/eventyay-interpretation.
hellangleZ/burn-in-cceverywhere-ralph
A skill your agent uses when writing new features, fixing bugs, or refactoring code.
sanity-io/sanity
Test-driven development with red-green-refactor loop. An agent skill from sanity-io/sanity.
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…
nyldn/claude-octopus
Quick execution for ad-hoc tasks without full workflow overhead — use for small, self-contained requests
nyldn/claude-octopus
Thorough research across multiple sources — use for complex topics needing broad synthesis
nyldn/claude-octopus
OWASP compliance, vulnerability scanning, and adversarial red team testing — use for security reviews
nyldn/claude-octopus
Audit codebases for quality, consistency, and broken patterns — use for pre-release or tech debt review
nyldn/claude-octopus
Extract patterns and anatomy from URLs — use to reverse-engineer content strategies from live pages
nyldn/claude-octopus
Auto-detect work context (Dev vs Knowledge) — use to tailor workflows based on current task type
Categories
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.
Skill Iterative Loop fits situations like: iterative refinement.
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.
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.
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.
Going by SKILL.md and its folder, Skill Iterative Loop needs the command-line tools its instructions call (git and npm).
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.
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.
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.
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.
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.
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.