Skill Writing
millionco/expect
Write and improve agent skills (SKILL.md files). An agent skill from millionco/expect.
Runs continuous AI iteration loops that repeat build-test-fix cycles until success criteria are met.
$ npx skills add proffesor-for-testing/agentic-qe --skill iterative-loop -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install proffesor-for-testing/agentic-qe 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/proffesor-for-testing/agentic-qe.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/iterative-loop .claude/skills/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 "iterative-loop" agent skill from https://github.com/proffesor-for-testing/agentic-qe/tree/main/.claude/skills/iterative-loop into .claude/skills/iterative-loop/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "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/proffesor-for-testing/agentic-qe/tree/main/.claude/skills/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 proffesor-for-testing/agentic-qe --skill iterative-loop -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install proffesor-for-testing/agentic-qe iterative-loop --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/proffesor-for-testing/agentic-qe.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.claude/skills/iterative-loop .agents/skills/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 "iterative-loop" agent skill from https://github.com/proffesor-for-testing/agentic-qe/tree/main/.claude/skills/iterative-loop into .agents/skills/iterative-loop/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "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 proffesor-for-testing/agentic-qe --skill iterative-loop -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install proffesor-for-testing/agentic-qe iterative-loop --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/proffesor-for-testing/agentic-qe.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.claude/skills/iterative-loop .cursor/skills/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 "iterative-loop" agent skill from https://github.com/proffesor-for-testing/agentic-qe/tree/main/.claude/skills/iterative-loop into .cursor/skills/iterative-loop/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "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/proffesor-for-testing/agentic-qe.git --path .claude/skills/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 proffesor-for-testing/agentic-qe --skill iterative-loop -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install proffesor-for-testing/agentic-qe iterative-loop --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/proffesor-for-testing/agentic-qe.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.claude/skills/iterative-loop .gemini/skills/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 "iterative-loop" agent skill from https://github.com/proffesor-for-testing/agentic-qe/tree/main/.claude/skills/iterative-loop into .gemini/skills/iterative-loop/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "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 proffesor-for-testing/agentic-qe 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 proffesor-for-testing/agentic-qe --skill iterative-loop -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/proffesor-for-testing/agentic-qe.git skills-src && mkdir -p .github/skills && cp -r skills-src/.claude/skills/iterative-loop .github/skills/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 "iterative-loop" agent skill from https://github.com/proffesor-for-testing/agentic-qe/tree/main/.claude/skills/iterative-loop into .github/skills/iterative-loop/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "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 proffesor-for-testing/agentic-qe --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 proffesor-for-testing/agentic-qe iterative-loop --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/proffesor-for-testing/agentic-qe.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.claude/skills/iterative-loop .opencode/skills/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 "iterative-loop" agent skill from https://github.com/proffesor-for-testing/agentic-qe/tree/main/.claude/skills/iterative-loop into .opencode/skills/iterative-loop/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "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.
iterative-loopRuns continuous AI iteration loops that repeat build-test-fix cycles until success criteria are met.
Iterative Loop is an agent skill from proffesor-for-testing/agentic-qe. Runs continuous AI iteration loops that repeat build-test-fix cycles until success criteria are met. Use when building features requiring test-driven refinement, implementing tasks with clear pass/fail criteria, or automating iterative improvement workflows.
Its SKILL.md is about 2.5k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Development, covering Test-driven development. The repository describes itself as: Agentic QE Fleet is an open-source AI-powered QA/QE platform designed for use with Coding Agents (works best with Claude Code) featuring specialized agents and skills to support… The licence is MIT.
4 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 829d030. 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:
npxFrom the folder's file list and the shell code blocks in SKILL.md.
Links to these hosts (documentation or services it may open):
github.comghuntley.comFrom 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.
Iterative Loop loads about 2.5k tokens when it runs. Until then it costs about 68 tokens; SKILL.md has 470 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 proffesor-for-testing/agentic-qe at commit 829d030, republished under its MIT licence (© proffesor-for-testing). 470 words, ~2,470 tokens.
.claude/skills/iterative-loop/SKILL.md (or your agent's skills folder).The Iterative Loop skill implements continuous AI-driven development loops that persist until completion criteria are met. Inspired by the Ralph Wiggum technique, this approach enables autonomous, self-correcting development cycles where the AI sees its previous work in files and git history, iteratively improving until success.
# Define task with clear completion criteria
TASK="Implement user authentication with JWT.
Success criteria:
- All unit tests pass
- Integration tests pass
- No TypeScript errors
- Security audit passes
Output <promise>COMPLETE</promise> when all criteria met."
# Execute iterative loop (conceptual)
while ! task_complete; do
claude_execute "$TASK"
check_completion_criteria
done# Using claude-flow hooks for iterative task
npx --no-install ruflo hooks pre-task --description "Implement auth with iteration" --taskId "auth-impl"
# Store iteration state in memory
npx --no-install ruflo memory store \
--key "iteration-auth" \
--value '{"iteration": 1, "maxIterations": 20, "criteria": "all tests pass"}' \
--namespace iterationsEssential: Every iterative task MUST have objectively measurable completion criteria.
Good Criteria Examples:
✅ All unit tests pass (npm test returns exit code 0)
✅ Coverage > 80% (coverage report shows 80%+)
✅ No TypeScript errors (tsc --noEmit returns 0)
✅ Linting passes (eslint returns 0)
✅ Performance < 100ms (benchmark shows < 100ms)Bad Criteria Examples:
❌ "Code looks good" (subjective)
❌ "Works properly" (undefined)
❌ "Well-structured" (no measurable check)Break complex tasks into incremental phases:
## Task: Implement User Authentication
### Phase 1: Data Layer
- Create User model with Prisma schema
- Write migration
- Run tests: `npm test -- --grep "User model"`
- Criteria: Model tests pass
### Phase 2: Service Layer
- Implement AuthService with JWT
- Add token generation/validation
- Run tests: `npm test -- --grep "AuthService"`
- Criteria: Service tests pass
### Phase 3: API Layer
- Create /auth/login endpoint
- Create /auth/register endpoint
- Run tests: `npm test -- --grep "auth API"`
- Criteria: API tests pass
### Phase 4: Integration
- End-to-end authentication flow
- Run tests: `npm test`
- Criteria: ALL tests pass
Output <promise>AUTH_COMPLETE</promise> when Phase 4 passes.Always include escape conditions:
## Safety Rules
1. **Max Iterations**: Stop after 20 attempts
2. **Stuck Detection**: After 5 iterations without progress:
- Document what's blocking
- List attempted approaches
- Suggest alternative strategies
3. **Critical Errors**: Stop immediately if:
- Database corruption detected
- Security vulnerability introduced
- Breaking changes to existing featuresEach iteration should:
# Iteration pattern
1. Read previous state (files, git log)
2. Identify remaining work
3. Implement specific change
4. Run verification suite
5. If all pass -> output completion promise
6. If failures -> analyze and continue iteration## TDD Iteration Task
1. Write failing test for [feature]
2. Implement minimal code to pass test
3. Run `npm test`
4. If test fails -> debug and fix implementation
5. If test passes -> check if more tests needed
6. Repeat until all acceptance tests pass
7. Refactor if needed
8. Output <promise>TDD_COMPLETE</promise>## Bug Fix Task
1. Write failing test that reproduces bug
2. Implement fix
3. Run test suite
4. If reproduction test fails -> analyze why fix didn't work
5. If other tests fail -> fix regressions
6. If all tests pass -> output <promise>BUG_FIXED</promise>
Max iterations: 10
After 5 iterations without fix:
- Document root cause analysis
- Suggest alternative approaches## Coverage Improvement Task
Target: 80% line coverage
1. Run coverage analysis
2. Identify uncovered code paths
3. Write test for highest-impact uncovered path
4. Run tests with coverage
5. If coverage >= 80% -> output <promise>COVERAGE_ACHIEVED</promise>
6. If coverage < 80% -> continue iteration
Max iterations: 30
Progress check: If coverage doesn't improve for 3 iterations -> analyze blockers## Performance Optimization Task
Target: Response time < 100ms
1. Run performance benchmark
2. Identify slowest operation
3. Implement optimization
4. Run benchmark again
5. If target met -> output <promise>PERF_TARGET_MET</promise>
6. If not improved -> try different approach
Max iterations: 15
Record metrics each iteration for trend analysis# Store iteration state
npx --no-install ruflo memory store \
--key "current-iteration" \
--value '{"task": "auth", "iteration": 5, "lastResult": "2 tests failing"}' \
--namespace iterations
# Search for similar past iterations
npx --no-install ruflo memory search \
--query "auth implementation" \
--namespace iterations
# Learn from successful completions
npx --no-install ruflo hooks post-task \
--taskId "auth-impl" \
--success true \
--quality 0.9For complex tasks, use multiple agents iterating in parallel:
# Initialize swarm for parallel iteration
npx --no-install ruflo swarm init --topology mesh --max-agents 5
# Spawn specialized iterators
Task("Iterate on unit tests", "Fix failing unit tests until all pass", "tester")
Task("Iterate on integration", "Fix integration tests until all pass", "tester")
Task("Iterate on performance", "Optimize until benchmarks pass", "performance-engineer")Include:
Example Well-Structured Prompt:
## Task: Implement Feature X
### Success Criteria (ALL must pass):
1. `npm test` exits with code 0
2. `npm run lint` exits with code 0
3. `npm run typecheck` exits with code 0
4. No console.log statements in production code
### Phases:
1. Write failing tests
2. Implement feature
3. Fix any failures
4. Clean up and refactor
### Safety:
- Max iterations: 20
- After 10 iterations: summarize blockers
- Stop if security issues detected
### Completion:
When ALL success criteria pass, output:
<promise>FEATURE_X_COMPLETE</promise>Ideal for:
Not ideal for:
Symptoms: Same errors repeat without improvement
Solutions:
Symptoms: Loop ends but task not actually complete
Solutions:
Symptoms: Previously passing tests fail after new changes
Solutions:
Origin: Based on Ralph Wiggum plugin from claude-code repository (anthropics/claude-code) Adapted for: Agentic QE v3 with Claude Flow integration
© proffesor-for-testing, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Just SKILL.md in .claude/skills/iterative-loop of proffesor-for-testing/agentic-qe.
Open the folder on GitHubat commit 829d030
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 |
|---|---|---|---|---|---|---|
| Iterative Loop this skillproffesor-for-testing/agentic-qe | 494 | — | ~2.5k | Automated safety check: Pass | MIT | |
| Skill Writingmillionco/expect | 3.6k | — | ~1.8k | Automated safety check: Pass | Custom licence | |
| Go Rigmudrii/openclaw-dashboard | 458 | — | ~2.8k | Automated safety check: Pass | MIT | |
| Go Development Guidelinesjumppad-labs/jumppad | 263 | — | ~1.5k | Automated safety check: Pass | MPL-2.0 | |
| Work Issuejoesaby/astro-mermaid | 123 | — | ~891 | Automated safety check: Pass | MIT | |
| Go Rigmudrii/openclaw-dashboard | 458 | — | ~682 | Automated safety check: Pass | MIT |
millionco/expect
Write and improve agent skills (SKILL.md files). An agent skill from millionco/expect.
mudrii/openclaw-dashboard
A skill your agent uses when building, reviewing, or refactoring Go code that must follow strict design discipline — ATDD/TDD workflow, explicit dependency injection, package-boundary discipline…
jumppad-labs/jumppad
Sets idiomatic Go conventions with a test-first workflow, using testify/require for assertions and mockery for mocks, for new features, packages and refactors.
joesaby/astro-mermaid
End-to-end workflow for resolving a GitHub issue in astro-mermaid — triages complexity, then runs brainstorm → TDD → implement → docs/spec → code review at the right depth.
mudrii/openclaw-dashboard
A skill your agent uses when building, reviewing, or refactoring Go code in this repository.
runceel/ReactiveProperty
ReactiveProperty repository development policy. An agent skill from runceel/ReactiveProperty.
proffesor-for-testing/agentic-qe
Consumer-driven contract testing for microservices using Pact, schema validation, API versioning, and backward compatibility testing.
proffesor-for-testing/agentic-qe
Test quality validation through mutation testing, assessing test suite effectiveness by introducing code mutations and measuring kill rate.
proffesor-for-testing/agentic-qe
Profiles application performance under load using k6, Artillery, or JMeter to measure latency, throughput, and error rates.
proffesor-for-testing/agentic-qe
Conduct context-driven code reviews focusing on quality, testability, and maintainability.
proffesor-for-testing/agentic-qe
Scans for security vulnerabilities including XSS, SQL injection, CSRF, and auth flaws using OWASP Top 10 methodology.
proffesor-for-testing/agentic-qe
Database schema validation, data integrity testing, migration testing, transaction isolation, and query performance.
Categories
Runs continuous AI iteration loops that repeat build-test-fix cycles until success criteria are met. Iterative Loop is an agent skill from proffesor-for-testing/agentic-qe. Runs continuous AI iteration loops that repeat build-test-fix cycles until success criteria are met.
Iterative Loop fits situations like: building features requiring test-driven refinement; implementing tasks with clear pass/fail criteria; automating iterative improvement workflows.
Run `npx skills add proffesor-for-testing/agentic-qe --skill iterative-loop -a claude-code`. Or copy the skill folder (.claude/skills/iterative-loop in proffesor-for-testing/agentic-qe) into .claude/skills/iterative-loop in your project. Claude Code loads it when a task matches its description.
Run `npx skills add proffesor-for-testing/agentic-qe --skill iterative-loop -a codex`. Or copy the skill folder (.claude/skills/iterative-loop in proffesor-for-testing/agentic-qe) into .agents/skills/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 proffesor-for-testing/agentic-qe --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/iterative-loop, .gemini/skills/iterative-loop, .github/skills/iterative-loop and .opencode/skills/iterative-loop in your project.
Going by SKILL.md and its folder, Iterative Loop needs the command-line tools its instructions call (npx). Our summary lists: Node.js.
SKILL.md names 2 domains. As links in the text: github.com and ghuntley.com. 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.
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 2.5k tokens (SKILL.md is roughly 9.9k 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 Iterative Loop: Skill Writing (millionco/expect, 3.6k stars), Go Rig (mudrii/openclaw-dashboard, 458 stars), Go Development Guidelines (jumppad-labs/jumppad, 263 stars) and Work Issue (joesaby/astro-mermaid, 123 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
proffesor-for-testing (a GitHub user) maintains it in proffesor-for-testing/agentic-qe, which has 494 GitHub stars. The repository holds 95 skills in this directory. The repository was last updated on October 4, 2026.
Source: proffesor-for-testing/agentic-qe on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.