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Get Started Free →Autonomous Test-Driven Development Agent workflow for implementing features without human intervention. Use this skill when: - Implementing new features from failing tests - Fixing broken test suites - Creating production-ready code with test coverage - Working in TDD mode with minimal guidance The skill provides a 6-step autonomous loop: analyze tests → implement → predict → iterate → refactor → summarize. Works with any Python project using pytest.
.claude/skills/shadd0wtaka-autonomous-tdd-agent/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✓→✗ | ▼ Worse | -79% | 0% |
| case-02 | ✓→✗ | ▼ Worse | 19% | 0% |
| case-06 | ✓→✗ | ▼ Worse | 50% | 0% |
| case-07 | ✓→✗ | ▼ Worse | -81% | 0% |
| case-15 | ✓→✗ | ▼ Worse | -88% | 0% |
Execute Test-Driven Development autonomously without asking for clarification. Make reasonable assumptions, document them, and proceed through the implementation loop.
python# 1. READ TESTS # Read and understand all test requirements # 2. IMPLEMENT # Write initial implementation # 3. TEST & ITERATE while failing_tests > 0: run_tests() fix_failures() document_fixes() # 4. REFACTOR improve_code_quality() verify_tests_still_pass() # 5. SUMMARIZE report_changes()
Feature: Implement a rate limiter
Test file: tests/unit/test_rate_limiter.py
[Execute 6-step loop autonomously]After completion, provide:
See references/WORKFLOW_EXAMPLES.md for detailed examples.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | pass→fail | 22,155 | 4,694 | -79% | 1 | 1 | 0% | 4,779 | 986 | -79% | 0 | 0 | — |
case-02 | pass→fail | 27,133 | 31,125 | +15% | 1 | 1 | 0% | 5,710 | 6,779 | +19% | 0 | 0 | — |
case-03 | fail→fail | 29,515 | 4,145 | -86% | 1 | 1 | 0% | 5,685 | 817 | -86% | 0 | 0 | — |
case-04 | pass→pass | 17,557 | 20,717 | +18% | 1 | 1 | 0% | 3,867 | 5,096 | +32% | 0 | 0 | — |
case-05 | fail→fail | 7,838 | 11,617 | +48% | 1 | 1 | 0% | 907 | 1,764 | +94% | 0 | 0 | — |
case-06 | pass→fail | 20,764 | 28,219 | +36% | 1 | 1 | 0% | 4,492 | 6,754 | +50% | 0 | 0 | — |
case-07 | pass→fail | 22,083 | 4,228 | -81% | 1 | 1 | 0% | 4,565 | 886 | -81% | 0 | 0 | — |
case-08 | fail→fail | 19,800 | 5,301 | -73% | 1 | 1 | 0% | 4,043 | 937 | -77% | 0 | 0 | — |
case-09 | fail→fail | 25,347 | 30,373 | +20% | 1 | 1 | 0% | 5,755 | 6,787 | +18% | 0 | 0 | — |
case-15 | pass→fail | 31,960 | 11,006 | -66% | 1 | 1 | 0% | 6,183 | 734 | -88% | 0 | 0 | — |
case-10 | pass→pass | 21,961 | 31,395 | +43% | 1 | 1 | 0% | 5,076 | 6,769 | +33% | 0 | 0 | — |
case-11 | fail→fail | 3,595 | 4,540 | +26% | 1 | 1 | 0% | 111 | 739 | +566% | 0 | 0 | — |
case-12 | fail→fail | 4,200 | 31,125 | +641% | 1 | 1 | 0% | 140 | 859 | +514% | 0 | 0 | — |
case-13 | pass→pass | 30,480 | 30,237 | -1% | 1 | 1 | 0% | 6,197 | 6,778 | +9% | 0 | 0 | — |
case-14 | fail→fail | 12,646 | 4,607 | -64% | 1 | 1 | 0% | 2,689 | 820 | -70% | 0 | 0 | — |
case-16 | fail→fail | 27,561 | 4,139 | -85% | 1 | 1 | 0% | 6,190 | 738 | -88% | 0 | 0 | — |
case-17 | pass→fail | 21,216 | 6,087 | -71% | 1 | 1 | 0% | 5,161 | 763 | -85% | 0 | 0 | — |
case-18 | pass→fail | 53,237 | 3,321 | -94% | 1 | 1 | 0% | 5,291 | 747 | -86% | 0 | 0 | — |
case-19 | fail→fail | 4,791 | 4,245 | -11% | 1 | 1 | 0% | 210 | 791 | +277% | 0 | 0 | — |
case-20 | fail→fail | 20,780 | 6,065 | -71% | 1 | 1 | 0% | 4,288 | 903 | -79% | 0 | 0 | — |
case-21 | pass→fail | 16,662 | 28,840 | +73% | 1 | 1 | 0% | 3,624 | 6,757 | +86% | 0 | 0 | — |
case-22 | fail→fail | 25,619 | 2,827 | -89% | 1 | 1 | 0% | 6,177 | 734 | -88% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted, and 8 counted toward the lift figure. The other 14 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of -100 percentage points is the difference between those two pass rates over the 8 comparable cases. 11 cases got worse with the skill loaded, and they are included in that figure.
Other measured skills in the registry, with their headline benchmark lift.