Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/catlog22/Claude-Code-Workflownpx agentmods add skills/catlog22/claude-code-workflow/workflow-test-fix-cycleWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/catlog22/claude-code-workflow/workflow-test-fix-cycle)<a href="https://agentmods.dev/skills/catlog22/claude-code-workflow/workflow-test-fix-cycle"><img src="https://agentmods.dev/badge/skills/catlog22/claude-code-workflow/workflow-test-fix-cycle/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/catlog22/claude-code-workflow/workflow-test-fix-cycle"><img src="https://agentmods.dev/badge/skills/catlog22/claude-code-workflow/workflow-test-fix-cycle.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00063 | $0.04630 |
| Opus 5.5 | $0.00025 | $0.01852 |
| Sonnet 5.5 | $0.00013 | $0.00926 |
| Haiku 4.5 | $0.00006 | $0.00463 |
Grade A, and why
workflow-test-fix-cycle scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 411 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Workflow Test-Fix Cycle
End-to-end test-fix workflow pipeline: generate test sessions with progressive layers (L0-L3), AI code validation, and task generation (Phase 1), then execute iterative fix cycles with adaptive strategy engine until pass rate >= 95% (Phase 2).
Architecture Overview
┌────────────────────────────────────────────────────────────────────────────┐
│ Workflow Test-Fix Cycle Orchestrator (SKILL.md) │
│ → Full pipeline: Test generation + Iterative execution │
│ → Phase dispatch: Read phase docs, execute, pass context │
└───────────────┬────────────────────────────────────────────────────────────┘
│
┌────────────┴────────────────────────┐
↓ ↓
┌─────────────────────────┐ ┌─────────────────────────────┐
│ Phase 1: Test-Fix Gen │ │ Phase 2: Test-Cycle Execute │
│ phases/01-test-fix-gen │ │ phases/02-test-cycle-execute │
│ 5 sub-phases: │ │ 3 stages: │
│ ① Create Session │ │ ① Discovery │
│ ② Gather Context │ │ ② Main Loop (iterate) │
│ ③ Test Analysis (Gemini)│ │ ③ Completion │
│ ④ Generate Tasks │ │ │
│ ⑤ Summary │ │ Agents (via spawn_agent): │
│ │ │ @cli-planning-agent │
│ Agents (via spawn_agent)│ │ @test-fix-agent │
│ @test-context-search │ │ │
│ @context-search │ │ Strategy: conservative → │
│ @cli-execution │ │ aggressive → surgical │
│ @action-planning │ │ │
└────────┬────────────────┘ └────────────┬──────────────────┘
↓ ↓
IMPL-001..002.json Pass Rate >= 95%
TEST_ANALYSIS_RESULTS.md Auto-complete session
Task Pipeline:
┌──────────────┐ ┌─────────────────┐ ┌─────────────────┐ ┌──────────────┐
│ IMPL-001 │───→│ IMPL-001.3 │───→│ IMPL-001.5 │───→│ IMPL-002 │
│ Test Gen │ │ Code Validate │ │ Quality Gate │ │ Test & Fix │
│ L1-L3 │ │ L0 + AI Issues │ │ Coverage 80%+ │ │ Max 10 iter │
│@code-developer│ │ @test-fix-agent │ │ @test-fix-agent │ │@test-fix-agent│
└──────────────┘ └─────────────────┘ └─────────────────┘ └──────────────┘
│
Fix Loop: │
┌──────────────────┘
↓
┌──────────┐
│ @cli-plan│───→ IMPL-fix-N.json
│ agent │
├──────────┤
│@test-fix │───→ Apply & re-test
│ agent │
└──────────┘
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 411 lines · 63 tokens per session scan A 0477ad77ada7
workflow-test-fix-cycle is a skill published in the GitHub repository catlog22/Claude-Code-Workflow (2,124 stars, last pushed 4mo ago), licensed MIT. It adds 63 tokens to every session and 4,630 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
debugging-livekit-agents
Drives a multi-turn conversation with a LiveKit agent running locally to see what it does. Use when the user says "test my agent", "try my agent", "does this work", "why did it call that tool", "it says the wrong thing when I ask X", "test this change", or whenever you have edited an agent and need to check how it…
lov-runtime-verification
Verify client runtime fixes on actual desktop builds, physical iOS/Android devices, React Native/Flutter hosts, and deployed Web/PWA browsers with comparable workloads and lifecycle checks.
lov-wechat-parity-validation
A review workflow for checking whether a WeChat compatibility fix works across data parsing, shared meaning, interfaces, downstream uses, and the real user path. It treats regression checks as proof that the fix is complete.
lov-fix-until-no-error
A verification-focused debugging workflow that repeatedly runs specified checks, fixes the highest-priority failure, and stops when the checks pass or a genuine external blockage remains.
test-fix
Automatically invoke when any test run produces failures: FAIL output, test errors, or assertion failures in unit or E2E suites. Do NOT wait for the user to ask. Hard limit of 3 fix attempts per failure, then stops and escalates. Trigger on: "tests are failing", "fix the tests", "tests are broken".
ai-regression-testing
Specialized regression testing for AI-assisted development and blind spot mitigation.