Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/vpeetla-ai/react-agent-pattern/tdd-agent-loopsnpx skills add vpeetla-ai/react-agent-pattern --skill tdd-agent-loopsgit clone --depth 1 https://github.com/vpeetla-ai/react-agent-patternWrote 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/vpeetla-ai/react-agent-pattern/tdd-agent-loops)<a href="https://agentmods.dev/skills/vpeetla-ai/react-agent-pattern/tdd-agent-loops"><img src="https://agentmods.dev/badge/skills/vpeetla-ai/react-agent-pattern/tdd-agent-loops.svg" alt="Measured on agentmods" 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 | $0.00048 | $0.00353 |
| Opus 5 | $0.00024 | $0.00177 |
| Sonnet 5 | $0.00010 | $0.00071 |
| Haiku 4.5 | $0.00005 | $0.00035 |
Grade A, and why
tdd-agent-loops 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 4d 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.
This is a copy
100% identical to tdd-agent-loops — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
TDD for Agent Loops
Inspired by mattpocock/skills tdd — adapted for LangGraph.
Red → Green → Refactor
- Red — Write failing test for routing or node output (mock LLM)
- Green — Minimal node/routing fix
- Refactor — Extract helpers only when second test needs it
What to test
| Layer | Test |
|---|---|
| Routing | route_after_quality returns retry/pass/escalate |
| Nodes | Given state in, partial state out (no real LLM) |
| Integration | Full graph with FakeLLM fixture |
| Repo-fix | Clone fixture repo, assert branch + patch count |
Fixtures
@pytest.fixture
def fake_llm():
return FakeLLM(responses=[{"content": "..."}])
Async
@pytest.mark.asyncio
async def test_graph_happy_path(tmp_path):
...
Avoid
- Tests that only assert "graph compiles"
- Snapshotting full LLM prose (flaky)
- Hitting live Groq in CI
Reference
loop-engine-agent-platform/tests/test_repo_fix.py*-agent-pattern/tests/— minimal pattern coverage
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.
- 4d ago First seen · 54 lines · 48 tokens per session scan A 26c8e7ebe7c5
tdd-agent-loops is a skill published in the GitHub repository vpeetla-ai/react-agent-pattern (2 stars, last pushed today), licensed MIT. It adds 48 tokens to every session and 353 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to tdd-agent-loops, differing in 0 lines, and is treated as a copy.
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