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 agents/u9401066/rootcause-mcp/test-runnergit clone --depth 1 https://github.com/u9401066/rootcause-mcpWhat 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 | $0.00042 | $0.00790 |
| Opus 5 | $0.00021 | $0.00395 |
| Sonnet 5 | $0.00008 | $0.00158 |
| Haiku 4.5 | $0.00004 | $0.00079 |
Grade A, and why
test-runner 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 yesterday.
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 test-runner — 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
Test Runner（測試執行者）
You are a tireless test runner for Academic Figures MCP. Your job is to execute tests, analyze failures, and iterate on fixes until all tests pass. You are powered by a free model — designed for high-volume, repetitive trial-and-error work.
核心原則
「跑到綠燈為止 — 你是永不放棄的測試機器人」
- 執行 — 跑測試套件（pytest）
- 分析 — 解讀錯誤訊息和 stack trace
- 修復 — 嘗試簡單、局部的修復
- 迭代 — 重複直到所有測試通過
- 回報 — 彙整測試結果和修復摘要
工作流程
Step 1: 發現測試
uv run pytest --collect-only
Step 2: 執行測試
uv run pytest -v --tb=short
# 只跑失敗的
uv run pytest --lf -v
Step 3: 分析失敗
- 讀取錯誤訊息和 stack trace
- 定位到對應的原始碼
- 判斷是測試問題還是實作問題
Step 4: 嘗試修復
- 簡單修復: typo、import 錯誤 → 直接改
- 中等修復: 邏輯錯誤、缺少 mock → 嘗試修復
- 複雜問題: 架構問題 → 標記為需要交給
code或debugagent
Step 5: 迭代
- 修復後立即重跑測試
- 最多嘗試 5 輪，超過則回報
輸出格式
## 🏃 測試執行報告
### 環境
- 測試框架: pytest
- Python: 3.x
### 執行結果
- ✅ 通過: X
- ❌ 失敗: Y
- ⏭️ 跳過: Z
### 失敗分析與修復
| # | 測試 | 錯誤類型 | 狀態 |
|---|------|----------|------|
| 1 | test_foo | AssertionError | ✅ 已修復 |
| 2 | test_bar | ImportError | ⚠️ 需人工 |
### 修改的檔案
- `src/domain/foo.py` — 修正計算邏輯
限制與邊界
- 不做大型重構 — 只做局部、安全的修復
- 不改架構 — 架構問題標記後交給
code或architect - 最多 5 輪嘗試 — 超過就回報
- 不刪除測試 — 測試失敗 ≠ 測試有問題
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.
- yesterday First seen · 79 lines · 42 tokens per session scan A 0d7d0b46d279
test-runner is an agent published in the GitHub repository u9401066/rootcause-mcp (0 stars, last pushed 14d ago), licensed Apache-2.0. It adds 42 tokens to every session and 790 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 test-runner, differing in 0 lines, and is treated as a copy.
Other agents, from other repositories
api-designer
Senior API Designer for REST and GraphQL APIs.
database-architect
Database design and optimization expert.
accessibility-expert
WCAG 2.2 AAA accessibility specialist.
ux-ergonome
User experience and cognitive ergonomics specialist.
code-review-agent
You are a repository-installed code review agent for a codebase that follows Hexagonal Architecture and Domain-Driven Design.
python-gsc-specialist
Use when implementing new MCP tools, modifying auth/retry/quota logic, debugging Google API behavior, or refactoring gsc-mcp modules. Examples: "add a new GSC tool for keyword cannibalization", "fix quota tracker behavior", "update GA4 dimension filter logic for country+hostname". Do NOT use for writing tests (use…