Borrowing it
Nothing to install: this file belongs to AsiaOstrich/EngramGraph. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/AsiaOstrich/EngramGraph/main/.agents/skills/tdd-assistant/SKILL.mdgit clone --depth 1 https://github.com/AsiaOstrich/EngramGraphWrote 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/asiaostrich/engramgraph/tdd-assistant)<a href="https://agentmods.dev/skills/asiaostrich/engramgraph/tdd-assistant"><img src="https://agentmods.dev/badge/skills/asiaostrich/engramgraph/tdd-assistant/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/asiaostrich/engramgraph/tdd-assistant"><img src="https://agentmods.dev/badge/skills/asiaostrich/engramgraph/tdd-assistant.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 | $0.00140 | $0.01397 |
| Opus 5.5 | $0.00056 | $0.00559 |
| Sonnet 5.5 | $0.00028 | $0.00279 |
| Haiku 4.5 | $0.00014 | $0.00140 |
Grade A, and why
tdd 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 14d 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 — 101 lines — stays where its author put it; the contents beside it link to each section on GitHub.
TDD Assistant | TDD 助手
⚠️ Status: Reference（參考用，非可執行流程） — TDD 流程協調（RED→GREEN→REFACTOR 循環、測試執行 gates）已於 2026-04-28 移至 adoption layer（XSPEC-095）。此處保留 TDD 原則、FIRST 規則、AAA 格式、語言範例、流程指南供參考。 Lifecycle orchestration has moved to the adoption layer (XSPEC-095); this document is reference-only. For enforced execution, use your adoption layer's toolchain.
Guide through the Test-Driven Development workflow: Red-Green-Refactor.
引導測試驅動開發（TDD）流程：紅-綠-重構。
TDD Cycle | TDD 循環
┌─────┐ ┌───────┐ ┌──────────┐
│ RED │ ────► │ GREEN │ ────► │ REFACTOR │
└─────┘ └───────┘ └──────────┘
▲ │
└────────────────────────────────┘
Workflow | 工作流程
📖 以下為流程的參考結構，非可執行步驟；自動化／強制執行請用 adoption layer 工具鏈（XSPEC-095）。 The steps below are a reference structure, not an executable workflow.
Phase 1: RED - Write Failing Test | 紅燈 - 撰寫失敗測試
- Write a test that describes the desired behavior | 撰寫描述預期行為的測試
- Run tests - confirm it fails for the right reason | 執行測試 - 確認正確地失敗
- Use AAA pattern (Arrange-Act-Assert) | 使用 AAA 模式
Phase 2: GREEN - Make Test Pass | 綠燈 - 讓測試通過
- Write minimum code to pass the test | 撰寫最少的程式碼讓測試通過
- Hardcoding is acceptable at this stage | 此階段可以硬編碼
- Run tests - confirm it passes | 執行測試 - 確認通過
Phase 3: REFACTOR - Improve Code | 重構 - 改善程式碼
- Remove duplication (DRY) | 移除重複
- Improve naming and structure | 改善命名與結構
- Run tests after every change | 每次變更後執行測試
- No new functionality added | 不新增功能
FIRST Principles | FIRST 原則
| Principle | Description | 說明 |
|---|---|---|
| Fast | Tests run quickly (< 100ms/unit) | 快速執行 |
| Independent | No shared state between tests | 測試間無共享狀態 |
| Repeatable | Same result every time | 每次結果相同 |
| Self-validating | Clear pass/fail result | 明確的通過/失敗 |
| Timely | Written before production code | 在產品程式碼之前撰寫 |
What ships with it
3 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.
- 14d ago First seen · 101 lines · 140 tokens per session scan A 2ecb79126ff5
tdd is a skill published in the GitHub repository AsiaOstrich/EngramGraph (3 stars, last pushed today), licensed MIT. It adds 140 tokens to every session and 1,397 once invoked, about $0.0006 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 2026-10-16.
Other skills, from other repositories
kivgraph
Use Kivgraph's local MCP server to search symbols, read their source, and follow references, dependencies, cross-repository consumers and graph status in the indexed code graph.
source-command-sparc-coder
Migrated source command sparc-coder.
source-command-sparc-tdd
Migrated source command sparc-tdd.
mushroom
Live graph memory for agents — query and create entities, derive relationships by rule, explain why two things are associated, read the graph at a past commit, ask what a change would do, and answer as one role. Trigger on: memory, remember, recall, relationship, entity, association, why are X and Y related, as of…
sparc-methodology
SPARC (Specification, Pseudocode, Architecture, Refinement, Completion) comprehensive development methodology with multi-agent orchestration.
Pair Programming
AI-assisted pair programming with multiple modes (driver/navigator/switch), real-time verification, quality monitoring, and comprehensive testing. Supports TDD, debugging, refactoring, and learning sessions. Features automatic role switching, continuous code review, security scanning, and performance optimization with…