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/alekseiseleznev/intent-driven-codex/tddnpx skills add AlekseiSeleznev/intent-driven-codex --skill tddgit clone --depth 1 https://github.com/AlekseiSeleznev/intent-driven-codexWrote 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/alekseiseleznev/intent-driven-codex/tdd)<a href="https://agentmods.dev/skills/alekseiseleznev/intent-driven-codex/tdd"><img src="https://agentmods.dev/badge/skills/alekseiseleznev/intent-driven-codex/tdd.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 | $0.00045 | $0.01325 |
| Opus 5 | $0.00023 | $0.00662 |
| Sonnet 5 | $0.00009 | $0.00265 |
| Haiku 4.5 | $0.00005 | $0.00133 |
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 5d 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.
The source is not reproduced here
A licence we could not identify
The repository carries a LICENSE file, but it is custom or dual enough that GitHub cannot name it and neither can this catalogue. Unknown terms are not permission, so the body is not copied here. Read the licence at the source and decide for yourself.
What ships with it
5 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.
- 5d ago First seen · 145 lines · 45 tokens per session scan A 6a7aaa4e7e3e
tdd is a skill published in the GitHub repository AlekseiSeleznev/intent-driven-codex (5 stars, last pushed 3mo ago), with no licence file. It adds 45 tokens to every session and 1,325 once invoked, about $0.0002 per session on Opus 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 2026-08-31.
Other skills, from other repositories
tdd-workflow
Use when implementing any feature or bugfix, before writing implementation code - write the test first, watch it fail, write minimal code to pass; ensures tests actually verify behavior by requiring failure first. Enforces RED-GREEN-REFACTOR cycle with iron-law compliance.
execute
Dispatch and execute implementation plans with TDD and checkpoints. Use when plan is ready. Parallel by default for independent tasks.
tdd
Strict test-driven development for behavior changes. Requires verified RED before production code, minimal GREEN, and refactor only after passing tests.
research-topic-compiler
Product Research / 产品研究编译器:当用户要围绕一个产品问题做系统调研、竞品证据、替代方案、行业信号、 用户与市场研究、最佳实践提炼、产品候选池、本地 HTML 研究看板、可视化研究报告或跨职能 Dashboard 时使用。 当用户只有大白话、模糊方向、业务愿望或 Roadmap/PRD 前置材料想法,需要先转成清晰研究目标、研究问题和输出要求时也使用。 适合把研究转成 Research Project、学习报告、证据矩阵、PM 决策看板、候选池、模板、实践任务、业务判断、商业化输入或高门槛应用研究前置。适合“系统研究一个主题” “整理到…
prd-architect
PRD 架构师 / 需求文档起草:当用户要把一个产品想法、需求草稿、脑暴结果或功能说明整理成 PRD 时使用。 可用中文唤起:“帮我写 PRD”“帮我选 PRD 模板”“把这个需求整理成 PRD”“判断该用轻量 PRD 还是标准 PRD” “补一张可编辑 Draw.io 核心流程图”“PRD 里加架构图”。 会在 PRD-lite、PRD-standard、PRD-ai-native 中选择一个模板资产按需加载,并在需要时加载 mockup handoff、 Draw.io 图示或开发 handoff 附录;页面型 PRD 默认联动生成项目 UI 对齐的 HTML、关键截图和正文证据。 不用于直接编码、单纯画…
brainstorming
设计脑暴 / 实现前方案校准:当用户想把已基本成立的想法、功能方向或产品问题,在写 PRD、画 mockup 或进入开发计划前,先比较方案、确认取舍、对齐 UI/视觉约束,并收敛成可执行设计 spec 时使用。 可用中文唤起:“先脑暴一下方案”“先不要写 PRD,帮我设计几种路径”“参考 brainstorming 把这个需求变成设计 spec” “实现前先讨论设计”。问题还没定义清楚时先用 ai-collaboration-calibration;已有方案要压力测试时用 grill-me; 直接写 PRD 时用 prd-architect;grill-me 返回精确设计 gap 时,本 Skill 只输出 Design…