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 skills add kambleakash0/agent-skills --skill incremental-tddgit clone --depth 1 https://github.com/kambleakash0/agent-skillsWrote 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/kambleakash0/agent-skills/incremental-tdd)<a href="https://agentmods.dev/skills/kambleakash0/agent-skills/incremental-tdd"><img src="https://agentmods.dev/badge/skills/kambleakash0/agent-skills/incremental-tdd/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/kambleakash0/agent-skills/incremental-tdd"><img src="https://agentmods.dev/badge/skills/kambleakash0/agent-skills/incremental-tdd.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 | $0.00035 | $0.01295 |
| Opus 5 | $0.00017 | $0.00647 |
| Sonnet 5 | $0.00007 | $0.00259 |
| Haiku 4.5 | $0.00003 | $0.00129 |
Grade A, and why
incremental-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 10d 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.
Test-Driven Development (Red-Green-Refactor)
This skill forces you to work in tight TDD loops: one failing test, one implementation, then refactor. It prefers vertical slices and deep modules so tests stay honest, stable, and focused on real behavior instead of implementation details.
This skill comes with extra documents you can reference for philosophy and examples:
- deep-modules.md – why deep modules make AI‑driven TDD easier and safer.
- refactoring.md – patterns for safe refactors.
- interface-design.md – how to design interfaces for TDD.
- mocking.md – how and when to mock without lying to yourself.
- tests.md – how and when to mock without lying to yourself.
When you need guidance on design or refactoring, open and read these files instead of guessing.
When to Use
Use this skill when the user:
- Wants to build a feature or fix a bug using TDD.
- Mentions “red‑green‑refactor”, “test‑first”, or “write the tests first”.
- Wants honest integration or module‑level tests that drive design rather than brittle unit tests.
- Is running longer “Ralph” loops or autonomous agents and wants higher‑quality changes.
If the requirements are unclear, suggest using /grill-me and /spec-writer first, then come back here for execution.
Overall Workflow
You can compress steps when context is obvious, but keep the red → green → refactor discipline.
-
Clarify behavior and interfaces
- Confirm what behavior should exist from the outside: which inputs, outputs, and observable effects matter.
- Identify or design the interfaces you’ll use to expose that behavior (public functions, handlers, endpoints, etc.).
- Prefer deep modules: fewer, larger modules with thin, stable interfaces on top.
- If needed, open deep-modules.md and adjust your interface design to match that philosophy.
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.
- 10d ago First seen · 101 lines · 35 tokens per session scan A 59cdc6568b3c
incremental-tdd is a skill published in the GitHub repository kambleakash0/agent-skills (9 stars, last pushed 23d ago), licensed MIT. It adds 35 tokens to every session and 1,295 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.
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