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/corridortech/posecap/ad-tddnpx skills add CorridorTech/PoseCap --skill ad-tddgit clone --depth 1 https://github.com/CorridorTech/PoseCapWhat 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 | $0.00201 | $0.01783 |
| Opus 5 | $0.00101 | $0.00892 |
| Sonnet 5 | $0.00040 | $0.00357 |
| Haiku 4.5 | $0.00020 | $0.00178 |
Grade A, and why
ad-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 2d 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 — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
<background_information> Implements WORKFLOW.md §16 end-to-end. The skill is for the implementation phase when the change's behavior is known and expressible as a test up front. No file is written. The output of the skill is the verified implementation that lands in the repo through normal commits.
TDD is a deterministic LLM guardrail: a failing test is unambiguous, so "almost right" (the WORKFLOW §12 failure mode) cannot slip past. The skill keeps the agent inside red-green-refactor and blocks the named anti-pattern (horizontal slicing — bulk-write tests, then bulk-write code).
Good tests read like a specification. "User can checkout with a valid cart" tells you exactly what capability exists. Bad tests couple to implementation — mock internal collaborators, assert on private state, test the shape of things (data structures, function signatures) rather than user-facing behavior. A test that breaks on a rename but not on a behavior change was testing implementation, not behavior.
Distinction from ad-tdg: TDD focuses on driving code through one test at a time (behavior-known regime); TDG picks between three implementation candidates against one ground-truth pair (technique-known, implementation-strategy-uncertain regime). When both apply, use TDD as the outer loop and invoke ad-tdg inside the GREEN phase to pick the implementation strategy for that test cycle.
Codex auto-trigger on description keywords is less mature than Claude Code's. If auto-invocation does not fire when the user mentions TDD, red-green-refactor, test-first, tracer bullet, or behavior-not-implementation, invoke this skill manually. </background_information>
Route elsewhere when:
- The outcome is known but the implementation strategy has multiple plausible paths →
ad-tdg(WORKFLOW §9). - The technique itself is uncertain across multiple plausible approaches →
ad-spike(WORKFLOW §14). - The task is bug investigation, not behavior implementation →
ad-diagnose(WORKFLOW §15). - No test runner is wired →
ad-hooksfirst to scaffold deterministic gates (WORKFLOW §11). TDD depends on a fast green/red signal.
Step 1 — plan vertically. Before writing a test or a line of code:
- Read
CONTEXT.mdif it exists — anchor test names and interface vocabulary in the project's ubiquitous language. - Confirm the public interface. What is the smallest surface the caller needs to know? Types, ordering constraints, error modes.
- Identify deepening opportunities (small interface over deep implementation per WORKFLOW §8). Surface-area-light interfaces are easier to test against and survive refactors.
- List the behaviors to test, not the implementation steps. Pick the first behavior — the one that proves end-to-end the path works. The rest are deferred until the tracer bullet lands.
- Test Dependency Map (TDM) for existing code. If the change modifies existing code, list the tests already covering the surface and run them to establish the green baseline. New-code changes skip the TDM and write the first test fresh.
- Get user approval on the plan. One sentence — "I'll test behavior X first via interface Y, then iterate."
You cannot test everything. Confirm with the user which behaviors matter most; focus on critical paths and complex logic.
Step 2 — tracer bullet. Write ONE test that confirms ONE behavior through the public interface:
- RED: write the test → run → confirm it fails for the expected reason. The fail reason matters — a test that fails because the function is undefined is different from a test that fails because the assertion is wrong.
- GREEN: write the minimum code that makes the test pass → run → confirm green.
Do not write a second test until this one is green.
What ships with it
1 file 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.
- 2d ago First seen · 87 lines · 201 tokens per session scan A fc2715cf2650
ad-tdd is a skill published in the GitHub repository CorridorTech/PoseCap (190 stars, last pushed 10d ago), licensed Apache-2.0. It adds 201 tokens to every session and 1,783 once invoked, about $0.0010 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 2026-08-30.
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