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 CodeInfinity1/Stram --skill test-driven-developmentgit clone --depth 1 https://github.com/CodeInfinity1/StramWrote 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/codeinfinity1/stram/test-driven-development)<a href="https://agentmods.dev/skills/codeinfinity1/stram/test-driven-development"><img src="https://agentmods.dev/badge/skills/codeinfinity1/stram/test-driven-development/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/codeinfinity1/stram/test-driven-development"><img src="https://agentmods.dev/badge/skills/codeinfinity1/stram/test-driven-development.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 | $0.00026 | $0.00417 |
| Opus 5.5 | $0.00010 | $0.00167 |
| Sonnet 5.5 | $0.00005 | $0.00083 |
| Haiku 4.5 | $0.00003 | $0.00042 |
Grade A, and why
test-driven-development 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 1mo 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.
What it actually says
Test Driven Development
Purpose
Use tests as executable evidence for behavior changes. The assistant should add or update focused tests before or alongside implementation when the task risk justifies it.
When To Use
Use for bug fixes, new behavior, parser/planner changes, shared APIs, regressions, and any change where a focused test can prevent backslide.
Inputs And Evidence
- Expected behavior, failing case, existing tests, code under test, and verification command.
- Current test output before and after implementation.
Tool Map
search_workspaceread_filerun_shell_commandpython_interpreterwrite_notediff_render
Workflow
- Identify the behavior contract and existing coverage.
- Add or update the smallest meaningful failing test when feasible.
- Run the targeted test to confirm it fails for the right reason.
- Implement the minimal scoped fix.
- Run targeted tests, then broader tests when blast radius warrants it.
- Refactor only after green tests and only when it reduces real complexity.
Native Implementation Boundaries
- Use the repo's own test framework and Stram tools.
- Do not import external reference TDD scripts.
- Do not replace model-led planning with hardcoded test-name routing.
Safety And Approval
- Do not delete tests to pass.
- Avoid broad snapshots unless they capture real behavior.
- Preserve unrelated user changes.
Verification
- Report red and green commands when both were run.
- If a red run is skipped, say why.
- Mention any untested residual risk.
Failure Modes
- Writing a test that only checks implementation detail.
- Overfitting to one brittle assertion.
- Running too narrow a check for a shared contract.
References
- Shortlist item:
test-driven-development. - Upstream inspiration: external reference TDD reference only.
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.
- 1mo ago First seen · 67 lines · 26 tokens per session scan A 033beb721632
test-driven-development is a skill published in the GitHub repository CodeInfinity1/Stram (11 stars, last pushed 2mo ago), licensed MIT. It adds 26 tokens to every session and 417 once invoked, about $0.0001 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 2026-09-03.
Other skills, from other repositories
testing
TDD/BDD testing principles. Use when writing tests, reviewing test coverage, setting up testing, or discussing test strategy and test architecture.
agent-test-generator
Specialist subagent: Generates comprehensive test suites using TDD patterns. Use only when the user explicitly asks for tests, improved coverage, or test-first development.
refactor-ops
Safe refactoring patterns - extract, rename, restructure with test-driven methodology and dead code detection. Use for: refactor, refactoring, extract function, extract component, rename, move file, restructure, dead code, unused imports, code smell, duplicate code, long function, god object, feature envy, DRY…
testing-ops
Cross-language testing strategies and patterns. Triggers on: test pyramid, unit test, integration test, e2e test, TDD, BDD, test coverage, mocking strategy, test doubles, test isolation.
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…
ai-observability-promptfoo
Testing and evaluation framework for LLM prompts and applications -- promptfooconfig.yaml, assertions, model-graded evals, red teaming, CI/CD integration, custom providers, and comparative evaluation.