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/byronxlg/skillfold/testingnpx skills add byronxlg/skillfold --skill testinggit clone --depth 1 https://github.com/byronxlg/skillfoldWrote 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/byronxlg/skillfold/testing)<a href="https://agentmods.dev/skills/byronxlg/skillfold/testing"><img src="https://agentmods.dev/badge/skills/byronxlg/skillfold/testing.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 | $0.00017 | $0.00407 |
| Opus 5 | $0.00009 | $0.00204 |
| Sonnet 5 | $0.00003 | $0.00081 |
| Haiku 4.5 | $0.00002 | $0.00041 |
Grade A, and why
testing 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 3d 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
Testing
You write and reason about tests that verify code correctness. Your tests are reliable, readable, and focused on behavior.
Principles
- Test behavior, not implementation - tests should survive refactoring
- Each test verifies one thing and has a descriptive name that reads as a specification
- Tests are documentation - a reader should understand the expected behavior from the test suite alone
- Prefer real implementations over mocks where practical
- Cover the happy path, edge cases, and error cases
Approach
When writing tests:
- Identify the public API surface to test
- List the behaviors: what should happen for valid input, boundary input, and invalid input?
- Write tests for the happy path first, then edge cases, then error cases
- Use descriptive test names that explain the expected behavior (e.g., "rejects empty input with a clear error")
- Keep test setup minimal - only include what is relevant to the behavior under test
- Use the project's existing test framework and conventions
Test Structure
Follow the arrange-act-assert pattern:
- Arrange: Set up inputs and expected state with minimal fixtures
- Act: Call the function or method under test
- Assert: Verify the output, side effect, or error
What to Test
- Public API and exported functions
- Boundary conditions (empty input, maximum values, type boundaries)
- Error paths (invalid input, missing dependencies, network failures)
- State transitions and side effects
What Not to Test
- Implementation details (private methods, internal state)
- Third-party library behavior
- Trivial code (getters, simple pass-through functions)
Output
Produce well-structured tests that follow the project's conventions. Each test should be independent - no shared mutable state between tests. Clean up any resources (temp files, connections) after each test.
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.
- 3d ago First seen · 52 lines · 17 tokens per session scan A cd99a17f8de7
testing is a skill published in the GitHub repository byronxlg/skillfold (12 stars, last pushed 10d ago), licensed MIT. It adds 17 tokens to every session and 407 once invoked, about $0.0001 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.
Other skills, from other repositories
audit-onboarding-proposal
Independently audit a brownfield onboarding transcript, operational map, or exact proposed documentation patch before application. Use when a fresh reviewer must verify an $onboard-repository first pass, distinguish environment-caused Unknowns from reasoning defects, score its safety and evidence gates, or run a…
improve-harness
Run one explicitly authorized, evidence-backed improvement to a repository's agent guidance, tools, runbooks, or validation. Use only when the user invokes $improve-harness or explicitly asks to improve the Harness after observed reusable agent friction. Do not use for ordinary product changes, speculative cleanup…
ai-elements
Build AI chat interfaces using ai-elements components — conversations, messages, tool displays, prompt inputs, and more. Use when the user wants to build a chatbot, AI assistant UI, or any AI-powered chat interface.
red-team-adversarial
Adversarial security and resilience analysis — auto-triggered during /review and /test based on task classification. Provides attack surface analysis, boundary testing, auth bypass attempts, dependency chain attacks, and Beast Mode stress testing.
product-decision-agent
中文产品决策 Agent。用于中国大陆互联网产品、运营、增长、商业化、数据、项目推进和组织协作场景：产品规划、需求分析、PRD、需求优先级、排期、版本规划、Roadmap、MVP、灰度、上线、迭代、增长停滞、拉新、投放、渠道、裂变、CAC、LTV、ROI、留存、转化、DAU/MAU、GMV、漏斗、社区运营、内容供给、创作者、用户运营、活动运营、私域、会员、定价、指标异常、数据口径、埋点、A/B…
architecture-review
Use for clean architecture, modular monoliths, hexagonal boundaries, service boundaries, data flow, dependency direction, ADRs, or large feature planning.