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/jamesgray-ai/handsonai-plugins/testnpx skills add jamesgray-ai/handsonai-plugins --skill testgit clone --depth 1 https://github.com/jamesgray-ai/handsonai-pluginsWrote 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/jamesgray-ai/handsonai-plugins/test)<a href="https://agentmods.dev/skills/jamesgray-ai/handsonai-plugins/test"><img src="https://agentmods.dev/badge/skills/jamesgray-ai/handsonai-plugins/test.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 | $0.00080 | $0.02751 |
| Opus 5 | $0.00040 | $0.01375 |
| Sonnet 5 | $0.00016 | $0.00550 |
| Haiku 4.5 | $0.00008 | $0.00275 |
Grade A, and why
test 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 4d 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 — 146 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Test Workflow
Structured testing and evaluation of AI workflow artifacts. Walk the user through running their workflow against real scenarios, scoring output quality, diagnosing issues back to specific building blocks, and deciding whether the workflow is ready for deployment.
Workflow
1. Load context
Registry entry: the workflow's registry entry is its Workflow concept node in the workspace's
registry/bundle — seeindexing-registry/references/registry-bundle.md(in this plugin) for resolution, write rules, and your fields. If the workspace has noregistry/SCHEMA.md, offer thescaffolding-registryskill first (it also migrates legacyworkflow.yamlworkspaces); do not write registry entries until the bundle exists.
Read the workflow's Workflow node (registry/workflows/<slug>.md) to locate the artifacts, then read the Design Spec and the Workflow Requirements it references (the requirements own the Acceptance Criteria, Example Scenarios, and Golden Examples). Resume orientation: if the user arrived via "continue my workflow" or with no stated workflow, check registry/workflows/ for existing Workflow nodes (if several, list them) and infer progress from which artifacts each node's # Artifacts section already links — "You've completed through Step [N] ([name]) — next is Step [N+1]" — and if Test isn't the next step, say so and route to the right skill. If no Workflow node exists yet but legacy flat files (outputs/[name]-*.md) do, use those paths. Verify both files exist before proceeding — if either is missing, stop and say which.
From these, identify:
- The test scenarios (E1, E2, …) and what to look for in each output
- The scoring dimensions from the Acceptance Criteria
- Any Golden Examples — known-good outputs (or excerpts) attached to scenarios. These are the strongest evaluation tool you have: scoring becomes "compare against this reference" instead of "how does it feel?"
Introduce the vocabulary in plain language the first time you use it (most users are non-technical): a scenario (E1, E2…) is one realistic test input you'll run the workflow on; the eval suite is simply running the workflow across all those scenarios; a baseline is the saved scorecard from this round that you'll compare against later to catch quality slipping. Define each term in a sentence before using it — don't assume the user knows it.
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.
- 4d ago First seen · 146 lines · 80 tokens per session scan A df22f2f7accf
test is a skill published in the GitHub repository jamesgray-ai/handsonai-plugins (8 stars, last pushed 23d ago), licensed MIT. It adds 80 tokens to every session and 2,751 once invoked, about $0.0004 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
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…