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/porcupine-md/jonggrang/testingnpx skills add porcupine-md/jonggrang --skill testinggit clone --depth 1 https://github.com/porcupine-md/jonggrangWrote 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/porcupine-md/jonggrang/testing)<a href="https://agentmods.dev/skills/porcupine-md/jonggrang/testing"><img src="https://agentmods.dev/badge/skills/porcupine-md/jonggrang/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 | $0.00008 | $0.00571 |
| Opus 5 | $0.00004 | $0.00285 |
| Sonnet 5 | $0.00002 | $0.00114 |
| Haiku 4.5 | $0.00001 | $0.00057 |
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 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 — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context
Project {{project_name}} uses {{stack}} with test framework {{test_framework}}. Generate {{input.type}} tests for: {{input.target}}.
Read AGENTS.md for testing conventions.
Instructions
-
Analyze target code
- Read the file/directory to be tested: {{input.target}}
- Identify: exports, functions, classes, side effects
- Identify: dependencies that need to be mocked
- Understand business logic and edge cases
-
Analyze existing tests
- Read existing test files to understand patterns
- Identify: test structure, naming convention, setup/teardown
- Identify: mock patterns, fixture usage
-
Generate test file
- Path: according to project convention
- Co-located:
{{input.target}}.test.ts - Separate:
tests/{{input.target}}.test.ts
- Co-located:
- Structure:
describe('[Module/Function name]', () => { describe('[method/scenario]', () => { it('should [expected behavior]', () => {}) }) })
- Path: according to project convention
-
Test categories based on type:
Unit tests:
- Happy path (expected input -> expected output)
- Edge cases (empty, null, boundary values)
- Error cases (invalid input, thrown errors)
- Mock external dependencies
Integration tests:
- API endpoint tests (request -> response)
- Database operations (CRUD + constraints)
- Service interactions (service A calls service B)
- Use real dependencies (test DB, not mocks)
E2E tests:
- User flows (register -> login -> use feature)
- Browser interactions (click, type, navigate)
- Visual verification (if applicable)
-
Setup test utilities (if not already present)
- Test database setup/teardown (integration)
- Common fixtures/factories
- Mock helpers
Validation
- Test file created in the correct location
- All tests passing
- Coverage increase for target files
- No skipped tests
- Test names descriptive and clear
- Mocks/fixtures clean (no leaked state between tests)
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 · 82 lines · 8 tokens per session scan A b112b6bddba6
testing is a skill published in the GitHub repository porcupine-md/jonggrang (11 stars, last pushed 8d ago), licensed MIT. It adds 8 tokens to every session and 571 once invoked, about $0.0000 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
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…