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 context4ai/workflow --skill make-test-plangit clone --depth 1 https://github.com/context4ai/workflowWrote 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/context4ai/workflow/make-test-plan)<a href="https://agentmods.dev/skills/context4ai/workflow/make-test-plan"><img src="https://agentmods.dev/badge/skills/context4ai/workflow/make-test-plan/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/context4ai/workflow/make-test-plan"><img src="https://agentmods.dev/badge/skills/context4ai/workflow/make-test-plan.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 | $0.00049 | $0.02135 |
| Opus 5.5 | $0.00020 | $0.00854 |
| Sonnet 5.5 | $0.00010 | $0.00427 |
| Haiku 4.5 | $0.00005 | $0.00214 |
Grade A, and why
make-test-plan 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.
How it starts
The opening of the file, as written. The whole thing — 274 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Make Test Plan
Generates E2E test skeleton files from a PRD (Product Requirements Document) and Plan. Creates a version test index (README.md) and bone-skeleton test files using test.todo(...) placeholders, categorized by verification type.
Agent Responsibility Boundaries
The agent's role when running /make-test-plan is strictly limited to:
- Generate test skeleton files with
test.todo(...)placeholders - Create a version test index (
README.md) documenting all planned tests - Categorize each test by verification type (AUTO / AGENT / HUMAN)
- Update the Plan file with E2E coverage references
The agent MUST NOT:
- Implement test assertions or actual test logic — only
test.todo(...)placeholders - Modify feature source code
- Run or execute any tests
- Make decisions about feature implementation
Three Verification Types
Tests are categorized by how they can be verified. The guiding principle is to minimize HUMAN tests — prefer AUTO whenever possible, fall back to AGENT, and only use HUMAN as a last resort.
| Type | Symbol | Meaning | Who Verifies |
|---|---|---|---|
| AUTO | [A] |
Fully automatable with deterministic assertions | CI / /verify |
| AGENT | [G] |
Requires agent judgment (visual diff, semantic check, heuristic) | AI agent during /verify |
| HUMAN | [H] |
Requires human interaction, subjective evaluation, or physical device | Human tester |
Preference Order: AUTO > AGENT > HUMAN
When deciding the verification type for a test:
- Can the assertion be expressed as a deterministic code check? (string match, status code, DOM state, data comparison) → AUTO
- Can an AI agent evaluate it with sufficient confidence? (visual layout, semantic content quality, heuristic rules) → AGENT
- Does it require physical interaction, subjective judgment, or real-world side effects? (UX feel, hardware behavior, payment flow) → HUMAN
E2E Implementation Ownership
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 · 274 lines · 49 tokens per session scan A ddee7b6e65ff
make-test-plan is a skill published in the GitHub repository context4ai/workflow (4 stars, last pushed 6mo ago), licensed MIT. It adds 49 tokens to every session and 2,135 once invoked, about $0.0002 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 2026-08-31.
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