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/shinpr/codex-workflows/integration-e2e-testingnpx skills add shinpr/codex-workflows --skill integration-e2e-testinggit clone --depth 1 https://github.com/shinpr/codex-workflowsWhat 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 | $0.00025 | $0.00795 |
| Opus 5 | $0.00013 | $0.00398 |
| Sonnet 5 | $0.00005 | $0.00159 |
| Haiku 4.5 | $0.00003 | $0.00080 |
Grade A, and why
integration-e2e-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 2d 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 — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Integration and E2E Testing
Reference
Read references/e2e-design.md only when a selected browser-level claim needs UI Spec mapping or browser harness guidance. Use the repository's browser harness when it differs from the examples.
Selection Gate
Select an integration or E2E test only when all conditions hold:
- a confirmed AC, preserved behavior, or task Verification Focus names an observable claim;
- correctness depends on a component, persistence, process, browser, or service boundary that a local/unit proof cannot exercise;
- existing tests leave that failure mode unproven at the boundary;
- the expected regression-detection value justifies the fixture, environment, runtime, and maintenance cost.
When any condition fails, leave the claim to its focused local or task verification. An empty integration/E2E selection is a successful result and needs no absence artifact.
Lanes and Ceilings
| Lane | Use when | Ceiling per outcome |
|---|---|---|
integration |
In-process component, persistence, or contract interaction must stay real | 3 |
fixture-e2e |
Browser-visible interaction needs the real UI but controlled backend/fixture state is sufficient | 3 |
service-integration-e2e |
The claim specifically depends on a running local cross-service boundary that other lanes cannot prove | 2 |
Ceilings are limits, not targets or reserved slots. Prefer the lowest-cost lane that proves the named claim. Real external production services are outside these lanes; verify their repository-owned contract instead.
Candidate Selection
For each boundary-dependent claim:
- state the material failure that could remain falsely green;
- identify the boundary that must remain real and what may be controlled or mocked;
- compare with existing tests and lower-cost proof;
- select the candidate only when it adds distinct material detection value;
- combine claims that share setup, boundary, and observable outcome when one test can prove them clearly.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 2d ago First seen · 80 lines · 25 tokens per session scan A a9551d5b9185
integration-e2e-testing is a skill published in the GitHub repository shinpr/codex-workflows (38 stars, last pushed 4d ago), licensed MIT. It adds 25 tokens to every session and 795 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
chrome-cdp
Drive a headless Chrome over the Chrome DevTools Protocol (CDP) for browser QA — navigate, click, fill forms, read the DOM/accessibility tree, screenshot, and assert. Use whenever a task requires loading a web page and interacting with it like a user. Chrome is launched by a bash step (recipe below); this skill…
metrics-instrumentation
Specification for instrumenting an opik-backend workflow with operational OpenTelemetry metrics — per-stage throughput/latency/error counters and native histograms, dimensioned per-customer (workspace). Use when a pipeline (scoring, ingestion, experiments, jobs) needs per-stage visibility. Covers metric emission only…
new-app
Scaffold a new Atomic Agents project from scratch — create the directory, pyproject.toml, env file, first agent, and a runnable entry point. Use when the user asks to start a new atomic-agents project from scratch, says "scaffold" / "new project" / "start from zero", or runs /atomic-agents:new-app.
ax-go-flow
Use when writing Go code with github.com/ax-llm/ax/packages/go for flows, nodes, program graphs, nested programs, dynamic options, caching, and optimizer components.
ax-python-agent
Use when writing Python code with axllm for agents, child delegation, tools, MCP, citations, persistent playbook learning, stage instructions, runtime state, final typed responses, and direct-respond executor skipping.
chat-complex-documents
Chat with and search your complex documents — ask questions, extract tables and fields, and get answers grounded in the source. Connects the hosted Unstructured Transform MCP server to parse, structure, and enrich PDFs, Word/Excel/PowerPoint, images, scanned files, emails, and 60+ other formats into clean, AI-ready…