Agent-first verification for end-to-end API workflows
Don't trust another testing pitch. Ask your agent to compare Glubean with the API workflows, tests, CI, clients, auth, and failure diagnosis already in your repo.
A green run is useful. Proof is what your team can review, trace, and keep.
real endpoints · shared state · structured failure evidence · CI promotion
Repo comparison prompt
Use Glubean skill.
Compare Glubean with our API workflow testing setup.
Look at tests that hit real endpoints, multi-step flows, auth/session, and state.
Tell me where it adds flow evidence, repair, or CI promotion; where our current unit, integration, or API-client tests are already enough; and the smallest end-to-end flow worth trying.
Do not recommend migration unless the benefit is clear.npx skills add glubean/skillNo local agent? Ask the hosted Glubean GPT — skill and docs already loaded.
Open Glubean GPTGreen Is Not Proof
Agents can already write tests and make them pass. The missing layer is what teams can inspect: what was covered, what evidence came back, what changed during repair, and what should become permanent verification.
Agents can generate and run tests now. The hard question is whether they covered auth boundaries, negative paths, state flows, schemas, and business rules.
Pass/fail text is useful for humans, but agents need failed steps, request/response context, traces, and actual-vs-expected values to repair from facts.
If the agent broadens matchers, deletes negative cases, or turns schema checks into status-only tests, the next green result becomes less defensible.
Evidence, Not Logs
A terminal log tells the agent that something failed. A failure object tells it where, why, and what changed. Glubean keeps the request, response, assertion, trace, and actual-vs-expected values as structured evidence the agent can query, group, and repair against.
Raw CI transcript
The agent knows something failed, then has to infer the cause from noisy framework output.
FAIL auth/me.test.ts › returns profile
AssertionError: expected 200, received 401
at auth/me.test.ts:14:23
at processTicksAndRejections (node:internal/process/task_queues:95:5)
Body: {"error":"Unauthorized"}
Headers: {"content-type":"application/json"}
...11 more linesRaw transcript → the agent guesses
Failure event JSON
The same failure becomes readable by people and structured enough for an agent to repair without weakening the test.
{
"status": "failed",
"runId": "clr_8f32",
"testId": "auth.get-me",
"step": "GET /users/me",
"reason": { "kind": "assertion", "expected": 200, "actual": 401 },
"events": [
{ "type": "http.request", "method": "GET", "url": "/users/me" },
{ "type": "http.response", "status": 401, "bodyShape": { "error": "string" } },
{ "type": "assert.failed", "path": "status", "expected": 200, "actual": 401 }
]
}Failure object → the agent knows where, why, and what changed
The Defensibility Loop
Plan defines what "tested" should cover, and Run executes real, multi-step API flows against live endpoints. Promote means only evidence-backed tests become long-term verification the team owns. The middle of the loop keeps repair from weakening the claim.
Agent-written test to defensible verification
One loop connects the plan, evidence, repair, and permanent test.
Plan defines what "tested" must cover before the agent fills in code.
test("checkout-flow")
.step("login", async ({ http }) =>
http.post("/auth/login").json()
)
.step("create-cart", async ({ http }, { token }) =>
http.post("/cart", { json: { token } }).json()
)
.step("apply-promo", async ({ http }, { cartId }) =>
http.post("/cart/promo", {
json: { cartId, code: "AMBER10" }
})
)
.step("checkout", async ({ http, expect }) => {
const order = await http.post("/checkout");
expect(order).toHaveStatus(201);
expect(order.body.webhookDelivered).toBe(true);
});Reviewable test shape
Auth, state flows, negative cases, schemas, and business rules are named before code fills the gaps.
Not a single endpoint check
The first artifact explains what "tested" should mean, then turns it into runnable TypeScript.
The important shift
Generation becomes governable because the test can be inspected and changed.
Starting Slices
The hero prompt compares Glubean with your whole repo. Here, start narrow: pick the one multi-step API flow where defensible evidence would matter most, and let your agent prove a single slice first.
Agent-first quick start
Install the Skill, then ask your agent.
npx skills add glubean/skillFirst, choose what you want your agent to answer.
Start broad, compare familiar tools, inspect your repo, or find one migration slice.
Compare Glubean with familiar API testing tools
Use this when you are deciding between Postman, Vite/Supertest, and Glubean.
Use Glubean skill.
Compare Glubean with Postman, Vite/Supertest, and common API test setups.
Focus on:
1. agent-written tests and how easy they are to audit;
2. failure diagnosis from logs versus structured evidence;
3. CI promotion and long-term ownership;
4. migration cost and when the existing stack is enough.
Give me the tradeoffs, not a sales pitch.Then use the same Skill for the slice worth trying
Point your agent at source, OpenAPI, or live endpoints. Glubean helps produce TypeScript workflows with auth, assertions, and structured failure evidence.
Start from requirements. The agent writes executable contracts before implementation, and reviewers read the generated surface before code lands.
Give the agent the failed step, request, response, assertion, and trace instead of a raw CI transcript.
Shared outcome
Same install
Shared outcome
Same runtime
Shared outcome
Same path to CI and Cloud
Trust Note
What Glubean Is Not
Glubean is not another AI wrapper. It is a verification system for teams that need generated tests to be planned, inspected, repaired, and owned. Keep unit tests where they work; reach for Glubean when API behavior crosses endpoints, auth, state, and CI.