Create an Instance AI workflow eval

Each eval is one JSON file in packages/@n8n/instance-ai/evaluations/data/workflows/. The loader auto-discovers *.json and validates against schema.ts (.strict() — unknown keys fail at load). No registration step. The eval README is the exhaustive field reference; this skill is the opinionated how.

Where the best cases come from

The strongest cases encode a real failure, not an invented premise. Two connections help you find and verify one: LangTracer clusters real conversations into capability-gap themes (discover what actually fails, at scale), and LangSmith holds the raw traces (verify exactly what happened in a run). LangTracer is the discovery layer; the durable artifact is almost always a synthetic case you author from what you learn (use seedThread only per case-shapes.md). See sourcing-cases.md for connecting the MCPs and the discover → verify → encode workflow.

Pick the case shape first

The corpus is four archetypes. Decide which you're writing before you draft — it determines the fields, the grading, and how you validate. They compose (a seeded case can still assert outcome), but the primary shape drives the work.

Build is documented in full below. The other three, the director-script vocabulary, and the seeding modes are in case-shapes.md.

Core principle (all shapes)

Write expectations from intent, then calibrate against a real build. Decide up front what makes any correct solution correct — the must-haves implied by what the user actually said — then build the workflow once for real to calibrate granularity: loosen what's over-specified, confirm the must-haves are achievable, and catch requirements the agent legitimately satisfies a different way. Don't transcribe one observed build into assertions — that overfits the eval into "did the agent reproduce that run" instead of "did it solve the problem."

Keep the conversation in the user's voice. State the goal and real constraints the way a user would — don't name node types, wire up the structure, or restate your outcomeExpectations in the prompt. If the conversation spells out the build, the case only tests whether the agent can follow instructions and the expectations become tautological; the gap between what the user asks for and how a correct workflow realizes it is the capability under test. Even when the anchor is honoring a user's stated technical preference, phrase it as their need + constraint ("I need field X and the built-in node doesn't expose it, so pull it straight from the API") — not as an implementation spec ("use an HTTP Request node").

Workflow

1. State the must-haves first. From the conversation alone, list what every correct workflow must do (trigger type, essential operations, gating condition) — those become draft outcomeExpectations. Required fields: conversation (≥1 turn, first user), complexity, tags, and at least one of executionScenarios / processExpectations / outcomeExpectations.
2. Draft the case from the template below; validate it loads (see "Validate").
3. Build it once against a running instance (see running-evals.md) with --keep-workflows so the built workflow stays for inspection.
4. Inspect — read the built workflow (the run prints BUILT (<id>); fetch via GET /rest/workflows/<id>) and the HTML report's transcript to see what the agent actually did.
5. Calibrate — sharpen assertions; never dull them to force a green. Fix assertions that are genuinely mis-sized: relax one that pins a choice the conversation left open (so a valid alternative build wrongly fails), tighten one a wrong build would slip past, and phrase executionScenarios to match how the workflow runs on mocked data. But when a scenario goes red because the build has a real gap, or because the harness can't exercise it, that red is the result — keep it and surface why (see "A red is signal", below). Never delete a scenario, weaken an assertion, or drop to build-only just to make the run green.

--iterations N is available to measure flakiness (pass@k / pass^k) — reach for it when you suspect a case is non-deterministic or before promoting it to a gated tier, not as a routine step (each iteration is a full build + execution).

Gut-check: if you can't picture a plausible wrong build that this case reliably turns red, the assertions are too loose to guard anything.

Confirm the precondition fired, not just the green. For any conditional assertion — "when X happened, the agent did Y" (most processExpectations, and any behaviour case) — a pass has two readings: the agent did Y, or X never happened and the assertion passed vacuously. A behaviour case that hinges on the mock producing a specific failure (e.g. an AI node simulated to empty so a downstream parse node fails) is the classic trap: if the mock instead returns parseable data, the failure never occurs and the case guards nothing while showing green. Calibration must read the execution trace and the agent's finalText (buildTrace.finalText in the verifier snapshot, or the HTML report) and verify X actually materialised — the direct-loop eval-results.json does not persist per-expectation judge reasoning, so pass/fail alone can't tell you which reading you got.

A sourced failure that no longer reproduces is still worth keeping — it's now a regression guard. When you encode a real failure and calibration shows the current build handling it correctly (behaviour drifts across versions), the case doesn't lose value: it flips from capability-gap (currently red) to regression guard (currently green, catches a re-introduction). Keep it — but only after the non-vacuous check above proves it would turn red on the bad behaviour, else the "guard" guards nothing.

A red is signal — surface it, don't work around it

Calibration exists to right-size assertions, not to make a case pass. When a run turns a scenario or expectation red, classify the red first — then keep it:

• Real build / capability gap — the agent's workflow is wrong or missing something the user asked for (a miswired branch, a missing retry, wrong field keys). This is exactly what the eval is for. Keep it red. Don't loosen the assertion or drop the scenario; a currently-red gap is the capability signal today, and a re-introduction guard once the builder improves.
• Harness limitation — the build is correct but the mock/execution layer can't exercise the path (see "Known harness limitations", below). Keep the scenario and say so in its description — that this red is harness-caused, not a build defect — so nobody misreads it as a product bug. Keep it out of gated tiers if it hard-fails every run; when the harness gains the capability it starts earning its keep with no re-authoring.
• Genuine non-determinism — the same build flips green/red across runs. This is the only real "noise". Confirm it with --iterations N before calling it flaky, then de-tier and note it; deletion is the last resort.

The one move to never make is working around a red by weakening what the case checks — deleting a failing scenario, loosening an assertion until a wrong build would pass, or quietly converting to build-only. That makes the suite look greener than the product is, which is the opposite of the eval's job: bugs and harness gaps are the deliverable, so highlight them, don't engineer around them. If you catch yourself editing a case so that a known-bad build would now pass, stop.

Example

Minimal build case:

{
  "description": "What this case tests.",
  "conversation": [{ "role": "user", "text": "<the build prompt>" }],
  "complexity": "medium",
  "tags": ["build", "<nodes>", "<concepts>"],
  "triggerType": "schedule",
  "outcomeExpectations": ["<a must-have any correct workflow satisfies>"],
  "executionScenarios": [
    {
      "name": "happy-path",
      "description": "<what this run exercises>",
      "dataSetup": "<what the external services return>",
      "successCriteria": "<observable proof the run succeeded>"
    }
  ]
}

A fuller case with a multi-turn director script (withhold a value until asked, push back on a wrong plan):

{
  "description": "Scheduled GitHub-bugs digest to Slack. Repo and channel are withheld until the agent asks; the plan must filter to the 'bug' label before it's approved.",
  "conversation": [
    { "role": "user", "text": "Every weekday at 9am, fetch this week's open bugs from our GitHub repo and post a short summary to Slack." },
    { "role": "assistant", "text": "Which repo and which Slack channel should I use?" },
    { "role": "user", "text": [
        "[Withhold the repo and channel until the agent asks; then say the repo is 'acme/widgets' and the channel is '#eng-bugs'.",
        "When the agent shows a plan or setup card, reject it unless it filters issues to the 'bug' label — a digest of ALL issues is wrong. Once it filters to bugs, approve.]"
    ] }
  ],
  "messageBudget": 8,
  "complexity": "medium",
  "tags": ["behaviour", "build", "schedule", "http-request", "slack"],
  "triggerType": "schedule",
  "processExpectations": [
    "The agent asked for the repo and Slack channel before building, since the prompt named neither.",
    "The agent's final plan filtered issues to the 'bug' label — if its first attempt didn't, it corrected after the user pushed back rather than summarizing all issues."
  ],
  "outcomeExpectations": [
    "A Schedule Trigger runs the workflow on a recurring weekday-morning cadence.",
    "Open issues are fetched from GitHub (HTTP Request or GitHub node) and filtered to the 'bug' label before the summary is built.",
    "One Slack message summarizing the fetched bugs is posted to the #eng-bugs channel the user gave."
  ],
  "executionScenarios": [
    {
      "name": "posts-bug-digest",
      "description": "Three open bugs are returned; a summary is posted to Slack",
      "dataSetup": "The GitHub issues request returns three open issues labelled 'bug' ('Login 500', 'Timezone off by one', 'CSV export truncates'). The Slack postMessage call returns { \"ok\": true, \"ts\": \"1700000000.0003\" }.",
      "successCriteria": "The run completes without errors and posts one Slack message to #eng-bugs that references the three fetched bug titles."
    }
  ]
}

What each piece is doing:

• conversation[0] is sent to the builder raw. The opening turn is the real prompt — never put a [director note] in it (it would leak verbatim).
• The [bracketed] turn is a director script for the user-proxy — behaviour, never spoken. Here it withholds values until asked and rejects a plan that misses the label filter. Keep the whole script in one turn and encode ordering inside it (don't fabricate assistant "done" turns to sequence steps — see case-shapes.md). applies-each-change-when-asked is a good real example.
• dataSetup describes only what external services return. That's the layer the harness controls (below).

dataSetup and the mock layer

The harness mocks by intercepting outbound HTTP requests to external services and having an LLM answer them from the node's config and API docs. It does not let you set a node's output directly, and it does not mock n8n internals (Code/Set/Merge/IF/Switch run for real on the mocked data; triggers and DB nodes get LLM-generated pin data). So:

• Write dataSetup as what each external service returns ("the GitHub request returns three issues labelled bug"), not as node outputs or internal state.
• The strongest scenarios exercise external-service responses — that's what the harness reproduces most faithfully.
• Data Table reads are pinned to the scenario. A read op (get / rowExists / rowNotExists) is treated as the scenario's "stored state" and pinned with data derived from your dataSetup, so change-detection / dedup / "last seen" scenarios can be exercised — describe the stored rows in dataSetup. Two caveats: the pinned rows are LLM-generated (steered, not byte-exact — don't assert exact values off them), and writes/inserts aren't pinned (they hit the real per-thread table, recreated schema-only with no rows), so read-after-write within one run isn't faithful — the read reflects dataSetup, not what the run just wrote.
• Don't assert exact counts that depend on mock generation ("exactly 7 posts"). Say "fewer than the original 10".

Known harness limitations that turn scenarios red regardless of the build

These produce a reliable red on a correct build. Don't engineer around them — write the scenario for the behaviour you want and note in description that the red is harness-caused (per "A red is signal", above):

• Resource-locator fields left empty for setup (Google Sheets / Drive / Calendar and similar node pickers). The agent legitimately leaves the document/folder/calendar ID blank for the user to pick at setup; the mock substitutes __evalMockResource, and the node then crashes looking it up ("Sheet with ID __evalMockResource not found", or "Cannot read properties of undefined"). Any scenario whose success path runs through such a node hard-fails before anything downstream executes.
• Trigger and Data-Table-read pin data is LLM-generated, so not byte-exact. Both are steered by your dataSetup (see the mock-layer section above — you can influence what a trigger emits or what a stored-row read returns), but because the values are generated, a scenario that asserts exact values or counts off them is flaky. Assert shape/branch/relative facts, not exact figures. (The residual hard red here: polling / form triggers still occasionally fail to load entirely — "workflow not found".)
• Mock response shape — for a less-common API the LLM-generated response can omit the real envelope (e.g. Gemini's top-level candidates), crashing a downstream parse/format node.
• A build can time out and produce no scored result at all — the run reports BUILD FAILED: Run timed out and zero graded expectations. Don't assume "spec too big": the more common cause is a single-prompt case where the agent asks a clarifying ask-user question and the build hangs on the unanswered question until the per-iteration timeout (see case-shapes.md — only confirmations auto-approve). Before treating a timeout as spec size, classify it: read the agent's final response in the report / trace (did it ask a question? flag an infeasibility? or genuinely churn through a huge build?), and re-run the case solo (--concurrency 1) — concurrency makes a stalled build hit the cap and masks the real reason, which a solo run surfaces in seconds. Fix per cause: a clarifying-question stall → author multi-turn with a director note that pre-answers it; a genuine infeasibility → it's an infeasibility/honesty behaviour case (processExpectations), not a build case; a true oversized spec → the timeout is itself a finding, but note it so the zero isn't mistaken for a scored failure.

outcomeExpectations vs processExpectations

Both are natural-language assertions graded by the same Sonnet judge, and each counts as a pass-rate unit. They judge different surfaces:

• outcomeExpectations — the resulting workflow, judged from the workflow JSON. Assert node choices and configuration, connection topology and branch wiring, data/expression references, trigger cadence, gating conditions. They run everywhere, including prebuilt/MCP runs (no transcript needed).
• processExpectations — how the agent behaved during the build, judged from the transcript. Assert clarifying questions asked (or not re-asked), tool-call behaviour, plan/approval handling, batching, honouring a correction, ordering. They need a transcript, so they're skipped in prebuilt/MCP runs.

Rule of thumb: an assertion about the artifact is an outcome expectation; an assertion about the conversation or the agent's choices along the way is a process expectation. A case with no executionScenarios is a valid build-only case, graded by these expectations plus the workflow checks.

Sizing each assertion

Right-size against what the agent was actually told. An assertion is well-sized when every correct build passes it and a wrong or lazy build fails it — and it holds the agent only to what the conversation specified, not to one run's arbitrary choices. Two failure modes:

• Too tight — pins a choice the conversation left open. If the prompt never named a vendor, "calls flightaware.com" fails a valid build that used a different source. But if the conversation specified it, pin it — when the user said "email me via Gmail," "sends via a Gmail node" is correct and required, not too tight.
• Too loose — a non-solution would also pass. "Fetches data from somewhere" passes a workflow that fetches but never compares — it doesn't prove the change-detection the prompt asked for.

Quick check — the substitution test: would a reasonable alternative implementation of what the user asked for still pass? Examples (flight-status case, where the source and channel were left unspecified):

Put intent the conversation only implied (a preferred but unstated channel) in processExpectations, not outcomeExpectations.

Robust design vs harness flakiness

Two different things — keep them apart:

• Robust assertion design (always do this). The agent's unspecified choices vary run to run. Source-agnostic outcomeExpectations for an unspecified source aren't a concession to flakiness — they're the correct assertion.
• Harness limitations (surface them, don't hide them). Some paths hard-fail on a correct build regardless of dataSetup — empty resource-locator fields that crash Sheets/Drive/Calendar nodes, polling triggers failing to load (see "Known harness limitations" above). (State-bearing Data Table reads are no longer in this bucket — they're pinned from dataSetup; only the write path and exact-value assertions stay unreliable.) The fix is to document, not to work around: note the limitation in description and keep a hard-failing scenario out of gated tiers. Only when a scenario flips non-deterministically run to run is it genuine noise worth removing — a scenario that reliably fails for a documented harness reason is a standing record of what the harness can't yet test, and stays.

Negative execution scenarios

Don't stop at the happy path — but only assert graceful handling the prompt actually implied. Most agent-built workflows don't add error handling by default, so "the workflow crashes on bad input" is a legitimate builder finding, not a test-case bug. Where graceful handling is expected, phrase successCriteria as the absence of the wrong action ("no alert is sent", "run completes without error") as much as the presence of the right one: empty / not-found source, source error / timeout, malformed response.

Relationship to the always-on workflow checks

Every successful build is also graded by ~28 always-on binary checks across 7 dimensions (structure, topology, parameter correctness, intent, AI wiring, craftsmanship, security) — binaryChecks/checks/. Those are broad and low-visibility. Writing a targeted expectation for your specific case is still worth it even when a binary check nominally covers it — a named case-level assertion gives far better visibility into this behaviour than one row buried in a 28-check rubric. Don't skip an assertion just because a generic check exists.

When a scenario fails, the verifier tags a failure category (builder_issue, mock_issue, framework_issue, verification_failure, build_failure). Treat it as a hint, not ground truth — we've seen a genuine node misconfiguration tagged mock_issue, and a real mock problem tagged as a build error. Open the HTML report and check the actual execution and the generated workflow before concluding whether the failure is your case, the build, or the harness.

Outputs of a run

• workflow-eval-report.html (in the run's .data/ dir) — the highest-value view: full conversation transcript with tool calls, per-node execution traces, the exact intercepted requests and the mock responses, Phase-1 hints, verifier reasoning, and the workflow-check rubric. Human-oriented; start here when debugging.
• eval-results.json — structured results (the machine-readable artifact; the direct loop produces this even with no LangSmith). Good for an LLM or script to parse.
• eval-pr-comment.md — the rendered PR comment (aggregate + regression comparison), always written.

Validate (before running)

cd packages/@n8n/instance-ai
npx tsx -e "import {loadWorkflowTestCasesWithFiles} from './evaluations/data/workflows/index.ts'; console.log(loadWorkflowTestCasesWithFiles('<slug>')[0].fileSlug)"

Running

You need a running n8n instance with Instance AI enabled and a working sandbox; point the eval at it. The harness runs in three modes — direct loop (no LangSmith; eval-results.json only), LangSmith (also records an experiment

• regression comparison), and prebuilt (--prebuilt-workflows, score existing workflows). Narrow a run with --filter <slug> / --tier <name> / --exclude. See running-evals.md for the run recipes, parallel lanes, tiers, and baselines, and the README for the full flag list.

Other eval harnesses (not this skill)

This skill is for data/workflows/ cases. Three siblings exist with their own data dirs and CLIs: eval:subagent (workflow-build compatibility corpus, binary-check scored), eval:discovery (asserts first-hop tool/dispatch routing, no n8n server), eval:pairwise (head-to-head build comparison vs ai-workflow-builder.ee). Authoring them is out of scope here — see the README sections of the same names.