n8n-create-instance-ai-eval

name	n8n:create-instance-ai-eval
description	Authors a new Instance AI workflow eval case — written locally as JSON, calibrated against a real build, then pushed to the LangTracer suite CI runs — build cases, behaviour/process cases, credential cases, and seeded (mid-conversation) cases — with intent-driven expectations. Use when adding or changing an Instance AI workflow eval, or debugging why one is flaky.

Create an Instance AI workflow eval

Each eval is one JSON case — authored locally as a file in packages/@n8n/instance-ai/evaluations/data/workflows/ (the disk loader auto-discovers *.json, no registration step), with a LangTracer suite as its durable home. Cases validate against harness/schema.ts (.strict() — unknown keys fail at load). The eval README is the exhaustive field reference; this skill is the opinionated how.

Committing new case JSONs into the repo is no longer the recommended approach. Author the file locally (uncommitted), calibrate it against a real build, then push it to a lang-tracer suite with eval:langtracer-push (see Push to a lang-tracer suite) — --suite baseline for the consolidated corpus n8n CI runs, or a dedicated capability suite like agents. The suite is the home for the case; the eval CLI reads it back via --source langtracer. You still write the JSON file — it's just the input to the push, not a committed artifact.

Seeded cases. An inline seed pushes with the case — the case-write API stores it verbatim, so the suite is its home like any other case. Only a seed.mode: "replay" case is refused (listed under skipped:): it's reconstructed from a LangSmith trace at run time, so it dies when that trace is pruned and has no durable home. Don't commit a replay case either — derive a synthetic case from it. See case-shapes.md.

Set the autonomy level first

Before you source, draft, or run anything, decide how hands-on the driver wants to be — and say it back. This skill runs at one of two autonomy levels. If the request makes the level clear ("just author and calibrate it yourself" vs. "stop me at each step", or an explicit mode), adopt it, state it in one line, and note how to override (e.g. "say 'stop me at calibration' to add a checkpoint"). If it's not clear, ask the driver one question offering the two levels before doing any work.

The skill has four natural decision gates — selection (which real failure to encode), shape + expectations (archetype, must-haves, scope trim), calibration (classify each red and resolve keep/loosen/drop), and push (kind + tier). The level decides what happens at each gate:

Level	Who decides when to stop	Behaviour
autonomous	agent	Runs all four gates start-to-finish; reports a decision log at the end for the driver to review.
checkpoint	driver, per gate	Stops at each gate with a compact proposal + recommendation; driver says "go" or redirects. At the calibration gate, hands the driver a link to the just-built thread on the live instance plus login credentials so they can review the real conversation and workflow themselves before confirming (below).

Calibration is special-cased at both levels. A calibration verdict that flips a case's meaning — a real capability-gap red vs. a harness-caused red, or any loosening that would let a known-bad build pass — is surfaced explicitly (interactively in checkpoint; in the decision log in autonomous), never silently committed. It's the one call where a quiet mistake corrupts the suite, so it never fully auto-commits.

Checkpoint calibration — review the real thread on the instance. Because the calibration verdict is trust-critical, in checkpoint mode you don't ask the driver to trust your reading of the run. You built the case against a live instance with --keep-workflows (step 4), so the thread and the workflow are still there — hand the driver a direct link and let them look:

• Thread: <base-url>/assistant/<threadId> — the exact conversation the case ran (the run prints the threadId; the built workflow prints as BUILT (<id>) and opens at <base-url>/workflow/<id>).
• Login: the email + password the eval signs in with (the owner you seeded on the instance — see running-evals.md; the default local seed is [e-mail] / PlaywrightTest123).

Present, per red: the assertion, whether it went green/red, your proposed classification (real capability gap / harness limitation / noise) and keep/loosen/drop, and the review link. The driver logs in, reads the thread and the workflow, and confirms or redirects before you write the verdict back into the case description.

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 seed.mode: "replay" 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.

Archetype	Question it answers	Primary fields
Build (default)	Does the workflow the agent builds actually work?	outcomeExpectations + executionScenarios
Behaviour / process	Does the agent converse correctly (ask the right clarifying question, not re-ask, honour a correction, respect plan approval)?	processExpectations + multi-turn director script; often build-only
Credential	Does the build behave correctly given a specific credential view?	credentials[]
Seeded	Start mid-thread, with prior work already in place, and drive the turn under test	seed (authored mode: "inline"; "replay" for a local check)

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").

Write the conversation in English unless the user asked otherwise (or the case exists specifically to test non-English handling). Sourced real threads are frequently non-English — translate the intent into English when you rewrite the prompt in the user's voice; the failure mode is the anchor, not the original language.

Trim to the smallest multi-turn conversation that reproduces the issue. Real sourced threads are long (dozens of turns of setup, debugging, and tangents) — do not transcribe them. Distill to the fewest turns that still drive the build or behaviour under test. Every retained turn must earn its place: a turn stays only if it is load-bearing — a value the agent must ask for (withheld until asked, via a director note), a correction/push-back the case exists to test, or a plan approval that gates the build. If removing a turn doesn't change what's tested, remove it. Collapse to a single turn whenever the whole request can be stated at once without a load-bearing exchange; keep it multi-turn only for those exchanges, and keep each director script in one turn (don't fabricate assistant "done" turns to sequence steps — see case-shapes.md). A minimal conversation isolates the capability; a transcribed one buries it in noise and tests instruction-following.

Size the build, not just the assertions. Real sourced prompts are often kitchen-sink ("production-ready, runs forever, 3 feed posts and 8 stories a day", "generate 50 articles daily") and reliably blow the ~900s build budget (see "Known harness limitations"). A faithful trim is a legitimate authoring move: reduce batch sizes, drop one of several parallel pipelines, or merge adjacent AI steps so the case builds within budget — then note the reduction in the case description ("the original request also asked for an 8-stories/day pipeline; scoped to feed posts so it builds in budget"). Keep the capability under test; cut the combinatorial bulk. A case that never builds tests nothing.

Workflow

These steps map to the four gates from Set the autonomy level first: sourcing (before step 1) is the selection gate; steps 1–2 are the shape + expectations gate; steps 5–6 are the calibration gate; step 7 is the push gate. In autonomous mode you flow through all of them and summarize in a decision log; in checkpoint mode you pause at each with a proposal, and at calibration you hand the driver the thread link + login to review the real build (see Set the autonomy level first). Calibration (step 6) always surfaces meaning-flipping verdicts explicitly regardless of level.

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. Smoke-test the environment with one case before any batch. Run a single case end-to-end first. This validates auth / model / --base-url / the built dist for ~1/Nth the cost — distinct from validating a case. If that one case crashes at execution (especially with an identical error you'd expect to hit every case), fix the environment before running the batch (see "A red is signal" → environment check). Running 15 cases only to discover a stale-dist crash on all of them wastes a full run.
4. Build it once against a running instance (see running-evals.md) with --keep-workflows so the built workflow stays for inspection.
5. 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.
6. 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.
7. Push to the suite — do NOT commit the JSON. Once calibrated, push the case into its curated lang-tracer suite with eval:langtracer-push (see Push to a lang-tracer suite); the suite is the case's home, not the repo. Leave the data/workflows/*.json file uncommitted (or delete it once it's in the suite). Committing new case JSONs into the repo is no longer the approach. (An seed pushes with the case; only a case is refused — it's a local throwaway; see .) For a sourced case, finish by over the MCP — see .

--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.

First rule out the environment. Before reading any red as a signal about a case, check the shape of the failures across the run. If every case fails the same way — every scenario with the same execution error while builds succeed, or every build erroring identically before it starts (an Agent error: Something went wrong… with zero tool calls) — that is almost never the cases; it's a broken environment, most often a stale dist after a branch or worktree switch. Two shapes to know:

• Stale packages/core / packages/cli dist — a refactor moved a runtime export and the built dist still calls the old one, so builds succeed but every execution fails the same way (e.g. (0 , n8n_workflow_1.createDeferredPromise) is not a function after createDeferredPromise moved to @n8n/utils).

• Stale/half-built @n8n/instance-ai dist — every run errors before building (Agent error…, zero tool calls) and the instance log shows Cannot find module '@/utils/...' from dist/skills/*.js: the build's tsc-alias step (which rewrites @/ path aliases to relative requires) didn't complete, so the dist is internally inconsistent.

• Out-of-sync node_modules — pnpm build itself dies early with Cannot find module '@n8n/<pkg>' even though that package is a declared workspace:* dependency and has a dist/. The workspace symlink is missing from the consumer's node_modules (typical after a branch or worktree switch). Confirm with ls -d packages/<consumer>/node_modules/@n8n/<pkg>; fix with a plain pnpm install — no need for the heavier pnpm reset --full.

Fix it, don't calibrate around it: run a full ordered pnpm build (a targeted --filter build can fail on unrelated stale-dep type errors; for the instance-ai shape, cd packages/@n8n/instance-ai && pnpm build runs tsc && tsc-alias), then restart the instance — the running node process holds the old dist in memory, so rebuilding on disk changes nothing until restart (and kill by env-var pattern misses it — kill the actual lsof -t -iTCP:<port> PID). Re-probe one case, confirm it builds and executes, then re-run the batch. Only once uniform environment failures are excluded do the three categories below apply:

• 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.

Annotate every kept red in the case description with a scannable prefix so a future reader tells the two apart at a glance. Use Harness note: … for a harness-caused red (name the limitation and why the build is still correct), and Capability-gap finding: current build reds because <X> — a real builder bug (flips to a regression guard once fixed) for a real gap. Consistent prefixes keep the corpus greppable and stop harness reds from being misread as product bugs.

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.

Who confirms the classification depends on the autonomy level. In checkpoint mode the keep/loosen/drop decision is the driver's to confirm: you hand them the thread link + login (see Set the autonomy level first) so they can review the real conversation and workflow, then you write the agreed Harness note: / Capability-gap finding: prefix back into the case description. In autonomous mode the agent proposes it explicitly in the end-of-run decision log. Either way the classification is stated in the open, never silently committed — misreading a harness red as a real gap (or the reverse) is the one calibration mistake that quietly corrupts the suite.

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": 
       
    
  

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" 
       
   
   
    
    
  
   
    
    
    
  
   
    
       
       
       
       
    
  

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 (in the baseline LangTracer suite) 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. A third caveat: only Data Table reads are seedable this way — dedup / change-detection built on workflow static data (removeItemsSeenInPreviousExecutions, $getWorkflowStaticData) is not seedable, because static data starts empty every run, so such a scenario reds vacuously (it sees everything as "new"). To get a seedable change-detection scenario, steer the build toward a Data Table; otherwise accept the static-data red as a harness limit and carry the logic in outcomeExpectations. Note the agent may choose static-data dedup on its own.
• 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 — the LLM-generated mock response can omit the real envelope, crashing a downstream parse/format node. Recurring, reproducible shapes to expect (all produce a red on a correct build):
  • OpenAI structured output — historically the mock returned a plain {content: "..."} instead of the Responses envelope (output[0].content[0].text), so a Structured Output Parser / Information Extractor / Text Classifier got nothing and errored with Model output doesn't fit required format. The Responses-envelope normalizer (PR #33578, merged) fixes the flat-envelope case, so many of these now execute cleanly. A narrower residual red remains for structured-output schemas declared with strict additionalProperties: false: the normalized output wrapper (and any extra fields the mock invents, e.g. subject/date) violate the strict schema, so the node still rejects the mock output. Both the old and residual forms are the mock, not the build — carry correctness in outcomeExpectations and note the red as harness-caused.
  • Gmail mock returns headers as top-level capitalized fields (From, Subject) instead of under payload.headers, so a Code/Filter node reading the sender/subject gets empty strings (e.g. a "drop no-reply senders" safety gate lets everything through). Assert the of such a gate in , not its runtime effect in a scenario.

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):

Verdict	Assertion	Why
❌ too tight	"Has an HTTP Request node calling flightaware.com"	Vendor was unspecified; a valid AeroDataBox build fails. (If the user had said "scrape FlightAware", this would be correct.)
❌ too tight	"Publishes via HTTP Request nodes"	Pins the transport when a first-party node is the idiomatic path — e.g. the Facebook Graph API node is the correct way to reach the Instagram Graph API, so a valid build using it fails. Assert the capability ("publishes to Instagram via the Graph API, through the Facebook Graph API node or HTTP Request"), not the mechanism.
❌ too loose	"Fetches flight data from somewhere"	A workflow that fetches but never compares passes — doesn't prove change-detection.
✅ right	"Persists the previously-seen status and compares it to the freshly-fetched one"	The defining behaviour; substitution-proof across vendors and storage choices.
✅ right	"Alert is sent only on the change-detected branch, gated by a conditional"	Proves the gate without pinning node or channel.

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. For per-case attribution under concurrency, parse this, not the streamed verbose log — with more than one lane the log lines interleave across cases, so a [scenario] FAIL line in the stream can't be reliably tied to its case. Authoritative fields: testCases[].buildSuccessCount, buildExpectationResultsPerRun[][].{pass,reason}, and scenarios[].runs[].{passed,failureCategory,rootCause,execErrors}.
• 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)"

Push to a lang-tracer suite

Once a case is calibrated, push it (and any others) up into a curated lang-tracer suite instead of committing the JSON. eval:langtracer-push upserts over the REST API: it creates cases missing from the suite, updates ones whose content drifted, leaves the rest unchanged, and never prunes. It's the inverse of --source langtracer (which pulls a suite down).

cd packages/@n8n/instance-ai
# preview first — no writes (use `npx dotenvx`; the bare `dotenvx` binary is usually not on PATH):
npx dotenvx run -f .env.eval -- pnpm eval:langtracer-push --suite baseline --dry-run --changed
# then push (drop --dry-run):
npx dotenvx run -f .env.eval -- pnpm eval:langtracer-push --suite baseline --changed

• Selectors (at least one required — no accidental push-all): positional <slugs...> (exact file slugs), --changed (new/untracked + staged + modified data/workflows/*.json, ideal right after authoring an uncommitted case), --filter/--tier (with --exclude as a modifier).
• Multiple positional slugs? Skip pnpm — call the script directly. pnpm eval:langtracer-push … slugA slugB forwards the slugs as one joined argument ("slugA slugB"), so no case file matches and nothing is pushed. Either use a no-positional selector through pnpm (--changed), or run the script directly so each slug is its own argv: npx dotenvx run -f .env.eval -- npx tsx evaluations/cli/langtracer-push.ts --suite <slug> <slug1> <slug2> ….
• Env: LANGTRACER_URL + LANGTRACER_API_KEY (an lt_ bearer; one key works for MCP + REST) — put them in .env.eval and run under npx dotenvx.
• Options: --set-kind regression|capability_gap (default regression, must match the suite's kind), --contains-user-data (default is synthetic). A case whose build is correct (outcome expectations green) but that carries a currently-red execution scenario from a builder bug is still a regression case — it guards the fix; reserve capability_gap for cases where the build itself is wrong.
• Scenarios sync on update too: PATCH /cases/:id reconciles executionScenarios by name (update in place, insert new, delete missing — lang-tracer #48), so scenario edits re-push like any other field. A lang-tracer deployment predating that change silently ignores the key; if a pushed scenario edit doesn't land, update the scenario in the lang-tracer UI.
• An inline seed pushes with the case: the case-write API stores it verbatim, so a seeded case lives in a suite like any other. Only a replay case is refused — the push lists it under skipped:, because it's reconstructed from a LangSmith trace at run time and dies when that trace is pruned or deleted. Don't commit a replay case either; derive a durable synthetic case as the artifact instead.

Link the pushed case to its source (provenance step — always do this)

A sourced case that isn't linked back to the conversation/finding it encodes is an orphan: six months later nobody can tell what real failure it guards. The push CLI doesn't carry provenance, so after pushing, link the case over the lang-tracer MCP with one update_test_case call on the new case id (the push prints it):

1. sourceThreadId — the source conversation's thread id (plus sourceRunId when the case anchors to one specific run/step within it). This is the DB-level link every by-version rollup, conversation float, and ?sourceThreadId= query joins on — the tags/description below are the human-readable layer on top, not a substitute. The thread must already be imported into lang-tracer (running get_conversation_analysis on it, as the sourcing flow does, is enough); source_kind is derived server-side, and the link is only editable on authored cases — promotion-recorded provenance is immutable.
2. expectedBehavior — the rule the case enforces, one paragraph — and failurePattern — what actually happened in the source thread, with turn references. Copy/adapt these from the analysis's extractedCases entry when the case came from get_conversation_analysis.

Then add_case_tags (additive; targets the LT-side tags array, not evalTags, so nothing round-trips into eval runs): add a capability tag (e.g. instruction-persistence). Tag normalization is aggressive (lowercase, kebab); colon-form tags get silently dropped. And keep the thread id + turn refs in the case description too (the drafter's habit of "Sourced from thread " is the convention) — the description is the only field shown everywhere.

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. Run with --keep-workflows when you want to review a build by hand — in checkpoint mode calibration this is how the driver opens the built thread (<base-url>/assistant/<threadId>) and workflow on the instance.

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.