feat(llm-challenge): rebuild evidence collector by dqn · Pull Request #1225 · tailor-platform/sdk

dqn · 2026-05-23T22:46:28Z

Summary

Rebuild llm-challenge as a small evidence collector for SDK affordance work, based on the new rebuild brief. The tool now records reproducible Codex runs and artifacts without grading, scoring, reference solutions, or trend analysis.

Main changes

Replace the previous challenge/evaluator pipeline with a single pnpm -C llm-challenge challenge run command that discovers problems, packs SDK refs, applies profiles, prepares workspaces, runs Codex in Podman, and writes report.json.
Add the 19 prompt/scaffold-only problems across the sdk-api and cli groups.
Add focused tests for argument parsing, problem discovery and filtering, no-docs profile filtering, report and artifact paths, and workspace preparation.
Harden the runner and reports around verification evidence, artifact summaries, no-docs solves, pnpm store reuse, and cleanup.
Add llm-challenge agent skill documentation for creating problems, API proposal reporting, and A/B testing workflows.

Notes

No changeset is included because this is development tooling for evidence collection, not SDK package behavior.

changeset-bot · 2026-05-23T22:46:33Z

⚠️ No Changeset found

Latest commit: a82e233

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github-actions · 2026-05-23T22:46:38Z

⚡ pkg.pr.new

@tailor-platform/sdk

pnpm add https://pkg.pr.new/@tailor-platform/sdk@a82e233

pnpm dlx https://pkg.pr.new/@tailor-platform/sdk@a82e233 --help

@tailor-platform/create-sdk

pnpm add https://pkg.pr.new/@tailor-platform/create-sdk@a82e233

pnpm dlx https://pkg.pr.new/@tailor-platform/create-sdk@a82e233 my-app

commit: a82e233

Each run was creating its own pnpm store under results/<runId>/.shared/pnpm-store and never deleting the per-problem node_modules unless --prune-workspace-deps was passed, leaving tens of GB on disk after a handful of runs. - Persist the pnpm store at llm-challenge/.cache/pnpm-store so packages are hardlinked across runs instead of being re-downloaded per run. - Wrap each problem's task body in try/finally so pruneWorkspaceDeps runs even when the solver fails or is interrupted; prune errors are logged so they can't mask the original failure. - Flip the default to on and rename the flag to --no-prune-workspace-deps so debugging is opt-in.

dqn · 2026-05-31T04:17:56Z

llm-challenge artifact analysis: SDK/API improvement signals

Scope: this comment lists API affordance candidates, not SDK usage mistakes. The observations are based on trace/work evidence from the latest llm-challenge artifacts, but local artifact paths are intentionally omitted. In the examples, Before means current correct SDK/API usage or workaround; After means an illustrative public SDK/API affordance candidate, not an implementation contract.

Area	Observed signal	Improvement proposal
Resolver Context normalization	The resolver-context solution had to add manual helpers for caller IDs, anonymous users, invoker presence, and runtime string values before the output matched the declared enum/string shape. Typecheck also caught raw `string` values being returned where narrower output enum values were expected.	Add a resolver context normalization helper for common caller/invoker/env summaries.
Resolver output schema builder discoverability	The structured-result solutions had to discover `t.object`, `t.enum`, `.typeName()`, and `t.output<typeof schema>`. Final code varied between bare output object literals and named `t.object(...).typeName(...)` schemas.	Add a more discoverable resolver output builder that keeps schema naming and result-type inference together.
TailorDB required/unique/enum field intent	The field-options solutions relied on SDK defaults and chained calls such as `db.string().unique()` and `db.enum(...)`. The task required human-visible required, unique, and constrained-choice intent, but required-ness is implicit unless the author already knows the default.	Add explicit field intent helpers or aliases so required/unique/enum intent is visible in code.
TailorDB relation naming	The relation-naming solutions used the low-level relation object with `type`, `toward.type`, `toward.as`, and `backward` to express a common belongs-to / has-many relation.	Add relation helpers for common cardinalities that make forward and backward names first-class.
Workflow job chaining	The workflow solutions expressed linear dependency by hand with an orchestrator job or nested `trigger()` calls between jobs. The ordering was correct, but the dependency graph lived inside arbitrary job bodies.	Add a workflow pipeline/sequence helper for simple linear job chains.
Workflow wait point resolution	The approval workflow solutions used `defineWaitPoint`, `wait`, and `resolve`, then hand-wired a resolver or executor with duplicate input parsing and output shape.	Add a wait-point resume/resolver helper that derives the resolver/executor shape from the wait point payload/result types.

Resolver Context normalization

Before:

type CallerType = "anonymous" | "user" | "machine_user";
type InvokerType = "none" | "user" | "machine_user";

function presentId(id: string | undefined): string | null {
  return id && id !== anonymousId ? id : null;
}

function callerType(type: string): CallerType {
  return type === "user" || type === "machine_user" ? type : "anonymous";
}

function invokerType(type: string | undefined): InvokerType {
  return type === "user" || type === "machine_user" ? type : "none";
}

body: ({ user, invoker, env }) => ({
  caller: {
    id: presentId(user.id),
    type: callerType(user.type),
    workspaceId: presentId(user.workspaceId),
  },
  request: {
    hasInvoker: invoker != null,
    invokerId: invoker?.id ?? null,
    invokerType: invokerType(invoker?.type),
  },
  environment: {
    summaryLabel: String(env.SUMMARY_LABEL ?? "unset"),
  },
})

After:

body: ({ user, invoker, env }) => {
  const context = resolverContext({ user, invoker, env });

  return {
    caller: context.callerSummary(),
    request: context.invokerSummary({ noneType: "none" }),
    environment: {
      summaryLabel: context.env.string("SUMMARY_LABEL", "unset"),
    },
  };
}

Resolver output schema builder discoverability

Before:

const inventoryDashboardOutput = t.object({
  summary: t
    .object({
      totalItems: t.int(),
      lowStockItems: t.int(),
      reorderNeeded: t.bool(),
    })
    .typeName("InventoryDashboardSummary"),
  items: t
    .object(
      {
        itemId: t.string(),
        sku: t.string(),
        stockStatus: t.enum(["inStock", "lowStock", "outOfStock"]),
      },
      { array: true },
    )
    .typeName("InventoryDashboardItemRow"),
}).typeName("InventoryDashboardResult");

export type InventoryDashboardResult = t.output<typeof inventoryDashboardOutput>;

After:

const inventoryDashboardOutput = resolverOutput("InventoryDashboardResult", (schema) => ({
  summary: schema.object("InventoryDashboardSummary", {
    totalItems: schema.int(),
    lowStockItems: schema.int(),
    reorderNeeded: schema.bool(),
  }),
  items: schema.arrayOf("InventoryDashboardItemRow", {
    itemId: schema.string(),
    sku: schema.string(),
    stockStatus: schema.enum("InventoryStockStatus", [
      "inStock",
      "lowStock",
      "outOfStock",
    ]),
  }),
}));

export type InventoryDashboardResult = typeof inventoryDashboardOutput.Output;

TailorDB required/unique/enum field intent

Before:

export const CustomerAccount = db.type("CustomerAccount", {
  customerId: db.uuid().unique(),
  contactEmail: db.string().unique(),
  displayName: db.string(),
  accountTier: db.enum([
    { value: "free", description: "Free account" },
    { value: "pro", description: "Professional account" },
    { value: "enterprise", description: "Enterprise account" },
  ]),
  signupTime: db.datetime(),
});

After:

export const CustomerAccount = db.type("CustomerAccount", {
  customerId: db.required.uuid().unique(),
  contactEmail: db.required.email().unique(),
  displayName: db.required.string(),
  accountTier: db.required.enum("AccountTier", [
    ["free", "Free account"],
    ["pro", "Professional account"],
    ["enterprise", "Enterprise account"],
  ]),
  signupTime: db.required.datetime(),
});

TailorDB relation naming

Before:

export const SalesOrder = db.type("SalesOrder", {
  orderNumber: db.string().unique(),
  customerId: db.uuid().relation({
    type: "manyToOne",
    toward: {
      type: Customer,
      as: "customer",
    },
    backward: "salesOrders",
  }),
});

After:

export const SalesOrder = db.type("SalesOrder", {
  orderNumber: db.required.string().unique(),
  customer: db.belongsTo(Customer, {
    foreignKey: "customerId",
    backref: "salesOrders",
  }),
});

Workflow job chaining

Before:

export const fulfillOrder = createWorkflowJob({
  name: "fulfill-order",
  body: async (order: OrderRequest): Promise<ConfirmationReceipt> => {
    const validatedOrder = await validateOrder.trigger(order);
    const reservation = await reserveInventory.trigger(validatedOrder);
    return await sendConfirmation.trigger(reservation);
  },
});

export default createWorkflow({
  name: "order-fulfillment",
  mainJob: fulfillOrder,
});

After:

export default createWorkflowPipeline({
  name: "order-fulfillment",
  input: orderRequest,
  steps: [
    validateOrder,
    reserveInventory,
    sendConfirmation,
  ],
});

Workflow wait point resolution

Before:

export const approvalGate = defineWaitPoint<ApprovalRequest, ApprovalDecision>(
  "approval-gate",
);

export const requestApproval = createWorkflowJob({
  name: "request-approval",
  body: async (input: ApprovalRequest) => {
    const decision = await approvalGate.wait(input);
    return await sendFinalNotification.trigger({ ...input, ...decision });
  },
});

export const submitApprovalDecision = createResolver({
  operation: "mutation",
  name: "submitApprovalDecision",
  input: {
    executionId: t.string(),
    approved: t.bool(),
    reviewer: t.string(),
    notes: t.string({ optional: true }),
  },
  body: async ({ input }) => {
    await approvalGate.resolve(input.executionId, () => ({
      approved: input.approved,
      reviewer: input.reviewer,
      notes: input.notes ?? null,
    }));
    return { resumed: true };
  },
});

After:

export const approvalGate = defineWaitPoint({
  name: "approval-gate",
  payload: approvalRequest,
  result: approvalDecision,
});

export const requestApproval = createWorkflowJob({
  name: "request-approval",
  body: async (input: ApprovalRequest) => {
    const decision = await approvalGate.wait(input);
    return await sendFinalNotification.trigger({ ...input, ...decision });
  },
});

export const submitApprovalDecision = approvalGate.createResolver({
  name: "submitApprovalDecision",
  operation: "mutation",
  mapInput: ({ approved, reviewer, notes }) => ({ approved, reviewer, notes }),
  output: { resumed: t.bool() },
});

dqn · 2026-05-31T09:30:07Z

A/B tested the SDK API affordance candidates with llm-challenge.

These are experiment results only; this PR now keeps SDK implementation changes out of the branch and contains llm-challenge changes. I am omitting local-only refs and paths from the PR comment.

success is the challenge success count. duration is average run duration. steps counts trace.jsonl item.completed events. Usage-limit zero-step rows were excluded and affected variants were rerun.

Area	Baseline	After	Delta
Resolver Context normalization	3/3, 270.4s, 69.0 steps	3/3, 212.0s, 58.3 steps	-58.4s, -10.7 steps
`resolverOutput`	3/3, 290.5s, 76.0 steps	3/3, 272.8s, 74.7 steps	-17.7s, -1.3 steps
TailorDB field helpers	3/3, 271.8s, 68.7 steps	3/3, 274.6s, 76.7 steps	+2.8s, +8.0 steps
`db.belongsTo`	3/3, 286.3s, 81.7 steps	3/3, 306.4s, 76.0 steps	+20.1s, -5.7 steps
`createWorkflowPipeline`	2/3, 228.2s, 58.7 steps	3/3, 226.2s, 53.0 steps	+1 success, -2.0s, -5.7 steps
wait point resolver helper	3/3, 429.9s, 88.3 steps	3/3, 375.4s, 88.3 steps	-54.5s, ±0 steps

Caveat: after artifacts showed full adoption for Resolver Context normalization, resolverOutput, TailorDB field helpers, db.belongsTo, and createWorkflowPipeline; the wait point createResolver helper was adopted in 1/3 after runs.

Note: these SDK/API affordance candidates were prototyped only in a throwaway A/B worktree — none of them are included in this PR. Whether to adopt any of them is a separate decision still to be made.

…design-reset # Conflicts: # llm-challenge/package.json # pnpm-lock.yaml

Extract the helpers that were copy-pasted across the runner modules into shared modules so they cannot drift: - src/utils.ts: toPosix, isObject, tailText, pathExists, pathExistsSync - src/workspace-files.ts: the workspace file walker and its exclude sets Fold runner.ts's private spawn-to-buffer helper into process.ts via a rejectOnNonZero option, drop the dead Problem.absolutePath field, collapse the stripDeclarationJsDoc pass-through wrapper, derive the artifact summary's command lists from a single terminal-command pass, hoist the glob regex out of the verification match loops, and reuse the rerun-runs report type instead of redeclaring it.

Repacking the SDK tarball with macOS `tar` stores xattrs/AppleDouble (`._*`, LIBARCHIVE.xattr) entries, which leak into the no-docs profile and add measurement noise when solvers inspect the package. Set COPYFILE_DISABLE for the extract and repack so the host tar omits them; GNU tar ignores it.

The minMatches field counts glob-matching files that contain the pattern, not regex occurrences, but the name and the "matches:" observation label read as occurrence counts. Document the file-counting semantics on the field and relabel the observation to "matchedFiles:" so spec authors are not misled. No behavior change: every shipped verify.json uses the default of 1, where file-count and occurrence-count are equivalent.

The A/B workflow recorded `success` as "the challenge report run result" and contemplated solverExitCode=0 yet success=false, but report.json has no `success` field, so the value could not be derived mechanically. Spell out the computation from existing artifacts — solver completion in report.json plus no unsatisfied/error check in the run's verification-summary.json — so the recorded value matches reality without changing the report schema.

With concurrency >= 2 each worker wrote the shared report snapshot independently, so a slow older write could land after a newer one and leave report.json missing completed runs if the process was interrupted before the final reconciling write — breaking later analysis and --rerun-nonzero-from. Chain the writes through a single promise; since report only grows, the on-disk file stays monotonic and never regresses to a stale snapshot.

…design-reset Resolve the pnpm-lock.yaml conflict by aligning llm-challenge's oxlint and oxlint-tsgolint to the workspace versions main bumped them to (1.66.0 / 0.23.0), then regenerate the lockfile so the hoisted linker resolves a single oxlint version. llm-challenge lint/typecheck/test pass on oxlint 1.66.0.

github-actions · 2026-06-01T04:50:00Z

Code Metrics Report (packages/sdk)

	main (aa19bc1)	#1225 (4882758)	+/-
Coverage	64.2%	64.2%	0.0%
Code to Test Ratio	1:0.4	1:0.4	0.0

Details

  |                    | main (aa19bc1) | #1225 (4882758) | +/-  |
  |--------------------|----------------|-----------------|------|
  | Coverage           |          64.2% |           64.2% | 0.0% |
  |   Files            |            377 |             377 |    0 |
  |   Lines            |          13114 |           13114 |    0 |
  |   Covered          |           8428 |            8428 |    0 |
  | Code to Test Ratio |          1:0.4 |           1:0.4 |  0.0 |
  |   Code             |          87299 |           87299 |    0 |
  |   Test             |          37390 |           37390 |    0 |

SDK Configure Bundle Size

	main (aa19bc1)	#1225 (4882758)	+/-
configure-index-size	18KB	18KB	0KB
dependency-chunks-size	33.52KB	33.52KB	0KB
total-bundle-size	51.51KB	51.51KB	0KB

Runtime Performance

	main (aa19bc1)	#1225 (4882758)	+/-
Generate Median	2,809ms	2,802ms	-7ms
Generate Max	2,913ms	2,840ms	-73ms
Apply Build Median	2,835ms	2,839ms	4ms
Apply Build Max	2,905ms	2,891ms	-14ms

Type Performance (instantiations)

	main (aa19bc1)	#1225 (4882758)
tailordb-basic	35,133	35,133
tailordb-optional	3,841	3,841
tailordb-relation	7,428	7,428
tailordb-validate	2,566	2,566
tailordb-hooks	5,767	5,767
tailordb-object	12,136	12,136
tailordb-enum	2,462	2,462
resolver-basic	9,424	9,424
resolver-nested	26,111	26,111
resolver-array	18,187	18,187
executor-schedule	4,234	4,234
executor-webhook	873	873
executor-record	8,166	8,166
executor-resolver	4,369	4,369
executor-operation-function	868	868
executor-operation-gql	869	869
executor-operation-webhook	888	888
executor-operation-workflow	1,714	1,714

Reported by octocov

toiroakr

Let's put it in for now.

dqn · 2026-06-03T07:12:47Z

LLM challenge measurement: TailorDB fluent API vs descriptor API

I added paired TailorDB schema problems to compare the current fluent API with the PR #905 descriptor/createTable API and ran 3 attempts for each style.

Context:

SDK ref: PR feat(tailordb,resolver)!: object-literal descriptor API and record-level hooks/validate #905 head (origin/feat/object-literal-descriptor-api, 532df7f)
Problems: same catalog-schema task, with prompts constrained to each API style
Runner note: the local Podman preflight failed, so this was measured with the host runner path while reusing the llm-challenge workspace setup, verification, report schema, and trace artifacts
Success means: solver exit code 0, not timed out, and no unsatisfied/error verification checks

API style	Runs	Successful	Avg duration	Avg steps	Avg output tokens	Main API-specific observation
Fluent API (`db.type`, `db.*`)	3	3/3	262.2s	67.0	9,397	No API-specific misconception observed. Runs converged on `db.type`, `db.decimal({ scale: 2 })`, `db.uuid().relation(...)`, and `db.fields.timestamps()`.
Descriptor API (`createTable`, field descriptors)	3	3/3	287.8s	67.0	10,148	The main friction was relation-field shape discovery. Solvers looked for/considered a relation descriptor shape, but the correct descriptor usage is a UUID field with a `relation` property, e.g. `{ kind: "uuid", relation: ... }`.

Observed delta, descriptor minus fluent:

Metric	Delta
Successful runs	+0
Avg duration	+25.6s
Avg steps	+0.0
Avg output tokens	+751

Notes:

Both API styles produced final artifacts that satisfied the checks.
The descriptor-specific issue was an intermediate discovery cost rather than a remaining incorrect final answer.
Non-SDK noise seen in traces included docs lookup misses and CLI-name exploration; I did not count those as API-specific mistakes.

dqn added 3 commits May 24, 2026 07:09

docs: reset llm challenge design

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docs: split challenge problem groups

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feat(llm-challenge): rebuild evidence collector

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dqn added 7 commits May 24, 2026 07:54

fix(llm-challenge): support unit test project

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fix(llm-challenge): harden challenge runner

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fix(llm-challenge): seal no-docs artifacts

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fix(llm-challenge): use bash entrypoint for codex

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fix(llm-challenge): approve generated workspace builds

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fix(llm-challenge): validate problem filters

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fix(llm-challenge): install codex in runner image

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dqn added 3 commits May 25, 2026 07:00

docs(llm-challenge): add agent skill

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fix(llm-challenge): reduce pnpm install churn

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feat(llm-challenge): record verification evidence

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fix(llm-challenge): tighten verifier checks

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dqn added 6 commits May 25, 2026 21:51

fix(llm-challenge): harden runner cleanup

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fix(llm-challenge): strip runtime jsdoc in no-docs profile

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fix(llm-challenge): harden workspace verification

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fix(llm-challenge): dedupe command summary events

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fix(llm-challenge): tighten verification evidence

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fix(llm-challenge): configure pnpm shared store

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dqn added 2 commits May 31, 2026 13:02

docs(llm-challenge): add API proposal analysis guidance

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docs(llm-challenge): clarify API proposal before after semantics

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dqn added 7 commits May 31, 2026 13:41

docs(llm-challenge): clarify API proposal reporting

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docs(llm-challenge): split problem creation guide

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feat(sdk): add resolver context helper

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docs(llm-challenge): document A/B testing workflow

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docs(llm-challenge): keep A/B reporting focused

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docs(llm-challenge): allow worktree A/B commits

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docs(llm-challenge): isolate A/B test commits

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Merge remote-tracking branch 'origin/main' into docs/llm-challenge-re…

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…design-reset # Conflicts: # llm-challenge/package.json # pnpm-lock.yaml

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dqn marked this pull request as ready for review June 1, 2026 04:46

dqn requested review from remiposo and toiroakr as code owners June 1, 2026 04:46

toiroakr approved these changes Jun 1, 2026

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toiroakr merged commit 8a67af2 into main Jun 1, 2026
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toiroakr deleted the docs/llm-challenge-redesign-reset branch June 1, 2026 06:09

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feat(llm-challenge): rebuild evidence collector#1225

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Summary

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⚡ pkg.pr.new

@tailor-platform/sdk

@tailor-platform/create-sdk

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llm-challenge artifact analysis: SDK/API improvement signals

Resolver Context normalization

Resolver output schema builder discoverability

TailorDB required/unique/enum field intent

TailorDB relation naming

Workflow job chaining

Workflow wait point resolution

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Code Metrics Report (packages/sdk)

SDK Configure Bundle Size

Runtime Performance

Type Performance (instantiations)

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dqn commented Jun 3, 2026

LLM challenge measurement: TailorDB fluent API vs descriptor API

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