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* feat: add a restricted and supervised Claude Code runner Preserve configured authentication and models while enforcing tool access, strict completion JSON, bounded output and process cleanup. Cover argv, failure handling, session metadata and Windows process containment. * feat: route outside reviews by harness and migrate wrapper installs Use Claude Code from Codex and Codex from other supported hosts, with shared invocation rendering, positive gate validation and per-phase provenance. Rename /claude to /claude-code, repair managed shared and copied installations safely, and generate native Kiro skills. Add installed-workflow, failure-injection and live cross-harness regression coverage. * test: recognize CEO mode labels without terminal spacing The paid workflow rendered SCOPEEXPANSION at option 4, but its driver required a literal space. Match the leading mode title without cursor-spacing artifacts and ignore adjacent preview text. Preserve missing-target failures and downstream posture assertions. * test: isolate plan-count fixtures before starting review workflows Seed the complete test plan in a private git repository before launching Claude, so a bare slash command cannot review the live workspace while a delayed fixture message remains queued. Preserve count thresholds, parsers and budgets. Add initial-context and installed-discovery tests, and retain startup/terminal diagnostics on failed evaluations. * test: stabilize review fixtures and Claude eval startup Preserve source boundaries in workflow judge inputs, isolate CEO mode plans, and wait for interactive trust input readiness. Keep startup failure evidence and retain existing models, budgets, and assertions. Co-Authored-By: OpenAI Codex <noreply@openai.com> * test: classify collapsed review modes and isolate seeded findings Keep review questions out of the setup count when terminal cursor positioning removes spaces. State existing webhook safeguards so the five-finding control measures its seeded defects without accidental extra security and concurrency gaps. Preserve question bands and the paired control. Co-Authored-By: OpenAI Codex <noreply@openai.com> * test: isolate browser daemon state across free shards Co-Authored-By: OpenAI Codex <noreply@openai.com> * test: stabilize native review counting and interactive navigation Co-Authored-By: OpenAI Codex <noreply@openai.com> * chore: prepare v1.82.0.0 release Co-Authored-By: OpenAI Codex <noreply@openai.com> * fix: eliminate browser and process-cleanup test flakes Pin every CI surface to Bun 1.4.0 to avoid extra-stdio finalizers closing reused live sockets. Add an isolated GC/listener regression that fails on Bun 1.3.13, and prevent coordinated rollback to an affected CI runtime. Check renderer cleanup against the render's own staging directory so concurrent renders cannot invalidate the assertion. Make the no-pgrep process-tree walk tolerate disappearing /proc entries, and synchronize its test fixture through child readiness and pipe EOF instead of sleeps. Validation: 9,157 passed, 31 skipped, zero failures across 556 files with retries disabled. Build, all-host generation freshness, and skill checks passed. All three races have failing-before/passing-after regressions. * fix: count completed native review questions in evals * fix: drive review navigation from confirmed native choices * fix: require complete section-loading eval reports * test: isolate telemetry HTTP transport from local assertions * fix: keep review input on the active native question * test: let tunnel revocation daemon choose an available port * test: allocate available ports for pairing and watchdog fixtures * fix: stabilize planning eval navigation and phase reporting * test: isolate installed runtime paths in planning evals * test: stabilize review evidence and concurrent refresh fixtures * fix: resolve design findings before editing the plan * fix: honor and persist disabled outside plan reviews * fix: preserve planning decisions and terminal evidence Load installed host reviews at autoplan phase entry and wait for completed reviewers and saved artifacts. Reuse approved remedies while preserving individual finding decisions. Drive interactive evals from the current terminal viewport, bind native questions across scrolling, and require complete native report evidence. Cover captured stale menus, permission lifecycles, setup classification, and disabled-review tool availability with deterministic regressions. Advance release metadata and the upgrade migration to the unclaimed 1.83.0.0 slot. * fix: drive native review questions and preserve current plans Use the native single-choice keyboard protocol and current terminal viewport, with per-question navigation inside packets and completed-call coverage. Keep permissions, multi-select menus, and Submit controls distinct. Send Autoplan reviewers the amended implementation plan, keep its review record separate, and supply retained application contracts in the chain fixture. Clarify individual DevEx decisions and complete CEO fix options; use one active plan destination for the section-loading report. * fix: preserve complete plan-review decisions * fix: recognize native plan dialogs and reviewer controls * fix: preserve review decisions and phase completion * fix: recognize completed reviews without losing findings * fix: preserve review continuity and native eval completion * test: fix native review completion and eval retry isolation * test: handle native review menus and complete eval fixtures * test: fix native review setup, completion, and isolation failures * test: limit native skill discovery to runtime assets * fix: bind Autoplan reviews to full ordered phase inputs * test: fix planning eval routing, counting, and timeout handling * chore: advance queued release to v1.84.0.0 * fix: preserve complete review inputs and planning decisions * fix: reconcile review approvals and preserve phase obligations * fix: preserve review obligations and unblock eval permissions Carry recorded Autoplan requirements into blind phase inputs, require Eng review approvals before exit, and exercise combined asynchronous flows in CEO reviews. Correct native finding and handoff classification and unblock repeated report edits using scoped request identities. * fix: retain plan requirements and complete native review dialogs * fix: complete native review prompts and retain plan references * fix: preserve review inputs and classify native eval evidence * fix: check competing completion orders in CEO reviews * fix: recognize review decisions and require phase methodology Require the current phase methodology before Autoplan snapshots. Correct substantive decision, closed handoff, and cache-finding classification, and honor the recommended implementation approach in native review dialogs. Add captured-transcript regressions without changing review thresholds, provider models, retries, or deadlines. * test: bind native review decisions and close completed handoffs * fix: complete review dialogs and verify methodology delivery * fix: preserve review evidence and unblock native eval prompts * fix: handle native review question completions * fix: recognize native review narration and controls * fix: count native review decisions and isolate eval fixtures * test: verify seeded review coverage and current artifact permissions * test: isolate model and brain-aware skill renders * fix: repair native workflow evaluation and clarify review steps * fix: stabilize workflow eval evidence and review guidance * test: repair native workflow observation and fixture isolation * fix: recognize completed workflow evidence and owned skill reads * test: repair seeded workflow delivery and completion evidence * test: recognize current review evidence across native forms * test: handle native review variants and permission redraws * fix: honor review preferences and recognize native eval evidence * test: recognize completed review decisions and queued permissions * test: match current review contracts and partial-line edits * test: recognize completed workflow evidence and bounded human waits * fix: preserve review entry gates and native eval interactions * fix: recognize native workflow evidence and preserve review gates * test: recognize current review evidence and preconfigure workflow fixtures * test: recognize completed review findings and scoped artifact permissions * fix: stabilize native workflow review and permission evidence * fix: recognize current review evidence and scoped edit confirmations Clarify Design and engineering review entry instructions and Design scoring. Recognize required legacy coverage and public Autoplan completion recaps. Bind the pending Edit confirmation to its exact file, ordered digest, and one-request approval when a preceding command display remains visible. Keep reviews within their existing size limits and preserve scope gates when extracting workflow fixtures from either supported preamble header. Keep failure outcomes, review thresholds, provider choices, and eval budgets. * fix: recover review workflow progress and eval evidence * fix: recognize valid review evidence and scope selection * test: fix review evidence parsing and repeated artifact prompts * test: recognize valid review decisions and pending native cards * fix(plan-eng-review): keep final navigation consistent with approved tasks * test: recognize valid review evidence and bind legacy diff requests * fix: stabilize review eval evidence and harness repair guidance * docs: update project documentation for v1.85.0.0 Co-Authored-By: OpenAI Codex <noreply@openai.com> * test: fix Windows CI fixtures and credential scan Rebase captured JSON values and filesystem evidence using the appropriate path convention. Compile native fake CLIs on Windows and synchronize pipe holder readiness, with cleanup retained when assertions fail. Assemble synthetic credential fixtures at runtime so the added-line scan keeps enforcing the same gate without flagging its own rejection controls. Discover generated skills directly for the empty-find regression check, avoiding a recursive scan through saved evaluation artifacts and dependencies. * fix: preserve source renders on Windows Compare canonical generator paths using native separators so an output sidecar pointing at the source cannot overwrite its skill or metadata. Keep the regression fixture isolated from the real checkout and expose freshness diagnostics before asserting subprocess status. Detach Windows drain-test pipe holders from the fake provider's automatic child cleanup while preserving the enclosing runner job and its assertions. * fix: clarify outside review fallback and CEO decisions Render one applicable own-harness fallback path and retain native review, disabled policy, and missing-coverage semantics. Align report field names and mode labels, and make the existing per-cut scope approval explicit. Regenerate skill outputs and keep the workflow judge's model, thresholds, and retry policy unchanged. * chore: move release to free version slot (v1.86.0.0) PR #2852 now claims v1.85.0.0. Align the release metadata and rename migration so upgrades from that version still receive it. Co-Authored-By: OpenAI Codex <noreply@openai.com> * fix: include engineering review prerequisites and restore branch context * fix: recognize coverage diagrams and clarify design review instructions * fix: preserve file identities and join Windows test processes --------- Co-authored-by: OpenAI Codex <noreply@openai.com>
205 lines
26 KiB
JSON
205 lines
26 KiB
JSON
{
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"provenance": {
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"sourceHead": "f26d569e0345cb1131d9ca52d4a43965085c3468",
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"sourceObservationSha256": "19fe5cb03390e626a239cfea2a257477343d4c63cba13405348bb61e1b6b3107",
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"publicNativeProofSha256": "df6a8eabe49dcdd1743abedccf891cc631456c4989a63758bab4fdccf12dcca9",
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"historicalOutcome": "plan_ready; local-ci-gate, missing-quickstart and reversed-arguments seed predicates failed",
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"paidOutcomesReclassified": false,
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"scope": "Exact completed first-attempt native questions, options, answers and acknowledgment times only; pending/retry calls are not included."
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},
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"calls": [
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{
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"sessionId": "8c7b28d3-987f-4387-81fb-ea3703c6cac4",
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"toolUseId": "toolu_01TUskvznLiAKducCZebGFeH",
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"questions": [
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{
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"question": "gstack works best when your project's CLAUDE.md includes skill routing rules. Add them? (One-time prompt per project. Note: plan mode is active, so if you pick A the edit and commit happen after this review exits plan mode.)",
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"header": "Routing",
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"multiSelect": false,
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"options": [
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{
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"label": "Add routing rules to CLAUDE.md (recommended)",
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"description": "Append a '## Skill routing' section mapping request types to gstack skills, then commit it. Applied after plan mode exits."
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},
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{
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"label": "No thanks, I'll invoke skills manually",
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"description": "Record routing_declined=true so this prompt does not recur for this project. Re-enable later via gstack-config."
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}
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]
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}
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],
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"answered": true,
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"failed": false,
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"answers": {
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"gstack works best when your project's CLAUDE.md includes skill routing rules. Add them? (One-time prompt per project. Note: plan mode is active, so if you pick A the edit and commit happen after this review exits plan mode.)": "Add routing rules to CLAUDE.md (recommended)"
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},
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"unansweredQuestionIndices": [],
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"answeredAt": "2026-09-10T18:02:43.124Z"
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},
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{
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"sessionId": "8c7b28d3-987f-4387-81fb-ea3703c6cac4",
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"toolUseId": "toolu_01RMPrDBdVYuxNKXQCCD4sY3",
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"questions": [
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{
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"question": "D1 — Mandatory 5-minute remote CI check before the first local result\nProject/branch/task: EvalKit SDK beta on main; DX POLISH review of the first-run contract.\nELI10: Today the very first evaluation, including the keyless `python -m evalkit.demo` that uses a mock transport and bundled data, cannot return until a remote CI check succeeds, and that check blocks for up to 300 seconds with no skip or offline path (docs/current-contracts.md lines 3-5). The onboarding study clocks EvalKit at 6 minutes, of which 5 is this wait; the agreed target is under 2 minutes and the fastest peer takes 2. The persona explicitly wants a local result BEFORE connecting to production CI, so the gate sits on the exact moment the demo is supposed to feel like magic.\nStakes if we pick wrong: the magical moment arrives five minutes late, a keyless demo fails outright on machines without network or CI access, and EvalKit stays last in its own benchmark.\nRecommendation: A because the persona's stated need is local-first, and a mock-transport demo has no reason to depend on remote CI; the existing progress and EVALKIT_CI_TIMEOUT messaging stays intact for the step where CI verification actually belongs.\nCompleteness: A=10/10, B=8/10, C=6/10, D=2/10\nNet: trading a first-run gate that verifies the sample-project binding against a first result that lands in seconds; the check still exists, it just stops standing between the developer and their first number.",
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"header": "First-run CI",
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"multiSelect": false,
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"options": [
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{
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"label": "Remove gate from local runs and demo (recommended)",
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"description": "✅ Demo and first local eval return in seconds; TTHW drops from 6 min to roughly 1 min, beating every peer. ✅ Keyless demo works offline and in sandboxed CI where the remote check cannot reach. ✅ Existing CI-check messaging (progress line, check URL, EVALKIT_CI_TIMEOUT) is kept for an explicit opt-in step when the developer connects to production CI. ❌ The sample-project binding is no longer verified on the very first run; misconfigured bindings surface at the CI-connection step instead. (human: ~1 day / CC: ~20 min)"
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},
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{
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"label": "Keep check, make it non-blocking",
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"description": "✅ Local result prints immediately while the CI check runs in the background and reports when it finishes. ✅ Binding is still verified on first run, so misconfiguration is caught as early as today. ❌ Background network activity during a keyless offline demo is surprising, and a failed background check still needs a reporting path the developer may never see. (human: ~2 days / CC: ~30 min)"
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},
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{
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"label": "Keep gate, add an explicit skip flag",
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"description": "✅ Existing contract is untouched for developers who want the verification. ✅ An escape hatch such as --skip-ci-check or EVALKIT_OFFLINE=1 lets the demo run offline. ❌ The default path still waits 5 minutes, so the study number does not move unless the developer knows the flag exists, which violates zero friction at T0. (human: ~half day / CC: ~10 min)"
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},
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{
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"label": "Keep as-is",
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"description": "✅ Zero implementation cost for the beta. ✅ Every first run is verified against CI before any result is shown. ❌ TTHW stays at 6 minutes against an agreed target of under 2, and the demo fails without network access."
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}
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]
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}
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],
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"answered": true,
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"failed": false,
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"answers": {
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"D1 — Mandatory 5-minute remote CI check before the first local result\nProject/branch/task: EvalKit SDK beta on main; DX POLISH review of the first-run contract.\nELI10: Today the very first evaluation, including the keyless `python -m evalkit.demo` that uses a mock transport and bundled data, cannot return until a remote CI check succeeds, and that check blocks for up to 300 seconds with no skip or offline path (docs/current-contracts.md lines 3-5). The onboarding study clocks EvalKit at 6 minutes, of which 5 is this wait; the agreed target is under 2 minutes and the fastest peer takes 2. The persona explicitly wants a local result BEFORE connecting to production CI, so the gate sits on the exact moment the demo is supposed to feel like magic.\nStakes if we pick wrong: the magical moment arrives five minutes late, a keyless demo fails outright on machines without network or CI access, and EvalKit stays last in its own benchmark.\nRecommendation: A because the persona's stated need is local-first, and a mock-transport demo has no reason to depend on remote CI; the existing progress and EVALKIT_CI_TIMEOUT messaging stays intact for the step where CI verification actually belongs.\nCompleteness: A=10/10, B=8/10, C=6/10, D=2/10\nNet: trading a first-run gate that verifies the sample-project binding against a first result that lands in seconds; the check still exists, it just stops standing between the developer and their first number.": "Remove gate from local runs and demo (recommended)"
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},
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"unansweredQuestionIndices": [],
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"answeredAt": "2026-09-10T18:05:39.125Z"
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},
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{
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"sessionId": "8c7b28d3-987f-4387-81fb-ea3703c6cac4",
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"toolUseId": "toolu_01QhJNW7VX12xDdTqPKfqQKy",
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"questions": [
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{
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"question": "D2 — run_eval(dataset, evaluator) vs run_batch(evaluator, dataset): reversed positional order\nProject/branch/task: EvalKit SDK beta on main; DX POLISH review of the public function signatures in docs/api.md.\nELI10: The two evaluation functions take the same two things, a dataset and an evaluator, but in opposite positional order, and neither requires keyword arguments (docs/api.md lines 3-9). An ML engineer who learns run_eval and then calls run_batch the same way passes the arguments swapped. Nothing in the signature stops them, so the mistake shows up at runtime, possibly as a confusing type error deep inside the evaluator. The Hall of Fame test for API design is 'can the persona use it correctly after seeing one example'; this pair fails that test by construction.\nStakes if we pick wrong: every developer who scales from single eval to batch trips on the swap, and once 2.0 final ships the order becomes a compatibility promise that is expensive to fix.\nRecommendation: A because 2.0.0b1 is a beta, so aligning the order now costs almost nothing, and a swapped-type check turns the remaining mistake into a one-line fix message instead of a stack trace.\nCompleteness: A=10/10, B=8/10, C=2/10\nNet: trading a small beta-only signature change against a permanent trap in the two most-called functions of the SDK.",
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"header": "Signatures",
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"multiSelect": false,
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"options": [
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{
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"label": "Align order to (dataset, evaluator) + swap detection (recommended)",
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"description": "✅ Both functions read the same way, so one example teaches both; matches the mental model 'evaluate this dataset with this evaluator'. ✅ If a developer still passes them swapped, the SDK detects the types and raises a message naming the fix: 'run_batch received an evaluator where the dataset goes; call run_batch(dataset, evaluator)'. ✅ Changelog carries a one-line migration note for anyone on an earlier beta. ❌ Anyone who wrote against the b1 order of run_batch must flip two arguments once. (human: ~half day / CC: ~15 min)"
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},
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{
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"label": "Make both keyword-only",
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"description": "✅ run_eval(dataset=..., evaluator=...) makes a swap impossible at every call site. ✅ Self-documenting at the call site without opening the docs. ❌ More typing for the common two-argument call, and it is a larger break for anyone already using positional calls in either function. (human: ~half day / CC: ~15 min)"
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},
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{
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"label": "Keep reversed order as documented",
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"description": "✅ No signature change and no migration note for the beta. ✅ Existing internal callers keep working untouched. ❌ The trap ships into 2.0 final where it becomes a compatibility promise, and the docs must carry a permanent warning about the reversed order."
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}
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]
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}
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],
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"answered": true,
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"failed": false,
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"answers": {
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"D2 — run_eval(dataset, evaluator) vs run_batch(evaluator, dataset): reversed positional order\nProject/branch/task: EvalKit SDK beta on main; DX POLISH review of the public function signatures in docs/api.md.\nELI10: The two evaluation functions take the same two things, a dataset and an evaluator, but in opposite positional order, and neither requires keyword arguments (docs/api.md lines 3-9). An ML engineer who learns run_eval and then calls run_batch the same way passes the arguments swapped. Nothing in the signature stops them, so the mistake shows up at runtime, possibly as a confusing type error deep inside the evaluator. The Hall of Fame test for API design is 'can the persona use it correctly after seeing one example'; this pair fails that test by construction.\nStakes if we pick wrong: every developer who scales from single eval to batch trips on the swap, and once 2.0 final ships the order becomes a compatibility promise that is expensive to fix.\nRecommendation: A because 2.0.0b1 is a beta, so aligning the order now costs almost nothing, and a swapped-type check turns the remaining mistake into a one-line fix message instead of a stack trace.\nCompleteness: A=10/10, B=8/10, C=2/10\nNet: trading a small beta-only signature change against a permanent trap in the two most-called functions of the SDK.": "Align order to (dataset, evaluator) + swap detection (recommended)"
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},
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"unansweredQuestionIndices": [],
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"answeredAt": "2026-09-10T18:06:04.706Z"
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},
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{
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"sessionId": "8c7b28d3-987f-4387-81fb-ea3703c6cac4",
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"toolUseId": "toolu_01LfVsMSQhhnfMjM6NGT9oan",
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"questions": [
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{
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"question": "D3 — Invalid API key raises AuthError(\"request failed\") with no code, cause, or fix\nProject/branch/task: EvalKit SDK beta on main; DX POLISH review of the authentication error in docs/api.md.\nELI10: Right after the demo, the persona creates a key in the console, copies it once, and exports EVALKIT_API_KEY. If they paste it wrong, export it in a different shell, or the key was revoked, the SDK raises AuthError(\"request failed\") (docs/api.md lines 11-13). That message does not say it was an auth problem, does not say which key was tried, and does not say where to get a new one. Every other EvalKit error already names the cause, the relevant argument, and a fix (docs/current-contracts.md lines 21-24); this one error is the outlier, and it sits at the first moment the developer moves from demo to real usage.\nStakes if we pick wrong: the developer's first live evaluation fails with a message that looks like a network fault, they debug the wrong thing, and the second minute after the magical moment becomes the drop-off point.\nRecommendation: A because the SDK already has the structured-error pattern for every other failure; the auth error just needs to join it, and it is the highest-traffic error a new developer will ever see.\nCompleteness: A=10/10, B=6/10, C=1/10\nNet: trading a small error-class change against the single most common first-live-call failure being undiagnosable.",
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"header": "Auth error",
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"multiSelect": false,
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"options": [
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{
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"label": "Structured auth error: code + cause + fix + link (recommended)",
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"description": "✅ Message follows the Stripe formula: 'EVALKIT_AUTH_INVALID_KEY: the API key from EVALKIT_API_KEY (ending ...ab12) was rejected by EvalKit. Create or rotate a key at https://console.evalkit.example/settings/api-keys and re-export EVALKIT_API_KEY.' ✅ Distinguishes the three real cases: key missing (env var unset), key invalid, key revoked or wrong project; each gets its own code and fix line. ✅ Error object exposes .code and .doc_url like the other typed errors, and the key is redacted to its last 4 characters per the existing redaction contract. ❌ Three codes means three messages to write and test instead of one. (human: ~half day / CC: ~15 min)"
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},
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{
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"label": "Improve the message text only",
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"description": "✅ Cheapest change: replace 'request failed' with 'Invalid API key; check EVALKIT_API_KEY' and ship. ✅ No new error codes or attributes to document. ❌ Still no code to search for, no distinction between missing and invalid, and no link to where a key is created, so the developer leaves the terminal to hunt for the console page. (human: ~1 hour / CC: ~5 min)"
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},
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{
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"label": "Keep AuthError(\"request failed\")",
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"description": "✅ No change to the beta error surface. ✅ Zero implementation cost. ❌ The one error every new developer is likely to hit is the only one in the SDK that violates the documented problem + cause + fix contract."
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}
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]
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}
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],
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"answered": true,
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"failed": false,
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"answers": {
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"D3 — Invalid API key raises AuthError(\"request failed\") with no code, cause, or fix\nProject/branch/task: EvalKit SDK beta on main; DX POLISH review of the authentication error in docs/api.md.\nELI10: Right after the demo, the persona creates a key in the console, copies it once, and exports EVALKIT_API_KEY. If they paste it wrong, export it in a different shell, or the key was revoked, the SDK raises AuthError(\"request failed\") (docs/api.md lines 11-13). That message does not say it was an auth problem, does not say which key was tried, and does not say where to get a new one. Every other EvalKit error already names the cause, the relevant argument, and a fix (docs/current-contracts.md lines 21-24); this one error is the outlier, and it sits at the first moment the developer moves from demo to real usage.\nStakes if we pick wrong: the developer's first live evaluation fails with a message that looks like a network fault, they debug the wrong thing, and the second minute after the magical moment becomes the drop-off point.\nRecommendation: A because the SDK already has the structured-error pattern for every other failure; the auth error just needs to join it, and it is the highest-traffic error a new developer will ever see.\nCompleteness: A=10/10, B=6/10, C=1/10\nNet: trading a small error-class change against the single most common first-live-call failure being undiagnosable.": "Structured auth error: code + cause + fix + link (recommended)"
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},
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"unansweredQuestionIndices": [],
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"answeredAt": "2026-09-10T18:06:30.294Z"
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},
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{
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"sessionId": "8c7b28d3-987f-4387-81fb-ea3703c6cac4",
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"toolUseId": "toolu_01Kc3x4EvYWAkRiBYMwGfySU",
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"questions": [
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{
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"question": "D4 — README quickstart points at examples/first_eval.py, which is not in the package or the examples archive\nProject/branch/task: EvalKit SDK beta on main; DX POLISH review of the packaged quickstart in README.md and docs/package-contents.txt.\nELI10: README.md line 11 tells the developer to run `python examples/first_eval.py` right after install. docs/package-contents.txt lines 8-9 confirm that file is absent from both the published wheel and the release examples archive. The only shipped runnable entry point is `python -m evalkit.demo` (evalkit/demo.py plus sample_responses.json), which is also the agreed magical-moment vehicle. So the first command the persona is told to type fails with 'No such file or directory' in minute one, and the command that actually works is mentioned as an afterthought three lines later.\nStakes if we pick wrong: the very first instruction in the quickstart is broken, which is the classic minute-two abandonment; even developers who recover now have two competing 'first commands', which the Hall of Fame flags as decision fatigue.\nRecommendation: A because the demo already exists, already works keyless, and is the chosen vehicle; making it the one quickstart command removes the broken reference and the fork in the road at once, and the live-eval example then has a clear home as step two.\nCompleteness: A=10/10, B=8/10, C=5/10\nNet: trading a README rewrite plus one shipped example module against a quickstart whose first line fails.",
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"header": "Quickstart",
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"multiSelect": false,
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"options": [
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{
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"label": "Quickstart = the demo; ship the live example as step two (recommended)",
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"description": "✅ Step 1 becomes `python -m evalkit.demo` with the documented expected output right below it, so install-to-scores is two commands and one golden path. ✅ Step 2 (after the key) runs a shipped module, `python -m evalkit.examples.first_eval`, so the file travels inside the wheel and can never be missing again; package-contents.txt is updated to list it. ✅ The stale examples/first_eval.py reference is removed from README and the examples archive gets the same file for people who browse the repo. ❌ One new module to write, test, and keep in sync with the demo output. (human: ~half day / CC: ~15 min)"
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},
|
|
{
|
|
"label": "Add examples/first_eval.py to package and archive",
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"description": "✅ The README command starts working exactly as written, with no doc rewrite. ✅ Developers who cloned the repo find the file where the README says. ❌ A top-level examples/ path only works from a source checkout, not after pip install, so the quickstart still fails for the pip-install persona; and the demo remains a second, competing first command. (human: ~2 hours / CC: ~10 min)"
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},
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{
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"label": "Remove the reference only",
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|
"description": "✅ Fastest fix: delete line 11 so the quickstart no longer points at a missing file. ✅ Zero packaging changes. ❌ There is then no packaged example of a live evaluation with a real key, so the jump from demo to real usage is undocumented. (human: ~15 min / CC: ~2 min)"
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|
}
|
|
]
|
|
}
|
|
],
|
|
"answered": true,
|
|
"failed": false,
|
|
"answers": {
|
|
"D4 — README quickstart points at examples/first_eval.py, which is not in the package or the examples archive\nProject/branch/task: EvalKit SDK beta on main; DX POLISH review of the packaged quickstart in README.md and docs/package-contents.txt.\nELI10: README.md line 11 tells the developer to run `python examples/first_eval.py` right after install. docs/package-contents.txt lines 8-9 confirm that file is absent from both the published wheel and the release examples archive. The only shipped runnable entry point is `python -m evalkit.demo` (evalkit/demo.py plus sample_responses.json), which is also the agreed magical-moment vehicle. So the first command the persona is told to type fails with 'No such file or directory' in minute one, and the command that actually works is mentioned as an afterthought three lines later.\nStakes if we pick wrong: the very first instruction in the quickstart is broken, which is the classic minute-two abandonment; even developers who recover now have two competing 'first commands', which the Hall of Fame flags as decision fatigue.\nRecommendation: A because the demo already exists, already works keyless, and is the chosen vehicle; making it the one quickstart command removes the broken reference and the fork in the road at once, and the live-eval example then has a clear home as step two.\nCompleteness: A=10/10, B=8/10, C=5/10\nNet: trading a README rewrite plus one shipped example module against a quickstart whose first line fails.": "Quickstart = the demo; ship the live example as step two (recommended)"
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|
},
|
|
"unansweredQuestionIndices": [],
|
|
"answeredAt": "2026-09-10T18:06:59.908Z"
|
|
},
|
|
{
|
|
"sessionId": "8c7b28d3-987f-4387-81fb-ea3703c6cac4",
|
|
"toolUseId": "toolu_01DYFQofwAoRqDeZ6HTxzjyP",
|
|
"questions": [
|
|
{
|
|
"question": "D5 — v2 removes Client.evaluate() immediately with no alias, deprecation warning, migration guide, or codemod\nProject/branch/task: EvalKit SDK beta on main; DX POLISH review of the v1-to-v2 client upgrade in docs/api.md.\nELI10: Version 1 users call Client.evaluate(). Version 2 renames it to Client.run() and deletes the old name on the spot (docs/api.md lines 15-18). A v1 team that bumps to 2.0 sees AttributeError: 'Client' object has no attribute 'evaluate' at runtime, possibly in production CI, with nothing pointing them to run(). The changelog is otherwise complete, so this rename is the only breaking change without a path. Upgrades should be boring; this one is a surprise.\nStakes if we pick wrong: existing v1 users, the people most likely to champion the SDK, get a red CI run on upgrade and learn that EvalKit majors break without warning, which poisons every future upgrade.\nRecommendation: A because a deprecated alias costs a few lines, gives v1 users one release of warning with the exact replacement in the message, and lets the rename land cleanly in 3.0.\nCompleteness: A=10/10, B=6/10, C=1/10\nNet: trading a few lines of alias plus a changelog section against v1 users discovering the rename via a production AttributeError.",
|
|
"header": "v1 to v2",
|
|
"multiSelect": false,
|
|
"options": [
|
|
{
|
|
"label": "Deprecated alias + warning + migration guide; remove in 3.0 (recommended)",
|
|
"description": "✅ Client.evaluate() keeps working in 2.x and emits DeprecationWarning: 'Client.evaluate() is deprecated and will be removed in 3.0; use Client.run(), same arguments.' ✅ Changelog gains a 'Migrating from 1.x' section with the one-line change, plus a copy-paste sed or one-file codemod that rewrites .evaluate( to .run( in a repo. ✅ v1 users upgrade with green CI and fix at their pace; removal is announced one major ahead. ❌ One deprecated symbol lives in the codebase for the 2.x line. (human: ~half day / CC: ~15 min)"
|
|
},
|
|
{
|
|
"label": "Remove, but ship a migration guide and a pointing error",
|
|
"description": "✅ Clean 2.0 surface with no legacy symbol. ✅ Accessing Client.evaluate raises a typed error naming run() and linking the migration guide instead of a bare AttributeError. ❌ Upgrading still breaks v1 code at runtime; the developer only learns the fix after the failure rather than before. (human: ~2 hours / CC: ~10 min)"
|
|
},
|
|
{
|
|
"label": "Keep the immediate removal as drafted",
|
|
"description": "✅ Zero implementation cost and the smallest 2.0 API surface. ✅ No deprecation lifecycle to manage. ❌ v1 users get an unexplained AttributeError on upgrade with no guide, the one breaking change the otherwise complete changelog does not cover."
|
|
}
|
|
]
|
|
}
|
|
],
|
|
"answered": true,
|
|
"failed": false,
|
|
"answers": {
|
|
"D5 — v2 removes Client.evaluate() immediately with no alias, deprecation warning, migration guide, or codemod\nProject/branch/task: EvalKit SDK beta on main; DX POLISH review of the v1-to-v2 client upgrade in docs/api.md.\nELI10: Version 1 users call Client.evaluate(). Version 2 renames it to Client.run() and deletes the old name on the spot (docs/api.md lines 15-18). A v1 team that bumps to 2.0 sees AttributeError: 'Client' object has no attribute 'evaluate' at runtime, possibly in production CI, with nothing pointing them to run(). The changelog is otherwise complete, so this rename is the only breaking change without a path. Upgrades should be boring; this one is a surprise.\nStakes if we pick wrong: existing v1 users, the people most likely to champion the SDK, get a red CI run on upgrade and learn that EvalKit majors break without warning, which poisons every future upgrade.\nRecommendation: A because a deprecated alias costs a few lines, gives v1 users one release of warning with the exact replacement in the message, and lets the rename land cleanly in 3.0.\nCompleteness: A=10/10, B=6/10, C=1/10\nNet: trading a few lines of alias plus a changelog section against v1 users discovering the rename via a production AttributeError.": "Deprecated alias + warning + migration guide; remove in 3.0 (recommended)"
|
|
},
|
|
"unansweredQuestionIndices": [],
|
|
"answeredAt": "2026-09-10T18:07:27.548Z"
|
|
}
|
|
]
|
|
}
|