Changelog

Unreleased

• Enforce project-owned accumulator-copy, widen-then-assert, and chained-assertion lint rules alongside the native accumulating-spread check. Remove double assertions through typed merge results, direct schema assignments, shared raw-body/Git transport methods, and iterable response streams.

• Add 113 CLI actions across eight new resource families and expanded scenario, metadata, dataset, saved-model, and connection APIs, with SDK exports and mutation preview contracts.

• Add LLMs, knowledge banks, project folders, data collections, users, groups, macros, and plugin administration; include MLflow imports and scoring exports.

• Bound ML training and macro status waits across requests, retries, and response bodies; preserve per-request timeout caps and distinguish request expiry from overall wait expiry. Use adaptive future and Project Git polling unless an interval is explicit.

• Keep early CLI usage errors lightweight with resource-local command loading while preserving structured errors.

• Reject malformed directory and scenario-history responses and missing run identifiers; consolidate SDK identifier validation and recursive secret sanitization.

• Use next consistently in metadata previews. Normalize commands to project-folder settings-get/settings-set, data-collection settings-set, and scenario payload-get/payload-set/active-set; remove the old names.

• Remeasure and verify native cross-model discovery-token baselines after API expansion and command renames.

• Publish the DSS 15 API-to-SDK-to-CLI coverage matrix, including remaining gaps and documented endpoint discrepancies.

• Optimize all seven skill references, cutting their combined OpenAI text-token count by 15.2% while preserving runnable examples and safety conditions; add per-reference budgets.

• Reduce installed-skill tokens by about 12% across model encodings and shorten discovery prose, retaining safety guidance, schema contracts, and reference links; tighten the skill budget to prevent regrowth.

• Add opt-in cross-model discovery token budgets using an external OMP native module, preserving the mandatory pinned OpenAI gate and adding no production dependency.

• Preserve request IDs and trusted target/timing metadata when enriching SQL failures.

• Treat SQLite missing-column and missing-table errors as nonretryable validation failures, including when SQL start retries are explicitly enabled; preserve ambiguous mutation outcomes.

• Decode bounded HTTP text incrementally to reduce large-response allocations while retaining byte limits, read deadlines, and UTF-8 truncation behavior.

• Load CLI command dependencies lazily and build only the selected resource registry during validation, preserving full discovery contracts.

• Publish SQL JSON exports atomically after query completion, retaining existing permission bits and using the process umask for new files; preserve existing destinations on failure and avoid iterable-string file writes.

• Expand the self-provisioning live runner with an --profile all selector that widens run-case availability to every profile inside one persistent lab (distinct from the destructive all lifecycle verb), persistent --state-dir demo labs, retained trained-ML state across iterations, and new expanded case families (bundles, Project Git, scenario statistics/payloads, application manifests, webapp backend state, infrastructure reads, disposable folders/notebooks, disposable global lifecycles).

• Add isolated-disposable global case families (users, groups, meanings, workspaces, data collections, API-deployer infra/services/deployments, plugins, project folders) guarded by exact lab-manifest reservations and ownership markers (ctx.markerFor; code-envs bind on the server desc.creationTag incarnation snapshot), with per-command identity re-verification, never-delete-blind conflict handling, and no modification of pre-existing accounts or configuration.

• Record explicit per-action blockers for capabilities the lab cannot self-provision: application templates, external SQL connectivity, external Git remotes, external auth backends, plugin-store access, and cost-bearing LLM/macro surfaces; every registered action stays enumerated as demonstrated, blocked with a recorded prerequisite, or uncovered, with no silent omissions.

• Serve plugin git-branches with GET: DSS 15 rejects POST on that route with 405 rawMethodPOSTnotsupported despite the public documentation listing POST.

• Clarify code-env set-packages clearing semantics: the package list is replaced wholesale, so an explicitly empty --packages value (or an empty --file) clears the requested specs, while a call with no package source flag is a usage error.

• Exercise plugin-managed code environments explicitly in the live harness: derived environments are deleted before the parent plugin (no server-side cascade) and plugin updates link the settings.codeEnvName association created after plugin creation; MLflow import fixtures use canonical code/data/env packaging inside owned runtime environments.

• Require the DSS 15 containerExecConfigName parameter explicitly on MLflow imports and evaluations: send NONE for archive imports, folder imports, and evaluation bodies (folder imports no longer inherit), with plan parity and regression coverage.

• Add a separate, self-provisioning test:live runner with persistent setup/run/clean, upfront case validation, optional capability profiles, guarded ownership and local outputs, failure-preserving cleanup journals, deterministic fixtures, explicit registry coverage reports, and regular type/lint gates.

• Normalize explicit boolean flag values and aliases before dispatch; share plan/dry-run resolution across direct, batch, and local commands.

• Bound stalled streaming body reads without imposing a total-duration limit on healthy archive transfers; use backpressure-aware file streaming and publish archive/folder outputs only after completion, preserving existing files on failure.

• Report unreadable JSON/text input files as structured usage errors rather than internal failures.

• Report the actual home target for global skill installation.

• Split the Dataiku skill into a short entrypoint and seven on-demand references; install and hash the complete bundle, detect incomplete reference trees, and preserve unrelated destination files.