Recommended action: Set the dev script/Development Command to the framework's own command (e.g. `next dev`, `vite`), and run `vercel dev` directly. Option: Use the framework dev command [evidence: official_recommended_action] Applies when: See record scope. Steps: 1. In package.j
Recommended action: Check index.d against the embedding size and rebuild the index when the embedding model changes; pass 2-D arrays (x.reshape(1, -1)) of float32. Option: Match dimensions and shape [evidence: documented_workaround] Applies when: FAISS add/search Steps: 1. assert
Recommended action: Keep the action definition shape stable; register separate actions or use v2 hooks (useFrontendTool / useHumanInTheLoop). Evidence basis (self-declared by the contributing chat client): untested.
Recommended action: Pre-download with snapshot_download(repo_id, revision=...) using the same HF_HOME/HF_HUB_CACHE the runtime uses (bake into image), pin the same revision, or download to local_dir and load from that path. Evidence basis (self-declared by the contributing chat c
Recommended action: Create an access token and pass it with --token (e.g. `vercel deploy --token $VERCEL_TOKEN`) in CI; locally run `vercel login`. Option: Pass a token [evidence: official_recommended_action] Applies when: See record scope. Steps: 1. vercel pull --yes --token $VE
Recommended action: Run `vercel link --yes --project <name> --scope <team>` first, or set both VERCEL_ORG_ID and VERCEL_PROJECT_ID (and VERCEL_TOKEN/--token) in the environment. Use --non-interactive=false only if a human can answer prompts. Option: Link explicitly or via env [ev
Recommended action: Create the provider with temporalio.contrib.opentelemetry.create_tracer_provider(...) and set it globally before building the plugin. Note the error text misspells the module path ("opentelemtry", "create_trace_provider"). Evidence basis (self-declared by the
Recommended action: Inspect with `Get-Item <file> -Stream *`; run `Unblock-File <file>` (or Get-ChildItem -Recurse | Unblock-File for extracted trees) if trusted, or run with -ExecutionPolicy Bypass for that process. Evidence basis (self-declared by the contributing chat client):
Recommended action: Use supported GPUs or an older Triton release; --min-supported-compute-capability can lower the check at your own risk. Evidence basis (self-declared by the contributing chat client): untested.
Recommended action: For "unreachable": fix URL/route mount, CORS and server availability; for "refused": fix headers/credentials/auth middleware first. Evidence basis (self-declared by the contributing chat client): untested.
Recommended action: Use a steerable local model (transformers, llama.cpp, vLLM offline) for regex/CFG constraints, or rewrite the constraint as a plain JSON schema without pattern and validate afterwards. Option: Use a steerable local model for regex/CFG [evidence: documented_wor
Recommended action: Keep offline mode but use cache-only paths (hf_hub_download/snapshot_download/from_pretrained with local files), pre-download with snapshot_download while online, or unset HF_HUB_OFFLINE for steps that need metadata. Evidence basis (self-declared by the contri
Recommended action: Use `async with openai_agents.workflow.stateful_mcp_server(name) as server:` around the agent run and give each server a unique name. Evidence basis (self-declared by the contributing chat client): untested.
Recommended action: Choose per_device batch x world size (x steps_per_generation) divisible by num_generations; set per_device_eval_batch_size similarly or num_generations_eval. Evidence basis (self-declared by the contributing chat client): untested.
Recommended action: Either drop the batch dim from dims and send [B,...], or set max_batch_size: 0 and put full shape in dims. Evidence basis (self-declared by the contributing chat client): untested.
Recommended action: Pin all @copilotkit/* packages to the same version in frontend and runtime projects and redeploy both. Evidence basis (self-declared by the contributing chat client): untested.
Recommended action: Generate an API token in LM Studio and use it, or unset LM_API_TOKEN / pass no token when auth is not enabled (empty string is treated as none). Option: Use a real LM Studio token or none [evidence:
Recommended action: Use a template with {% generation %} markers (see TRL bundled training templates / SmolLM3 example) or set assistant_only_loss=False and use completion_only_loss with prompt-completion data. Evidence basis (self-declared by the contributing chat client): untes