bug1_prompt_snapshot_failure_excerpt.txt

failures:

---- agents::prompt_manager::tests::test_all_platform_extensions stdout ----
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Snapshot Summary ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Snapshot file: crates/goose/src/agents/snapshots/goose__agents__prompt_manager__tests__all_platform_extensions.snap
Snapshot: all_platform_extensions
Source: crates/goose/src/agents/prompt_manager.rs:458
────────────────────────────────────────────────────────────────────────────────
Expression: system_prompt
────────────────────────────────────────────────────────────────────────────────
-old snapshot
+new results
────────────┬───────────────────────────────────────────────────────────────────
   94    94 │ ### Instructions
   95    95 │ 
   96    96 │ 
   97    97 │ You have these skills at your disposal, when it is clear they can help you solve a problem or you are asked to use them:
         98 │+• capacity - Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
         99 │+• customize - Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
        100 │+• deploy-model - Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
   98   101 │ • goose-doc-guide - Reference goose documentation to create, configure, or explain goose-specific features like recipes, extensions, sessions, and providers. You MUST fetch relevant goose docs before answering. You MUST NOT rely on training data or assumptions for any goose-specific fields, values, names, syntax, or commands.
        102 │+• microsoft-foundry - Deploy, evaluate, and manage Foundry agents end-to-end: Docker build, ACR push, hosted/prompt agent create, container start, batch eval, prompt optimization, prompt optimizer workflows, agent.yaml, dataset curation from traces. USE FOR: deploy agent to Foundry, hosted agent, create agent, invoke agent, evaluate agent, run batch eval, optimize prompt, improve prompt, prompt optimization, prompt optimizer, improve agent instructions, optimize agent instructions, optimize system prompt, deploy model, Foundry project, RBAC, role assignment, permissions, quota, capacity, region, troubleshoot agent, deployment failure, create dataset from traces, dataset versioning, eval trending, create AI Services, Cognitive Services, create Foundry resource, provision resource, knowledge index, agent monitoring, customize deployment, onboard, availability. DO NOT USE FOR: Azure Functions, App Service, general Azure deploy (use azure-deploy), general Azure prep (use azure-prepare).
        103 │+• preset - Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
   99   104 │ ## summarize
  100   105 │ 
  101   106 │ 
  102   107 │ ## summon
────────────┴───────────────────────────────────────────────────────────────────
To update snapshots run `cargo insta review`
Stopped on the first failure. Run `cargo insta test` to run all snapshots.

thread 'agents::prompt_manager::tests::test_all_platform_extensions' (1415564) panicked at /Users/sunilkumar/workspace/goose/.hermit/rust/registry/src/index.crates.io-1949cf8c6b5b557f/insta-1.46.3/src/runtime.rs:719:13:
snapshot assertion for 'all_platform_extensions' failed in line 458

---- providers::ollama::tests::test_stream_ollama_timeout_on_stall stdout ----

thread 'providers::ollama::tests::test_stream_ollama_timeout_on_stall' (1417192) panicked at crates/goose/src/providers/ollama.rs:523:10:
called `Result::unwrap()` on an `Err` value: failed to open file `/var/folders/q7/x6h6k94911g_dp0dtcqvlmnh0000gn/T/.tmpYAbqZG/state/logs/llm_request.a42f6e79-11e6-46e5-bcd7-c57ef806f595.jsonl`: No such file or directory (os error 2)


failures:
    agents::prompt_manager::tests::test_all_platform_extensions
    providers::ollama::tests::test_stream_ollama_timeout_on_stall

test result: FAILED. 1092 passed; 2 failed; 0 ignored; 0 measured; 0 filtered out; finished in 8.16s

error: test failed, to rerun pass `-p goose --lib`