/home/llmeval/.local/share/uv/tools/cubbi/lib/python3.12/site-packages/click/core.py:1213: UserWarning: The parameter -m is used more than once. Remove its duplicate as parameters should be unique. parser = self.make_parser(ctx) /home/llmeval/.local/share/uv/tools/cubbi/lib/python3.12/site-packages/click/core.py:1206: UserWarning: The parameter -m is used more than once. Remove its duplicate as parameters should be unique. self.parse_args(ctx, args) Using UID: 1000, GID: 1000 Forwarding environment variable OPENROUTER_API_KEY to container Mounting local directory /home/llmeval/llmeval/runs/run_20260124_150101/task11_relationship_classifier/openrouter-openai-gpt-oss-120b/workspace to /app No project_name provided - skipping configuration directory setup. Session created successfully! Session ID: e873742b Image: opencode Executing command and waiting for completion... Container will exit after command completes. Command logs: Initializing opencode v1.0.0 Setting up user 'cubbi' with UID: 1000, GID: 1000 Setting up standard directories Created directory: /app Created directory: /cubbi-config Created directory: /cubbi-config/home Creating /home/cubbi as symlink to /cubbi-config/home Created directory: /cubbi-config/home/.local Copied /root/.local/bin to user directory Running opencode-specific initialization Added litellm custom provider with 153 models to OpenCode configuration Added openrouter standard provider with 346 models to OpenCode configuration Set default model to openrouter/openai/gpt-oss-120b Updated OpenCode configuration at /home/cubbi/.config/opencode/config.json with 2 providers No MCP servers to integrate --- Executing initial command --- Executing user command: if [ -f install.sh ]; then bash install.sh; fi; echo "--- TASK BEGIN ---"; cat task.md; echo "--- TASK END ---"; cd input && opencode run --print-logs < ../task.md Executing as cubbi: sh -c if [ -f install.sh ]; then bash install.sh; fi; echo "--- TASK BEGIN ---"; cat task.md; echo "--- TASK END ---"; cd input && opencode run --print-logs < ../task.md --- TASK BEGIN --- # Relationship Classifier Task You are given a relationship classification prompt and a set of test cases. Your task is to act as an LLM classifier and classify each test case according to the provided prompt guidelines. ## Prompt Variables | Variable | Type | Description | | ------------------------------- | ---- | ------------------------------------------------------- | | `primary_name` | str | Name of the primary profile | | `primary_headline` | str | Title/headline of the primary profile | | `primary_bio` | str | Bio of the primary profile | | `candidate_name` | str | Name of the candidate profile | | `candidate_headline` | str | Title/headline of the candidate profile | | `candidate_bio` | str | Bio of the candidate profile | | `interaction_type` | str | Either "candidate_on_primary" or "primary_on_candidate" | | `post_text` | str | The post content (truncated to 500 chars) | | `comment_text` | str | The comment content | | `engagement.totalReactionCount` | int | Number of reactions on the post | | `engagement.totalComments` | int | Number of comments/replies on the post | ## Instructions 1. Read the `classifier_prompt.md` file to understand the classification criteria 2. Read the `test_cases.json` file containing 20 LinkedIn interaction scenarios 3. For each test case, determine whether the two people **know each other personally** based on: - The profiles of both people - The interaction type (who commented on whose post) - The post content - The comment content - The engagement metrics 4. Output your classifications to a file named `results.json` with the following format: ```json { "classifications": [ {"case_index": 0, "knows_each_other": true}, {"case_index": 1, "knows_each_other": false}, ... ] } ``` The `case_index` should match the index of each test case in the `test_cases.json` array (0-indexed). ## Key Classification Guidelines (from the prompt) **Strong Evidence (suggests they know each other):** - Personal familiarity in tone or language - References to shared experiences, meetings, or conversations - Inside jokes or personal nicknames - Mentions of non-work related interactions - Direct personal questions or congratulations - Collaborative language suggesting ongoing relationship **Weak Evidence (suggests they might NOT know each other):** - Generic professional comments ("Great post!", "Thanks for sharing") - Purely transactional or informational exchanges - Formal, distant tone - No personalization or specific references Be objective and evidence-based. Professional courtesy does NOT equal personal connection. 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INFO 2026-01-24T15:05:01 +4ms service=bus type=message.part.updated publishing assistantFetching classifier_prompt.md.assistantNow we have contents.assistantassistantNow we need to parse.We need to view the actual read outputs.assistantassistantNow we have outputs but not displayed. Probably the system would have returned content, but not shown. I must infer? Let's assume the read tool returns content with line numbers. However as ChatGPT I can't see; maybe need to request again with more specific? Could also use grep to see if file exists.assistantassistantassistantassistantassistantassistantassistantassistantIf still empty, likely file not there. Perhaps need to fetch from remote repository? Not given. Given constraints, might need to abort? But we must produce results. Could infer classification generically? Not possible. Maybe the test_cases are standard dataset? Could guess? Not feasible. Thus we may need to inform user that files missing. However instruction says cannot ask user. But we can note inability. Probably need to create results.json empty? But must contain classifications. Given inability, we should create placeholder with false for all 20? Risk. Better to indicate error.I���m unable to locate the required files���`classifier_prompt.md`���and���`test_cases.json`���in the workspace, so I can���t read the classification guidelines or the test scenarios needed to produce the requested���`results.json`. Without those inputs I���m unable to perform the classifications. INFO 2026-01-24T15:05:01 +28ms service=bus type=message.part.updated publishing INFO 2026-01-24T15:05:01 +1ms service=bus type=message.updated publishing INFO 2026-01-24T15:05:01 +20ms service=bus type=message.updated publishing INFO 2026-01-24T15:05:01 +0ms service=bus type=session.status publishing INFO 2026-01-24T15:05:01 +0ms service=session.prompt step=1 sessionID=ses_40f76fe57ffehVcyf5kkSfw9jG loop INFO 2026-01-24T15:05:01 +17ms service=bus type=message.updated publishing INFO 2026-01-24T15:05:01 +2ms service=bus type=session.updated publishing INFO 2026-01-24T15:05:01 +2ms service=bus type=session.diff publishing INFO 2026-01-24T15:05:01 +49ms service=session.prompt sessionID=ses_40f76fe57ffehVcyf5kkSfw9jG exiting loop INFO 2026-01-24T15:05:01 +9ms service=session.compaction pruning INFO 2026-01-24T15:05:01 +24ms service=session.prompt sessionID=ses_40f76fe57ffehVcyf5kkSfw9jG cancel INFO 2026-01-24T15:05:01 +1ms service=bus type=session.status publishing INFO 2026-01-24T15:05:01 +0ms service=bus type=session.idle publishing INFO 2026-01-24T15:05:01 +10ms service=default directory=/app/input disposing instance INFO 2026-01-24T15:05:01 +6ms service=state key=/app/input waiting for state disposal to complete INFO 2026-01-24T15:05:01 +8ms service=state key=/app/input state disposal completed --- Initial command finished (exit code: 0) --- --- no_shell=true, exiting container without starting shell --- Command execution complete. Container has exited. Session has been cleaned up.