microsoft/vscode-copilot-chat/troubleshoot

Troubleshoot

Purpose

This skill investigates and explains unexpected chat agent behavior using direct log files.

Use this skill for questions like:

• Why did this request take so long?
• Why was a tool or subagent called?
• Why did instruction/skill/agent files not load?
• Why was a tool call blocked or failed?
• Why did the model not follow expectations?

Base conclusions on evidence from logs. Do not guess.

Data Source

• Target session log directory/directories for analysis: {{VSCODE_TARGET_SESSION_LOG}}

Use direct debug log files written by Copilot Chat:

debug-logs/<sessionId>/
  main.jsonl                              — always start here; primary conversation log
  models.json                             — (optional) snapshot of available models at session start
  system_prompt_0.json                    — (optional) full system prompt sent to the model (untruncated)
  system_prompt_1.json                    — (optional) written when the model changes mid-session
  tools_0.json                            — (optional) tool definitions sent to the model
  tools_1.json                            — (optional) written when the model changes mid-session
  runSubagent-<agentName>-<uuid>.jsonl    — (optional) subagent's tool calls & LLM requests
  searchSubagent-<uuid>.jsonl             — (optional) search subagent work
  title-<uuid>.jsonl                      — (optional, UI-only) title generation
  categorization-<uuid>.jsonl             — (optional, UI-only) prompt categorization
  summarize-<uuid>.jsonl                  — (optional, UI-only) conversation summarization

Always read main.jsonl first — it has the full conversation flow. Child files only appear when those operations occurred. main.jsonl contains child_session_ref entries that link to each child file by name. Title, categorization, and summarize files are UI housekeeping and rarely relevant to troubleshooting. When investigating model availability or selection issues, read models.json — it contains the full list of models (with capabilities, billing, and limits) that were available when the session started.

When investigating what the model was told (system prompt, instructions), read the system_prompt_*.json file referenced by a system_prompt_ref entry in main.jsonl. The file contains the full untruncated system prompt as { "content": "..." }. When investigating which tools were available, read the tools_*.json file similarly. If the model changed mid-session, multiple numbered files exist — each llm_request entry has a systemPromptFile attr indicating which file was active for that request.

Each line is a JSON object. Common fields: ts (epoch ms), dur (duration ms), sid (session ID), type, name, spanId, parentSpanId, status (ok|error), attrs (type-specific details).

Event Type Reference with Examples

discovery — customization file loading (instructions, skills, agents, hooks)

{"ts":1773200251309,"dur":0,"sid":"62f52dec","type":"discovery","name":"Load Instructions","spanId":"2cb1f2f4","status":"ok","attrs":{"details":"Resolved 0 instructions in 0.0ms | folders: [/c:/Users/user/.copilot/instructions, /workspace/.github/instructions]","category":"discovery","source":"core"}}
{"ts":1773200251415,"dur":0,"sid":"62f52dec","type":"discovery","name":"Load Agents","spanId":"38a897d8","status":"ok","attrs":{"details":"Resolved 3 agents in 0.0ms | loaded: [Plan, Ask, Explore] | folders: [/workspace/.github/agents]","category":"discovery","source":"core"}}
{"ts":1773200251431,"dur":0,"sid":"62f52dec","type":"discovery","name":"Load Skills","spanId":"472eb225","status":"ok","attrs":{"details":"Resolved 6 skills in 0.0ms | loaded: [agent-customization, troubleshoot, ...]","category":"discovery","source":"core"}}

Key attrs: details (human-readable summary with folder paths, loaded items, skip reasons), category (always "discovery"), source ("core").

tool_call — tool invocation (success or failure)

{"ts":1773200222647,"dur":4,"sid":"62f52dec","type":"tool_call","name":"manage_todo_list","spanId":"000000000000000b","parentSpanId":"0000000000000003","status":"ok","attrs":{"args":"{\"operation\":\"read\"}","result":"No todo list found."}}
{"ts":1773200234047,"dur":8937,"sid":"62f52dec","type":"tool_call","name":"run_in_terminal","spanId":"000000000000000d","parentSpanId":"0000000000000003","status":"error","attrs":{"args":"{\"command\":\"echo rama\"}","result":"ERROR: conpty.node missing","error":"A native exception occurred during launch"}}

Key attrs: args (JSON string of tool input), result (tool output or error text), error (present when status:"error").

llm_request — model round-trip

{"ts":1773200231010,"dur":3001,"sid":"62f52dec","type":"llm_request","name":"chat:gpt-4o","spanId":"000000000000000c","parentSpanId":"0000000000000003","status":"ok","attrs":{"model":"gpt-4o","inputTokens":15025,"outputTokens":126,"ttft":1987,"maxTokens":32000,"systemPromptFile":"system_prompt_0.json","userRequest":"echo hello","inputMessages":"[{...}]"}}

Key attrs: model, inputTokens, outputTokens, ttft (time to first token in ms), maxTokens, temperature, topP, systemPromptFile (references a system prompt file in the session directory), toolsFile (references a tools file in the session directory), userRequest (the full user message content, untruncated), inputMessages (full messages array as JSON, pre-truncated at 64KB), error (when failed).

agent_response — model output (text + tool calls)

{"ts":1773200234011,"dur":0,"sid":"62f52dec","type":"agent_response","name":"agent_response","spanId":"agent-msg-000000000000000c","parentSpanId":"0000000000000003","status":"ok","attrs":{"response":"[{\"role\":\"assistant\",...}]","reasoning":"The user wants me to run a command."}}

Key attrs: response (JSON-encoded array of message parts; may be truncated), reasoning (optional — the model's chain-of-thought/thinking text when thinking mode is active; may be truncated).

user_message — user input

{"ts":1773200251345,"dur":0,"sid":"62f52dec","type":"user_message","name":"user_message","spanId":"000000000000000f","status":"ok","attrs":{"content":"using subagent count .md"}}

Key attrs: content (the user's message text).

subagent — subagent invocation

{"ts":1773200254954,"dur":7921,"sid":"62f52dec","type":"subagent","name":"Explore","spanId":"0000000000000014","parentSpanId":"0000000000000013","status":"ok","attrs":{"agentName":"Explore"}}

Key attrs: agentName, description (optional), error (when failed).

generic — miscellaneous events

{"ts":1773200260000,"dur":0,"sid":"62f52dec","type":"generic","name":"some-event","spanId":"abc123","status":"ok","attrs":{"details":"Additional context","category":"some-category"}}

Special generic entries:

• system_prompt_ref — references a system_prompt_*.json file in the session directory. attrs.file is the filename, attrs.model is the model it was written for. Read this file to see the full system prompt.
• tools_ref — references a tools_*.json file. attrs.file is the filename, attrs.model is the model.

session_start — session metadata (appears once at session start)

{"ts":1773200251300,"dur":0,"sid":"62f52dec","type":"session_start","name":"session_start","spanId":"session-start-62f52dec","status":"ok","attrs":{"copilotVersion":"0.43.2026033104","vscodeVersion":"1.99.0"}}

Key attrs: copilotVersion, vscodeVersion. Useful for identifying which build produced the logs.

turn_start / turn_end — tool-calling loop iteration boundaries

{"ts":1773200251400,"dur":0,"sid":"62f52dec","type":"turn_start","name":"turn_start:0","spanId":"turn-start-X-0","status":"ok","attrs":{"turnId":"0"}}
{"ts":1773200255000,"dur":0,"sid":"62f52dec","type":"turn_end","name":"turn_end:0","spanId":"turn-end-X-0","status":"ok","attrs":{"turnId":"0"}}

Key attrs: turnId (iteration number within a single user request's tool-calling loop). Use these to identify which iteration events belong to and to count total loop iterations.

Reading the event hierarchy

Events form a tree via spanId/parentSpanId. A typical chain:

1. user_message (spanId: X) — the user's turn
2. llm_request (parentSpanId: X) — model call for that turn
3. agent_response (parentSpanId: X) — what the model returned
4. tool_call (parentSpanId: X) — tool executed from the response
5. Another llm_request (parentSpanId: X) — next model call after tool result

Subagent calls create nested hierarchies: the tool_call for runSubagent (spanId: Y) becomes the parent for a child subagent span, which in turn parents its own llm_request/tool_call events.

Tooling Strategy (important)

Debug log files live outside the workspace (in user storage), so workspace-scoped search tools like grep_search cannot access them. Use the terminal instead.

Do not use `grep_search` for log files — it only works on workspace files.

macOS / Linux / WSL / Git Bash

Use run_in_terminal with grep or jq:

• Find errors: grep '"status":"error"' <logPath>
• Find discovery events: grep '"type":"discovery"' <logPath>
• Find slow events (duration > 5s): jq -c 'select(.dur > 5000)' <logPath>
• Find tool calls: grep '"type":"tool_call"' <logPath>
• Search for specific text: grep 'search_term' <logPath>
• Get last N lines: tail -n 50 <logPath>
• Count events by type: jq -r '.type' <logPath> | sort | uniq -c | sort -rn
• Extract specific fields: jq -c '{type, name, status, dur}' <logPath>
• Filter by type and show details: jq -c 'select(.type == "discovery")' <logPath>
• Find user messages: jq -c 'select(.type == "user_message") | .attrs.content' <logPath>

Windows (PowerShell)

Use run_in_terminal with PowerShell commands:

• Find errors: Select-String '"status":"error"' <logPath>
• Find discovery events: Select-String '"type":"discovery"' <logPath>
• Find tool calls: Select-String '"type":"tool_call"' <logPath>
• Search for specific text: Select-String 'search_term' <logPath>
• Get last N lines: Get-Content <logPath> -Tail 50
• Parse and filter with Node.js (always available): node -e "require('fs').readFileSync('<logPath>','utf8').split('\n').filter(Boolean).map(JSON.parse).filter(e => e.dur > 5000).forEach(e => console.log(JSON.stringify(e)))"
• Count events by type: node -e "const lines=require('fs').readFileSync('<logPath>','utf8').split('\n').filter(Boolean).map(JSON.parse);const c={};lines.forEach(e=>c[e.type]=(c[e.type]||0)+1);Object.entries(c).sort((a,b)=>b[1]-a[1]).forEach(([t,n])=>console.log(n,t))"

General rules

• Log files can be very large (tens of MB or more for long sessions). Always check file size first if unsure: ls -lh <logPath> (or (Get-Item <logPath>).Length on Windows). If the file is large, avoid commands that load the entire file into memory (e.g. node -e with readFileSync). Prefer streaming tools like grep, jq, Select-String, tail, or head.
• Use read_file only for small targeted ranges (a few lines) once you know the line numbers. Never read entire log files.
• Use run_in_terminal with ls -lh (or dir on Windows) to locate candidate .jsonl files and check their sizes.
• On Windows, if grep/jq are not available, fall back to Select-String or node -e one-liners (only for smaller files).

Investigation Workflow