Skip to content
cccskills
FreePublished on GitHub

session-investigator

Investigate fast-agent session and history files to diagnose issues. Use when a session ended unexpectedly, when debugging tool loops, when correlating sub-agent traces with main sessions, or when analyzing conversation flow and timing. Covers session.json metadata, history JSON format, message structure, tool call/result correlation, and common failure patterns.

The English summary is being prepared. Showing the original description.

See how to install

Files included (1)

  • SKILL.md8.1 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

Session Investigator

Diagnose fast-agent session issues by examining session and history files.

Session Directory Structure

Sessions are stored in .fast-agent/sessions/<session-id>/:

2601181023-Kob2h3/
├── session.json              # Session metadata
├── history_<agent>.json      # Current agent history
└── history_<agent>_previous.json  # Previous save (rotation backup)

Session IDs encode creation time: YYMMDDHHMM-<random> (e.g., 2601181023 = 2026-01-18 10:23).

Key Files

session.json

{
  "name": "2601181023-Kob2h3",
  "created_at": "2026-01-18T10:23:24.116526",
  "last_activity": "2026-01-18T10:39:42.873467",
  "history_files": ["history_dev_previous.json", "history_dev.json"],
  "metadata": {
    "agent_name": "dev",
    "first_user_preview": "is it possible to override..."
  }
}

history_<agent>.json

{
  "messages": [
    {
      "role": "user|assistant",
      "content": [{"type": "text", "text": "..."}],
      "tool_calls": {"<id>": {"method": "tools/call", "params": {"name": "...", "arguments": {}}}},
      "tool_results": {"<id>": {"content": [...], "isError": false}},
      "channels": {
        "fast-agent-timing": [{"type": "text", "text": "{\"start_time\": ..., \"end_time\": ..., \"duration_ms\": ...}"}],
        "fast-agent-tool-timing": [{"type": "text", "text": "{\"<tool_id>\": {\"timing_ms\": ..., \"transport_channel\": ...}}"}],
        "reasoning": [{"type": "text", "text": "..."}]
      },
      "stop_reason": "endTurn|toolUse|error",
      "is_template": false
    }
  ]
}

Investigation Commands

Basic inspection

# Message count
jq '.messages | length' history_dev.json

# Last N messages overview
jq '.messages[-5:] | .[] | {role, stop_reason, has_tool_calls: (.tool_calls != null), has_tool_results: (.tool_results != null)}' history_dev.json

# View specific message
jq '.messages[227]' history_dev.json

Tool call correlation

Tool calls and results are linked by correlation ID. Valid pattern: assistant with tool_calls → user with matching tool_results.

# Check tool call/result pairing
jq '.messages[-10:] | to_entries | .[] | {
  index: .key,
  role: .value.role,
  tool_calls: (if .value.tool_calls then (.value.tool_calls | keys) else [] end),
  tool_results: (if .value.tool_results then (.value.tool_results | keys) else [] end)
}' history_dev.json

Find specific tool calls

# Find all calls to a specific tool
jq '.messages | to_entries | .[] |
  select(.value.tool_calls != null) |
  select(.value.tool_calls | to_entries | .[0].value.params.name == "agent__ripgrep_search") |
  {index: .key, timing: (.value.channels."fast-agent-timing"[0].text)}' history_dev.json

Session Statistics

LLM Call Stats

# Total LLM time and call count
jq '[.messages[] | select(.role == "assistant") |
  select(.channels."fast-agent-timing") |
  .channels."fast-agent-timing"[0].text | fromjson | .duration_ms] |
  {count: length, total_ms: add, avg_ms: (add/length), max_ms: max, min_ms: min}' history_dev.json

# LLM calls sorted by duration (slowest first)
jq '[.messages | to_entries | .[] |
  select(.value.role == "assistant") |
  select(.value.channels."fast-agent-timing") |
  {index: .key, duration_ms: (.value.channels."fast-agent-timing"[0].text | fromjson | .duration_ms)}] |
  sort_by(-.duration_ms) | .[0:10]' history_dev.json

Tool Execution Stats

# All tool timings aggregated
jq '[.messages[] | select(.channels."fast-agent-tool-timing") |
  .channels."fast-agent-tool-timing"[0].text | fromjson | to_entries | .[].value.timing_ms] |
  {count: length, total_ms: add, avg_ms: (add/length), max_ms: max, min_ms: min}' history_dev.json

# Tool calls by name with timing
jq '[.messages | to_entries | .[] |
  select(.value.tool_calls) |
  (.value.tool_calls | to_entries | .[0]) as $tc |
  {index: .key, tool: $tc.value.params.name,
   llm_ms: (.value.channels."fast-agent-timing"[0].text | fromjson | .duration_ms)}] |
  group_by(.tool) |
  map({tool: .[0].tool, count: length, total_llm_ms: (map(.llm_ms) | add)}) |
  sort_by(-.count)' history_dev.json

Session Timeline

# Session duration from first to last timing
jq '.messages | [
  (map(select(.channels."fast-agent-timing")) | first | .channels."fast-agent-timing"[0].text | fromjson | .start_time),
  (map(select(.channels."fast-agent-timing")) | last | .channels."fast-agent-timing"[0].text | fromjson | .end_time)
] | {start: .[0], end: .[1], duration_sec: ((.[1] - .[0]) | round)}' history_dev.json

# Message rate over time (messages per minute estimate)
jq '{
  messages: (.messages | length),
  llm_calls: [.messages[] | select(.role == "assistant" and .channels."fast-agent-timing")] | length,
  total_llm_ms: [.messages[] | select(.channels."fast-agent-timing") | .channels."fast-agent-timing"[0].text | fromjson | .duration_ms] | add,
  total_tool_ms: [.messages[] | select(.channels."fast-agent-tool-timing") | .channels."fast-agent-tool-timing"[0].text | fromjson | to_entries | .[].value.timing_ms] | add
} | . + {llm_sec: (.total_llm_ms/1000), tool_sec: ((.total_tool_ms//0)/1000)}' history_dev.json

Sub-agent Stats

# Sub-agent calls (tools starting with "agent__")
jq '[.messages | to_entries | .[] |
  select(.value.tool_calls) |
  (.value.tool_calls | to_entries | .[0]) as $tc |
  select($tc.value.params.name | startswith("agent__")) |
  {index: .key, agent: $tc.value.params.name,
   llm_ms: (.value.channels."fast-agent-timing"[0].text | fromjson | .duration_ms)}] |
  group_by(.agent) |
  map({agent: .[0].agent, calls: length, total_ms: (map(.llm_ms) | add), avg_ms: ((map(.llm_ms) | add) / length)})' history_dev.json

Common Failure Patterns

Unanswered Tool Call

Symptom: API error "No tool output found for function call"

Pattern: History ends with assistant message having tool_calls and stop_reason: "toolUse", followed by user message WITHOUT matching tool_results.

# Check last message for pending tool call
jq '.messages[-1] | {role, has_tool_calls: (.tool_calls != null), stop_reason}' history_dev.json

Cause: Session interrupted mid-tool-loop, then resumed with new user input before tool completed.

Fix: Truncate history to last valid tool result:

# Find last user message with tool_results
jq '.messages | to_entries | map(select(.value.role == "user" and .value.tool_results != null)) | last | .key' history_dev.json

# Truncate (keep messages 0 to N inclusive, so use N+1)
jq '.messages = .messages[0:227]' history_dev.json > /tmp/fixed.json && mv /tmp/fixed.json history_dev.json

Duplicate User Messages

Pattern: Two consecutive user messages before assistant response.

Cause: Often from before_llm_call hooks appending instructions. Check agent card's tool_hooks configuration.

Sub-agent Trace Correlation

Sub-agent traces are saved as <agent_name>-<timestamp>.json in the working directory.

# List traces around session time
ls -la ripgrep_search*2026-01-18-10-3*.json

# Correlate via timing - match monotonic clock values
jq '.messages[-1].channels."fast-agent-timing"[0].text' ripgrep_search*.json

Compare start_time/end_time values between main session and sub-agent traces to correlate which sub-agent call corresponds to which main session tool call.

Log File

Check <fast-agent-home>/fast-agent-log.jsonl for errors during the session timeframe:

# Filter by timestamp range
cat .fast-agent/fast-agent-log.jsonl | while read line; do
  ts=$(echo "$line" | jq -r '.timestamp // empty' 2>/dev/null)
  if [[ "$ts" > "2026-01-18T10:20" && "$ts" < "2026-01-18T10:45" ]]; then
    echo "$line" | jq -c '{timestamp, level, message}'
  fi
done

Reviews

No reviews yet. Tell others what you thought.

Comments

No login needed. Share how it worked for you, ask a question, or note where it did not run.

No comments yet. Be the first to write one.

Comments are published right away. Do not include personal information such as email addresses or phone numbers. Up to 2 URLs.

0 / 1,000 characters

This form is protected by reCAPTCHA, and the Google Privacy Policy and Terms of Service apply.

More skills from this repository

Use this skill to generate well-branded interfaces and assets for fast-agent, either for production or throwaway prototypes/mocks/etc. Contains essential design guidelines, colors, type, fonts, assets, and UI kit components for protoyping.

The English summary is being prepared. Showing the original description.

evalstate/fast-agent3.9KUpdated Oct 19, 2026

Extract and analyze writing improvements from GitHub PR review comments. Use when asked to show review feedback, style changes, or editorial improvements from a GitHub pull request URL. Handles both explicit suggestions and plain text feedback. Produces structured output comparing original phrasing with reviewer suggestions to help refine future writing.

The English summary is being prepared. Showing the original description.

evalstate/fast-agent3.9KUpdated Oct 19, 2026

See all skills by evalstate

Report a problem with this skill