session-investigator
Analyze session history to debug agent errors.
Install
mkdir -p .claude/skills/session-investigator && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/2311" && unzip -o skill.zip -d .claude/skills/session-investigator && rm skill.zipInstalls to .claude/skills/session-investigator
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
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.Key capabilities
- →Parse session metadata and history files
- →Correlate tool calls with results using correlation IDs
- →Calculate LLM and tool execution timing statistics
- →Truncate history files to resolve tool loop errors
- →Correlate sub-agent traces with main session timing
How it works
The skill uses jq to query JSON structures within session directories, enabling extraction of timing data, message sequences, and tool execution logs. It identifies failure patterns by comparing assistant tool calls against subsequent user tool results.
Inputs & outputs
When to use session-investigator
- →Debug agent tool loops
- →Analyze unexpected session termination
- →Correlate sub-agent execution traces
- →Audit conversation history
About this skill
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
When not to use it
- →Analyzing logs outside the .fast-agent/sessions directory
- →Modifying session files without prior backup
Limitations
- →Requires manual identification of session IDs
- →Truncation logic assumes specific message ordering
How it compares
Unlike manual file inspection, this skill provides automated command-line patterns to correlate timing and fix common session corruption issues.
Compared to similar skills
session-investigator side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| session-investigator (this skill) | 2 | 2mo | Review | Intermediate |
| langsmith-observability | 4 | 7mo | Review | Intermediate |
| error-diagnostics-smart-debug | 5 | 4mo | No flags | Advanced |
| debugging-toolkit-smart-debug | 4 | 4mo | No flags | Intermediate |
Try saying
Example prompts that trigger this skill in your AI assistant.
More by evalstate
View all by evalstate →You might also like
langsmith-observability
davila7
LLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production systems, or building systematic testing pipelines for AI applications.
error-diagnostics-smart-debug
sickn33
Use when working with error diagnostics smart debug
debugging-toolkit-smart-debug
sickn33
Use when working with debugging toolkit smart debug
jaeger-analysis
incidentfox
Jaeger distributed tracing analysis. Use when investigating request latency, tracing errors across services, finding slow spans, or understanding service dependencies.
agentation
benjitaylor
Add Agentation visual feedback toolbar to a Next.js project
log-analyzer
mikopbx
Анализ логов Docker контейнера для диагностики проблем и мониторинга здоровья системы. Использовать при отладке ошибок, отслеживании процессов воркеров, исследовании проблем API или мониторинге поведения системы после тестов.