Imported from oimiragieo/agent-studio (.claude/skills/session-log-analyzer/SKILL.md). Install upstream with npx skills add oimiragieo/agent-studio --skill session-log-analyzer. Copyright stays with the author.

Session Log Analyzer

Parse Claude Code JSONL session logs to understand what happened during a session — tool calls, token usage, errors, subagent trees, and compaction events.

Based on patterns from claude-devtools session parsing engine.

When to Invoke

Skill({ skill: 'session-log-analyzer' });

Use when: debugging failed sessions, analyzing cost/token usage, understanding which tools consumed the most context, tracing subagent execution, detecting where compaction occurred.

Workflow

Step 1: Locate Session Logs

Session logs live at ~/.claude/projects/{encoded-path}/*.jsonl where {encoded-path} is the project directory with / replaced by -.

# List all project session directories
ls ~/.claude/projects/

# Find the most recent session for current project
ls -lt ~/.claude/projects/$(pwd | sed 's|/|-|g; s|^-||')/*.jsonl | head -5

# Or search by date
find ~/.claude/projects/ -name "*.jsonl" -newer /tmp/yesterday -type f

Step 2: Parse and Classify Messages

Each line in the JSONL is a JSON object with a type field:

# Count message types in a session
grep -o '"type":"[^"]*"' SESSION.jsonl | sort | uniq -c | sort -rn

# Extract only assistant messages
grep '"type":"assistant"' SESSION.jsonl | head -5

Message types: user, assistant, system, progress (hook output)

Step 3: Extract Tool Call Inventory

Tool calls appear as tool_use content blocks in assistant messages, results as tool_result in user messages:

# Count tool calls by name
grep -o '"type":"tool_use"' SESSION.jsonl | wc -l
grep -o '"name":"[^"]*"' SESSION.jsonl | sort | uniq -c | sort -rn

# Find errors (is_error: true in tool results)
grep '"is_error":true' SESSION.jsonl

Step 4: Estimate Token Usage

Claude Code logs include usage data per turn. Extract and sum:

# Extract token counts per turn
grep '"usage"' SESSION.jsonl | grep -o '"input_tokens":[0-9]*' | cut -d: -f2 | paste -sd+ | bc
grep '"usage"' SESSION.jsonl | grep -o '"output_tokens":[0-9]*' | cut -d: -f2 | paste -sd+ | bc

# Fallback: estimate from content size (chars / 4)
wc -c SESSION.jsonl  # divide by 4 for rough token estimate

Step 5: Detect Subagent Traces

Task tool calls spawn subagents. Extract the tree:

# Find all Task tool invocations
grep '"name":"Task"' SESSION.jsonl | grep -o '"subagent_type":"[^"]*"'

# Find task IDs
grep '"task_id"' SESSION.jsonl | grep -o '"task_id":"[^"]*"'

Step 6: Detect Compaction Boundaries

Context compaction shows as sudden drops in input_tokens between adjacent turns:

# Extract input_tokens sequence — look for >30% drops between adjacent values
grep '"input_tokens"' SESSION.jsonl | grep -o '"input_tokens":[0-9]*' | cut -d: -f2

Step 7: Generate Summary Report

Produce a summary with: total turns, tool call counts, estimated tokens, errors found, subagent tree, compaction events.

Memory Protocol (MANDATORY)

Before starting: ```bash cat .claude/context/memory/learnings.md cat .claude/context/memory/decisions.md ```

After completing:

• New pattern -> `.claude/context/memory/learnings.md`
• Issue found -> `.claude/context/memory/issues.md`
• Decision made -> `.claude/context/memory/decisions.md`

ASSUME INTERRUPTION: Your context may reset. If it's not in memory, it didn't happen.