leogodin217/session

Session Analysis

Analyze Claude Code session transcripts — search for sessions, summarize them, or perform ad-hoc analysis of how a session went.

Tool

A Python analyzer is available at ~/.claude/skills/session/analyzer.py. Run it via Bash.

Commands

# List recent sessions (all projects) — shows custom titles from /rename
python3 ~/.claude/skills/session/analyzer.py list --recent 20

# List ALL sessions for a project (no limit)
python3 ~/.claude/skills/session/analyzer.py list --project -home-leo-projects-fabulexa-sim --all

# List sessions for a specific project
python3 ~/.claude/skills/session/analyzer.py list --project -home-leo-projects-fabulexa-sim

# Find sessions by custom title (set via /rename in Claude Code)
python3 ~/.claude/skills/session/analyzer.py find "wtf_sprint_execution"
python3 ~/.claude/skills/session/analyzer.py find "sprint" --project -home-leo-projects-fabulexa-sim

# Search sessions by keyword in transcript content (searches main JSONL + tool-results + subagents)
# Shows context snippets around each match
python3 ~/.claude/skills/session/analyzer.py search "implement-sprint" --recent 50
python3 ~/.claude/skills/session/analyzer.py search "implement-sprint" --all      # search all sessions, not just recent
python3 ~/.claude/skills/session/analyzer.py search "implement-sprint" --verbose  # also warns on unknown JSONL types

# Get just the path of the Nth search result
python3 ~/.claude/skills/session/analyzer.py search "implement-sprint" --index 5

# Summarize a session (accepts path, UUID, or custom title)
python3 ~/.claude/skills/session/analyzer.py summary /path/to/session.jsonl
python3 ~/.claude/skills/session/analyzer.py summary "wtf_sprint_execution"

# Deep summary — aggregates parent + all subagent transcripts
python3 ~/.claude/skills/session/analyzer.py summary /path/to/session.jsonl --deep
python3 ~/.claude/skills/session/analyzer.py summary --search "keyword" --index 2 --deep

# Summarize directly from search results (preferred for search-then-inspect flows)
python3 ~/.claude/skills/session/analyzer.py summary --search "implement-sprint" --index 5

# Extract conversation flow (user prompts + assistant responses)
python3 ~/.claude/skills/session/analyzer.py conversation /path/to/session.jsonl --max-chars 800
python3 ~/.claude/skills/session/analyzer.py conversation --search "keyword" --index 2

# Tool usage timeline
python3 ~/.claude/skills/session/analyzer.py tools /path/to/session.jsonl
python3 ~/.claude/skills/session/analyzer.py tools --search "keyword" --index 2

# Compare two sessions — token usage, tool counts, duration, files touched
python3 ~/.claude/skills/session/analyzer.py diff SESSION_A SESSION_B

Session names

Sessions can be named via /rename in Claude Code. Names are stored as {"type": "custom-title", "customTitle": "..."} entries in the session JSONL. Named sessions appear with [title] prefix in list and search output, and can be looked up directly via find or passed to summary/conversation/tools.

Search scope

Search covers three sources per session:

• Main JSONL — the primary transcript
• Externalized tool results (<uuid>/tool-results/*.txt) — large tool outputs stored separately from the JSONL
• Subagent transcripts (<uuid>/subagents/*.jsonl) — Agent tool invocations

Context snippets (~80 chars) are shown around each match. Source labels ([tool-result], [subagent]) indicate where non-main matches came from.

Deep summary

summary --deep aggregates across the parent session and all its subagent transcripts:

• Combined duration, total tokens (input/output/cache), total tool calls
• Per-subagent breakdown table with duration, tool count, tokens, file size

Diff

diff SESSION_A SESSION_B shows side-by-side comparison of:

• Duration, file size, token usage (input, output, cache read/create)
• Tool call counts (total and per-tool breakdown)
• Files touched (Read/Write/Edit targets) — which files are in both, only A, or only B

Session Data Location

Sessions are stored at ~/.claude/projects/<project-slug>/<session-uuid>.jsonl. Each session may also have:

• <session-uuid>/subagents/*.jsonl — subagent transcripts (one per Agent tool invocation)
• <session-uuid>/tool-results/toolu_<id>.txt — externalized tool results (large outputs stored separately)

JSONL Format Reference

Each line in a session JSONL is one of these types:

| Type | What it is | |------|-----------| | user | User messages, command invocations, tool results | | assistant | Claude responses (text, thinking, tool_use blocks) | | custom-title | Session name set via /rename. Last entry wins (renames append, don't replace) | | progress | Granular execution tracking (bash, agent, hook progress) | | queue-operation | Background task enqueue/dequeue | | file-history-snapshot | File version tracking | | system | Turn duration metrics | | last-prompt | Records the last user prompt for session resume |

Use --verbose with search or summary to detect any types not in this known set.

Key fields per message

{
  "type": "user" | "assistant" | ...,
  "uuid": "...",
  "parentUuid": "...",           // conversation threading
  "sessionId": "...",
  "timestamp": "ISO-8601",
  "cwd": "/working/dir",
  "version": "2.1.x",
  "message": {
    "role": "user" | "assistant",
    "content": [...],            // text, tool_use, tool_result blocks
    "model": "claude-opus-4-6",  // assistant messages only
    "usage": {                   // assistant messages only
      "input_tokens": N,
      "output_tokens": N,
      "cache_read_input_tokens": N,
      "cache_creation_input_tokens": N
    }
  }
}

Custom title (from /rename)

{"type": "custom-title", "customTitle": "my_session_name", "sessionId": "uuid"}

A session can have multiple custom-title entries (each /rename appends a new one). The last entry is the current name.

Command invocations in user messages

Slash commands appear as XML in user message content:

<command-name>/implement-sprint</command-name>
<command-message>implement-sprint</command-message>

Tool use blocks (assistant content)

{"type": "tool_use", "id": "toolu_...", "name": "Bash", "input": {"command": "..."}}

Tool result blocks (user content)

{"type": "tool_result", "tool_use_id": "toolu_...", "content": "..."}

Ad-Hoc Analysis Guide

When asked to analyze a session beyond what the tool provides, read the JSONL directly. Common analyses:

How well did a /command work?

1. Run summary --deep to get the overview including subagent aggregation
2. Run conversation to see the flow
3. Check: Did the command's phases execute in order?
4. Check: Were subagents launched as expected?
5. Check: Did gates (tests, pre-commit, review) pass or fail?
6. Check: How much context was consumed vs. the budget?
7. Read specific messages for deeper investigation

Confusion and spec deviation signals

Search assistant messages for keywords that indicate an agent struggled or deviated from its instructions. Use inline Python to scan the JSONL:

Keywords to search for:

• Backtracking: "actually", "wait", "wrong", "mistake", "let me reconsider", "should have"
• Spec deviation: "spec says", "not what the spec", "different approach", "intentional", "skip", "omit"
• Rework: "revert", "undo", "let me try", "didn't work", "failed"
• Workarounds: "circular", "lazy import", "workaround", "hack"
• Scope creep: "refactor", "restructur", "redesign", "not implement"

Search both the main session and all subagent transcripts. Extract ~60 chars before and 100 chars after each match for context.

Context efficiency audit

Analyze where context tokens were wasted. Key metrics:

1. Duplicate reads within an agent: Count how many times each file_path appears in Read tool_use blocks per agent. Any file read 2+ times is waste.
2. Cross-agent file duplication: Track which files were read by multiple agents. Expected for some files (spec, config models), but the total KB reveals whether the command should pre-extract relevant sections.
3. Per-turn token growth: Plot cache_read_input_tokens + input_tokens + cache_creation_input_tokens per assistant turn. Look for spikes (large tool results) and plateaus (agent spinning).
4. Late notification waste: After the main task completes, count how many turns were spent processing late-arriving subagent notifications. Each burns a full context re-read for trivial output.
5. Tool result sizes: Rank all tool_result blocks by byte size. The top 5 largest results often reveal unnecessary full-file reads or verbose command output.
6. Spec reads: Count how many agents read the full spec. If N agents each read a 30KB spec, that's N*30KB consumed. Extracting phase-specific sections before dispatch saves ~(N-1)*30KB.

File provenance

Find which session created or modified a specific file:

# Search for Write tool_use blocks with matching file_path
python3 -c "
import json
path = '/path/to/session.jsonl'
target = 'path/to/file.py'
with open(path) as f:
    for line in f:
        if target in line and 'tool_use' in line:
            msg = json.loads(line)
            # check for Write/Edit tool calls
"

Or use search to find sessions mentioning the filename, then inspect the matching session's tool timeline.

Subagent identification

Subagent filenames (agent-a589d2e5...) don't describe their purpose. To map them:

1. In the main session, find Task tool_use blocks — the description field names the subagent (e.g., "Implement Phase 1: Config + Types")
2. The id returned in the tool_result matches the subagent filename suffix
3. Cross-reference with TaskOutput blocks to see what each subagent returned

# List all subagents with their sizes and durations
for f in /path/to/session-uuid/subagents/*.jsonl; do
    python3 ~/.claude/skills/session/analyzer.py summary "$f" 2>&1 | grep -E "(Path|Size|Duration)"
done

Session efficiency

1. Compare cache_read_input_tokens vs cache_creation_input_tokens — high cache reads = good reuse
2. Check tool result sizes — large results waste context
3. Count how many times the same file was read (violation of "never read twice" rules)
4. Look at the progress message count — very high counts may indicate spinning

What the analyzer does NOT do

The tool handles listing, searching (with context snippets), summarizing (with --deep aggregation), conversation extraction, tool timelines, and session comparison (diff). For these analyses, read the JSONL directly:

• Per-turn token breakdown — the tool shows totals, not per-turn growth curves
• Cross-agent duplication — use diff for two sessions, but not for duplication within a session's subagents
• Confusion/deviation signals — requires keyword search through assistant message text (see recipe above)
• File provenance — requires searching tool_use blocks for Write/Edit calls with specific file paths