| name | session-deep-dive |
| description | Deep qualitative analysis of high-signal sessions. Spawns subagents with v2 template, synthesizes patterns, compares against known findings. Use after /session-scan. |
| argument-hint | <session-id> | --last | --from-scan [--compare REPORT.md] |
| disable-model-invocation | true |
Session Deep Dive (Tier 2)
Qualitative analysis of high-signal sessions identified by /session-scan.
Spawns subagents with pre-computed metrics context for focused analysis.
Requirements
Requires ccrider MCP. If not available:
ccrider MCP is required. See: https://github.com/neilberkman/ccrider
Usage
/session-deep-dive ffa155ee-ed8a-492c-8797-878fcbec4d9e
/session-deep-dive --last # Most recent Tier 2 eligible
/session-deep-dive --from-scan # All Tier 2 eligible from last scan
/session-deep-dive --from-scan --compare .claude/UPDATED_PLUGIN_REPORT_160_SESSIONS.md
Pipeline
Step 1: Resolve Target Sessions
From $ARGUMENTS:
- Session ID: Single session to analyze
--last: Most recent Tier 2 eligible session from metrics.jsonl--from-scan: All sessions where tier2_eligible: true AND
tier2_completed: false in .claude/session-metrics/metrics.jsonl--compare REPORT.md: Previous report to compare against
(default: most recent .claude/session-analysis/insights-*.md)
If no metrics.jsonl exists, tell the user:
No metrics found. Run /session-scan first to discover and score sessions.
Step 2: Load Pre-computed Metrics
For each target session, read its entry from metrics.jsonl.
Format the metrics as a context block for subagent prompts:
## Pre-computed Metrics (from /session-scan)
- Friction: 0.42 (retry_loops: 1, user_corrections: 3, approach_changes: 2)
- Fingerprint: bug-fix (confidence: 0.85)
- Plugin opportunity: 0.65 (could use: investigate, quick)
- Tool profile: Read 28.7%, Edit 15.2%, Bash 19.3%, Tidewave 22.8%
- Duration: 78 minutes, 19 user messages, 171 tool calls
Determine PROJECT_ROOT from current working directory.
Step 3: Fetch Transcripts — One Subagent Per Session
CRITICAL: One ccrider call = one subagent. Full transcripts are
5-30KB each. Even 3 per worker floods the worker's context.
For EACH session, spawn a haiku subagent:
Task(subagent_type="general-purpose", model="haiku", mode="bypassPermissions", prompt="""
Fetch one session transcript and save it.
1. mcp__ccrider__get_session_messages(session_id: "{SESSION_ID}")
If > 200 messages: use last_n: 200
2. Write transcript to {PROJECT_ROOT}/.claude/session-analysis/{SHORT_ID}-transcript.md
Format:
# Session: {SHORT_ID}
Project: {PROJECT}
Date: {DATE}
Messages: {COUNT}
## Messages
### User (seq N)
{content}
### Assistant (seq N)
{content}
3. Report: "Wrote {SHORT_ID}-transcript.md ({N} messages)"
""")
Spawn ALL fetch subagents in parallel. Wait for all to complete.
Step 4: Analyze Sessions
Read the analysis template — inline it into subagent prompts:
Glob: **/session-deep-dive/references/analysis-template-v2.md
ALWAYS use subagents — never analyze in main context.
- 1-6 sessions: Spawn sonnet subagents (one per session)
- 7+ sessions: Spawn haiku subagents for speed
Each analysis subagent prompt:
Read the session transcript at {transcript_path}.
Apply the analysis template below to analyze this session.
The pre-computed metrics below give you quantitative context —
validate them and add qualitative depth.
{metrics_context_block}
{analysis_template_content}
Write your report (under 200 lines) to {report_path}.
Reports go to .claude/session-analysis/{short_id}-report.md.
Step 5: Compress (if 3+ sessions)
If 3+ sessions analyzed, spawn context-supervisor (haiku) to compress:
Read all report files in .claude/session-analysis/*-report.md.
Write a consolidated summary to .claude/session-analysis/summaries/consolidated.md.
Preserve: friction patterns, plugin opportunities, evidence strength tags.
Remove: per-file details, generic observations, repeated context.
Step 6: Synthesize
Read the synthesis template:
Glob: **/session-deep-dive/references/synthesis-template.md
Read the --compare report (or latest insights file).
Read MEMORY.md for known findings.
If 3+ sessions: read summaries/consolidated.md (NOT individual reports).
If 1-2 sessions: read individual reports directly.
Produce synthesis comparing:
- New findings vs known patterns from MEMORY.md
- Confirmed patterns (seen before, still present)
- New patterns (not in previous reports)
- Resolved patterns (previously noted, no new occurrences)
Step 7: Update Ledger
Use Python to safely update metrics.jsonl — never manually
read/modify/rewrite in the LLM context:
python3 -c "
import json
ids = {SESSION_IDS_SET} # e.g., {'ffa155ee-...', '90a74843-...'}
lines = open('{PROJECT_ROOT}/.claude/session-metrics/metrics.jsonl').readlines()
with open('{PROJECT_ROOT}/.claude/session-metrics/metrics.jsonl', 'w') as f:
for line in lines:
entry = json.loads(line)
if entry.get('session_id') in ids:
entry['tier2_completed'] = True
f.write(json.dumps(entry) + '\n')
"
Step 8: Write Output
Write synthesis to .claude/session-analysis/insights-{date}.md
Present key findings directly in conversation. Tell user:
Full report: .claude/session-analysis/insights-{date}.md
Per-session reports: .claude/session-analysis/{id}-report.md
Output Files
| File | Purpose |
|---|
.claude/session-analysis/{id}-transcript.md | Raw transcript |
.claude/session-analysis/{id}-report.md | Per-session analysis |
.claude/session-analysis/summaries/consolidated.md | Compressed reports |
.claude/session-analysis/insights-{date}.md | Cross-session synthesis |
Iron Laws
- ONE ccrider call = ONE subagent — never batch multiple fetches
- NEVER fetch or analyze in main context — always subagents
- Absolute paths in subagent prompts — subagents don't inherit skill context
- Python for jsonl updates — never manually rewrite in LLM context
- ALWAYS pass pre-computed metrics to analysis subagents — don't re-derive
- NEVER skip synthesis — cross-session patterns are the real value
- TAG evidence strength — every finding must be STRONG/MODERATE/WEAK
