{"slug":"session-deep-dive","title":"session-deep-dive","summary":"Deep qualitative analysis of high-signal sessions. Spawns subagents with v2 template, synthesizes patterns, compares against known findings. Use after /session-scan.","platform":"Claude","tags":[],"authorName":"LLM Mart","authorSlug":"llm-mart","score":0,"source":"github","price":null,"verified":false,"createdAt":"2026-10-04T15:14:20.206656Z","repo":{"url":"https://github.com/oliver-kriska/claude-elixir-phoenix","stars":560,"forks":44,"license":"MIT","updatedAt":"2026-10-02T04:11:38Z"},"bodyHtml":"
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name: session-deep-dive\ndescription: Deep qualitative analysis of high-signal sessions. Spawns subagents with v2 template, synthesizes patterns, compares against known findings. Use after /session-scan.\nargument-hint: \"

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Session Deep Dive (Tier 2)

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Qualitative analysis of high-signal sessions identified by /session-scan.\nSpawns subagents with pre-computed metrics context for focused analysis.

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Requirements

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Requires ccrider MCP. If not available:

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ccrider MCP is required. See: https://github.com/neilberkman/ccrider

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Usage

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/session-deep-dive ffa155ee-ed8a-492c-8797-878fcbec4d9e\n/session-deep-dive --last                    # Most recent Tier 2 eligible\n/session-deep-dive --from-scan               # All Tier 2 eligible from last scan\n/session-deep-dive --from-scan --compare .claude/UPDATED_PLUGIN_REPORT_160_SESSIONS.md\n
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Pipeline

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Step 1: Resolve Target Sessions

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From $ARGUMENTS:

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If no metrics.jsonl exists, tell the user:

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No metrics found. Run /session-scan first to discover and score sessions.

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Step 2: Load Pre-computed Metrics

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For each target session, read its entry from metrics.jsonl.\nFormat the metrics as a context block for subagent prompts:

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## Pre-computed Metrics (from /session-scan)\n\n- Friction: 0.42 (retry_loops: 1, user_corrections: 3, approach_changes: 2)\n- Fingerprint: bug-fix (confidence: 0.85)\n- Plugin opportunity: 0.65 (could use: investigate, quick)\n- Tool profile: Read 28.7%, Edit 15.2%, Bash 19.3%, Tidewave 22.8%\n- Duration: 78 minutes, 19 user messages, 171 tool calls\n
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Determine PROJECT_ROOT from current working directory.

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Step 3: Fetch Transcripts — One Subagent Per Session

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CRITICAL: One ccrider call = one subagent. Full transcripts are\n5-30KB each. Even 3 per worker floods the worker's context.

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For EACH session, spawn a haiku subagent:

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Task(subagent_type=\"general-purpose\", model=\"haiku\", mode=\"bypassPermissions\", prompt=\"\"\"\nFetch one session transcript and save it.\n\n1. mcp__ccrider__get_session_messages(session_id: \"{SESSION_ID}\")\n   If > 200 messages: use last_n: 200\n\n2. Write transcript to {PROJECT_ROOT}/.claude/session-analysis/{SHORT_ID}-transcript.md\n   Format:\n   # Session: {SHORT_ID}\n   Project: {PROJECT}\n   Date: {DATE}\n   Messages: {COUNT}\n\n   ## Messages\n   ### User (seq N)\n   {content}\n   ### Assistant (seq N)\n   {content}\n\n3. Report: \"Wrote {SHORT_ID}-transcript.md ({N} messages)\"\n\"\"\")\n
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Spawn ALL fetch subagents in parallel. Wait for all to complete.

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Step 4: Analyze Sessions

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Read the analysis template — inline it into subagent prompts:

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Glob: **/session-deep-dive/references/analysis-template-v2.md\n
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ALWAYS use subagents — never analyze in main context.

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Each analysis subagent prompt:

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Read the session transcript at .\nApply the analysis template below to analyze this session.\nThe pre-computed metrics below give you quantitative context —\nvalidate them and add qualitative depth.

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Write your report (under 200 lines) to .

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Reports go to .claude/session-analysis/{short_id}-report.md.

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Step 5: Compress (if 3+ sessions)

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If 3+ sessions analyzed, spawn context-supervisor (haiku) to compress:

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Read all report files in .claude/session-analysis/*-report.md.\nWrite a consolidated summary to .claude/session-analysis/summaries/consolidated.md.\nPreserve: friction patterns, plugin opportunities, evidence strength tags.\nRemove: per-file details, generic observations, repeated context.

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Step 6: Synthesize

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Read the synthesis template:

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Glob: **/session-deep-dive/references/synthesis-template.md\n
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Read the --compare report (or latest insights file).\nRead MEMORY.md for known findings.

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If 3+ sessions: read summaries/consolidated.md (NOT individual reports).\nIf 1-2 sessions: read individual reports directly.

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Produce synthesis comparing:

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Step 7: Update Ledger

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Use Python to safely update metrics.jsonl — never manually\nread/modify/rewrite in the LLM context:

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python3 -c \"\nimport json\nids = {SESSION_IDS_SET}  # e.g., {'ffa155ee-...', '90a74843-...'}\nlines = open('{PROJECT_ROOT}/.claude/session-metrics/metrics.jsonl').readlines()\nwith open('{PROJECT_ROOT}/.claude/session-metrics/metrics.jsonl', 'w') as f:\n    for line in lines:\n        entry = json.loads(line)\n        if entry.get('session_id') in ids:\n            entry['tier2_completed'] = True\n        f.write(json.dumps(entry) + '\\n')\n\"\n
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Step 8: Write Output

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Write synthesis to .claude/session-analysis/insights-{date}.md

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Present key findings directly in conversation. Tell user:

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Full report: .claude/session-analysis/insights-{date}.md\nPer-session reports: .claude/session-analysis/{id}-report.md

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Output Files

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FilePurpose
.claude/session-analysis/{id}-transcript.mdRaw transcript
.claude/session-analysis/{id}-report.mdPer-session analysis
.claude/session-analysis/summaries/consolidated.mdCompressed reports
.claude/session-analysis/insights-{date}.mdCross-session synthesis
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Iron Laws

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  1. ONE ccrider call = ONE subagent — never batch multiple fetches
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  2. NEVER fetch or analyze in main context — always subagents
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  3. Absolute paths in subagent prompts — subagents don't inherit skill context
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  4. Python for jsonl updates — never manually rewrite in LLM context
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  5. ALWAYS pass pre-computed metrics to analysis subagents — don't re-derive
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  6. NEVER skip synthesis — cross-session patterns are the real value
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  7. TAG evidence strength — every finding must be STRONG/MODERATE/WEAK
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