title: Root cause of context crash identified

insight: The crash happened because Genesis was flying blind with no visibility into context usage - no StatusLine, no PreCompact hook, no auto-compact tuning, and no CTM trigger

reasoning_chain: Agent a1d6f2d completed research on token alert systems

reasoning_chain: Research revealed StatusLine is the ONLY mechanism receiving live context_window data

reasoning_chain: Analysis of hooks showed no PreCompact hook existed

reasoning_chain: No token monitoring was connected to memory commit system

reasoning_chain: This meant context hit 100% with zero warning before crash

source_context: Root Cause: We were FLYING BLIND - No StatusLine configured -- zero visibility into context usage, No PreCompact hook -- no emergency save before compaction, No auto-compact tuning -- default threshold was too late, No CTM trigger -- context hit 100% with no warning


title: 4-Layer Defense System for context management

insight: Deploying four complementary solutions: StatusLine for real-time monitoring, Context Tracker for CTM triggers at thresholds, PreCompact hook for emergency backups, and auto-compact tuning at 85%

reasoning_chain: Research confirmed StatusLine receives live context data while hooks do not

reasoning_chain: Hooks can read transcript JSONL but StatusLine is more reliable

reasoning_chain: PreCompact hook fires before compaction - last chance to save state

reasoning_chain: Default auto-compact threshold ~83.5% is too late, need earlier trigger

reasoning_chain: Four solutions working together create defense in depth

source_context: Layer 1: StatusLine -- real-time context % bar in terminal, Layer 2: Context Tracker -- CTM at 50% warn at 65% CRITICAL at 80%, Layer 3: PreCompact CTM -- auto-backup transcript before compaction, Layer 4: Auto-compact at 85% -- CLAUDE_AUTOCOMPACT_PCT_OVERRIDE=85


title: StatusLine-to-State-File bridge pattern

insight: The key architectural insight is that StatusLine is the only hook receiving live context_window data, so it writes to a state file that other hooks read - creating a data bridge

reasoning_chain: Research found only StatusLine receives context_window JSON with used_percentage

reasoning_chain: Hooks do NOT receive token data in their stdin

reasoning_chain: PostToolUse hook can parse transcript but it's inefficient

reasoning_chain: Solution: StatusLine writes current.json, PostToolUse reads it

reasoning_chain: This bridge pattern solves the data access problem

source_context: The key architectural insight is the StatusLine-to-State-File bridge: StatusLine is the only mechanism that receives live context_window data. By having the statusline script write a state file (current.json), the PostToolUse hook can read that file to get accurate token counts without parsing the full transcript every time.


title: 16.5% autocompact buffer hidden trap

insight: The remaining_percentage in StatusLine includes a ~16.5% autocompact buffer that is NOT actually usable - when StatusLine shows 25% remaining, only 8.5% is truly available

reasoning_chain: Claude Code has internal autocompact buffer

reasoning_chain: StatusLine reports remaining_percentage this buffer

reasoning_chain: includingUsers see 25% remaining but compaction triggers earlier

reasoning_chain: Actual free = remaining_percentage - 16.5%

reasoning_chain: This surprises many users and causes unexpected crashes

source_context: The 16.5% Buffer Problem: The remaining_percentage includes a ~16.5% autocompact buffer that is NOT actually usable. To get real free space: actual_free = remaining_percentage - 16.5


title: Overnight evolution directive

insight: Kinan wants maximum system evolution overnight - evolve memory architecture, all MVPs, and ensure respawn/persistence so work survives

reasoning_chain: Kinan is going to sleep and wants system to maximize capabilities overnight

reasoning_chain: Key requirement: evolve memory architecture of knowledge base

reasoning_chain: Key requirement: evolve all MVPs across all fronts

reasoning_chain: Critical constraint: must respawn early to avoid being dead in water

reasoning_chain: Everything must write to E: drive files for persistence

source_context: I want you to do everything possible to max out the limits of the entire Genesis system stack so we can achieve maximum power capability improve the system overnight evolve the system evolve the memory of all the capabilities evolved the memory architecture of the knowledge base evolve all mvp's across all fronts and I wake up in the morning hoping for some really nice surprises make sure you respawn early otherwise we're dead in the water that's very important we must have persistence


title: post_edit_verify.py missing causing hook failures

insight: The PostToolUse hook for Edit|Write is silently failing because post_edit_verify.py doesn't exist despite being referenced in settings.json

reasoning_chain: Research found post_edit_verify.py referenced in settings.json

reasoning_chain: Hooks with missing command files fail silently

reasoning_chain: This means C: drive protection and SQLite detection are not running

reasoning_chain: Need to create this file to enable the protection

source_context: post_edit_verify.py is MISSING -- Referenced in your current settings.json at /mnt/e/genesis-system/.claude/hooks/post_edit_verify.py but the file does not exist. This means the PostToolUse hook for Edit|Write is silently failing.


type: DIRECTIVE

title: Persistence through disk writes

insight: Every agent must write output to E: drive files - if the session dies, work survives on disk. This is the key persistence insurance.

reasoning_chain: Today's crash killed 4 agents because their work was in memory

reasoning_chain: Lesson learned: nothing should be ephemeral

reasoning_chain: Every agent task writes to a file on E: drive

reasoning_chain: War room state saved to memory for session recovery

reasoning_chain: If Genesis dies, all work persists on disk

source_context: The key lesson from today's crash: every agent writes its output to a file on E: drive. Nothing ephemeral. If this session dies, the work survives on disk.


title: AI Ultra access for Jules force multiplication

insight: Google AI Ultra access activated gives Jules 20x higher limits enabling parallel multi-repo development - a force multiplier for the swarm

reasoning_chain: Kinan activated AI Ultra Access at AgileAdapt

reasoning_chain: This gives Jules 20x higher limits for multi-agent workflows

reasoning_chain: Enables Gemini 2.5 Pro full access via Google AI Studio

reasoning_chain: Can now fire Jules at multiple repos simultaneously for parallel development

source_context: Google AI Ultra activated for AgileAdapt. That gives us: Jules: 20x higher limits (multi-agent workflows, heavy async coding), Gemini 2.5 Pro: Full access via Google AI Studio, Higher quotas across all Google AI products. This is a force multiplier -- we can now fire Jules at multiple repos simultaneously for parallel development.


title: Duplex Voice immediate upgrade path identified

insight: LiveKit + Gemini 2.5 Flash Native Audio at ~$0.03/min is the immediate upgrade path for AIVA, with PersonaPlex for true full-duplex in phase 3

reasoning_chain: Agent 3 completed Duplex Voice Research (558 lines)

reasoning_chain: Analyzed 9 vendors: PersonaPlex, Sesame CSM-1B, LiveKit, ElevenLabs, Gemini Live, OpenAI Realtime

reasoning_chain: LiveKit + Gemini 2.5 Flash is cheapest at $0.03/min with acceptable latency

reasoning_chain: PersonaPlex offers true full-duplex but needs 24GB+ VRAM

reasoning_chain: Phase 1 immediate, Phase 3 for full-duplex

source_context: Agent 3 COMPLETE: Duplex Voice Research -- 558 lines at Research reports/DUPLEX_VOICE_RESEARCH.md. Key finding: LiveKit + Gemini 2.5 Flash Native Audio at ~$0.03/min is the immediate upgrade path for AIVA. PersonaPlex for true full-duplex later.


title: Sunaiva MVP evolution to $12.5K MRR

insight: The Sunaiva evolution plan outlines a 5-sprint path to $12.5K MRR with Supabase Auth, Stripe billing, and Railway deployment

reasoning_chain: Agent 6 completed Sunaiva MVP Evolution (1,233 lines)

reasoning_chain: Full codebase audit of server/main.py, parser.py, extractor.py, mcp_generator.py, mcp/server.py

reasoning_chain: Identified 9 critical vulnerabilities (in-memory data loss, no auth, JSON storage)

reasoning_chain: Designed Supabase Auth and Stripe integration

reasoning_chain: Revenue projections: $12,551 MRR by month 6

source_context: Agent 6 COMPLETE: Sunaiva MVP Evolution -- 1,233 lines at Sunaiva/ai-memory/SUNAIVA_EVOLUTION_PLAN.md. Massive delivery: Full auth design (Supabase), Stripe integration, Railway deployment, admin dashboard, competitive analysis, PostgreSQL schema, 5-sprint roadmap, revenue projections hitting $12.5K MRR by month 6.


title: Alpha Evolve Cycle 21 adds 35 axioms

insight: Alpha Evolve Cycle 21 synthesized 6 source documents into 35 new axioms (12 browser_automation, 7 memory_architecture, 5 client_delivery), bringing total to 352

reasoning_chain: Agent 4 completed Alpha Evolve KG Cycle 21

reasoning_chain: Ingested 6 documents: BROWSER_USE_RESEARCH, GRAPHITI_FIX_STATUS, GEORGE_BUNKER_MONDAY_DEMO, 19 existing axiom files, ClawdBot conversations, MEMORY.md

reasoning_chain: Executed protocol: INGEST -> EXTRACT -> SYNTHESIZE -> HARDCODE -> VERIFY -> EVOLVE

reasoning_chain: Produced 35 new axioms with 0.95 average confidence

reasoning_chain: Identified discrepancy: reported 434+ axioms but verified 352 in JSONL files

source_context: Alpha Evolve Cycle 21 is complete. Sources Ingested: BROWSER_USE_RESEARCH.md (566 lines), GRAPHITI_FIX_STATUS.md (224 lines), GEORGE_BUNKER_MONDAY_DEMO.md (179 lines), 19 existing axiom JSONL files (317 axioms), ClawdBot research conversations, MEMORY.md session context. Output: 35 new axioms at 0.95 average confidence. Knowledge Graph Totals: 352 axioms (was 317).


title: CTM threshold strategy at 50%

insight: The Context Tracker should trigger CTM (Commit To Memory) at 50% context usage - proactively saving state before reaching danger zones

reasoning_chain: Research showed default auto-compact at ~83.5% is too late

reasoning_chain: At 50% context used, there's plenty of headroom for CTM operations

reasoning_chain: CTM at 50% gives time to save decisions, blockers, progress to supermemory

reasoning_chain: Then warn at 65%, CRITICAL at 80% before compaction

reasoning_chain: This proactive approach prevents the crash that killed 4 agents

source_context: At 50%: Trigger CTM at 50%, warn at 65%, CRITICAL at 80%. Begin wrapping up current task. Consider running CTM protocol soon.


title: pre_tool_check.py using wrong API fields

insight: The pre_tool_check.py hook reads 'tool' and 'args' but the official Claude Code API sends 'tool_name' and 'tool_input' - causing silent failures

reasoning_chain: Research found pre_tool_check.py in settings but using wrong field names

reasoning_chain: Official API: hook_input.get('tool_name') and hook_input.get('tool_input')

reasoning_chain: Current code reads: hook_input.get('tool') and hook_input.get('args')

reasoning_chain: Also outputs wrong format: {'continue': False} instead of proper hookSpecificOutput

source_context: pre_tool_check.py uses wrong field names -- Your current hook reads hook_input.get('tool') and hook_input.get('args') but the official API sends tool_name and tool_input. It also outputs {"continue": False} but the correct format is either exit 2 with stderr, or JSON with hookSpecificOutput.permissionDecision: "deny".