--- name: phoenixclaw description: | Passive journaling skill that scans daily conversations from ALL session paths (main, agents, cron) via cron to generate markdown journals using semantic understanding.

Use when: - User requests journaling ("Show me my journal", "What did I do today?") - User asks for pattern analysis ("Analyze my patterns", "How am I doing?") - User requests summaries ("Generate weekly/monthly summary") metadata: version: 0.0.19 ---

PhoenixClaw: Zero-Tag Passive Journaling

PhoenixClaw automatically distills daily conversations into meaningful reflections using semantic intelligence.

Automatically identifies journal-worthy moments, patterns, and growth opportunities.

🛠️ Core Workflow

> [!critical] MANDATORY: Complete Workflow Execution > This 9-step workflow MUST be executed in full regardless of invocation method: > - Cron execution (10 PM nightly) > - Manual invocation ("Show me my journal", "Generate today's journal", etc.) > - Regeneration requests ("Regenerate my journal", "Update today's entry") > > Never skip steps. Partial execution causes: > - Missing images (session logs not scanned) > - Missing finance data (Ledger plugin not triggered) > - Incomplete journals (plugins not executed)

PhoenixClaw follows a structured pipeline to ensure consistency and depth:

1. User Configuration: Check for ~/.phoenixclaw/config.yaml. If missing, initiate the onboarding flow defined in references/user-config.md.
2. Context Retrieval:

- Scan memory files (NEW): Read memory/YYYY-MM-DD.md and memory/YYYY-MM-DD-*.md files for manually recorded daily reflections. These files contain personal thoughts, emotions, and context that users explicitly ask the AI to remember via commands like "记一下" (remember this). CRITICAL: Do not skip these files - they contain explicit user reflections that session logs may miss. - Scan session logs: Call memory_get for the current day's memory, then CRITICAL: Scan ALL raw session logs and filter by message timestamp. Session files are often split across multiple files. Do NOT classify images by session file mtime: ```bash # Read all session logs from ALL known OpenClaw locations, then filter by per-message timestamp # Use timezone-aware epoch range to avoid UTC/local-day mismatches. TARGET_DAY="$(date +%Y-%m-%d)" TARGET_TZ="${TARGET_TZ:-Asia/Shanghai}" read START_EPOCH END_EPOCH < <( python3 - <<'PY' "$TARGET_DAY" "$TARGET_TZ" from datetime import datetime, timedelta from zoneinfo import ZoneInfo import sys

day, tz = sys.argv[1], sys.argv[2] start = datetime.strptime(day, "%Y-%m-%d").replace(tzinfo=ZoneInfo(tz)) end = start + timedelta(days=1) print(int(start.timestamp()), int(end.timestamp())) PY )

# Recursively scan all session directories (multi-agent architecture support) for dir in "$HOME/.openclaw/sessions" \ "$HOME/.openclaw/agents" \ "$HOME/.openclaw/cron/runs" \ "$HOME/.agent/sessions"; do [ -d "$dir" ] || continue find "$dir" -type f -name "*.jsonl" -print0 done | xargs -0 jq -cr --argjson start "$START_EPOCH" --argjson end "$END_EPOCH" ' (.timestamp // .created_at // empty) as $ts | ($ts | split(".")[0] + "Z" | fromdateiso8601?) as $epoch | select($epoch != null and $epoch >= $start and $epoch < $end) ' ``` Read all matching files regardless of their numeric naming (e.g., file_22, file_23 may be earlier in name but still contain today's messages). - EXTRACT IMAGES FROM SESSION LOGS: Session logs contain type: "image" entries with file paths. You MUST: 1. Find all image entries (e.g., "type":"image") 2. Keep only entries where message timestamp is in the target date range 3. Extract the file_path or url fields 4. Copy files into assets/YYYY-MM-DD/ 5. Rename with descriptive names when possible - Why session logs are mandatory: memory_get returns text only. Image metadata, photo references, and media attachments are only available in session logs. Skipping session logs = missing all photos. - Activity signal quality: Do not treat heartbeat/cron system noise as user activity. Extract user/assistant conversational content and media events first, then classify moments. - FILTER HEARTBEAT MESSAGES (CRITICAL): Session logs contain system heartbeat messages that MUST be excluded from journaling. When scanning messages, SKIP any message matching these criteria: 1. User heartbeat prompts: Messages containing "Read HEARTBEAT.md" AND "reply HEARTBEAT_OK" 2. Assistant heartbeat responses: Messages containing ONLY "HEARTBEAT_OK" (with optional leading/trailing whitespace) 3. Cron system messages: Messages with role "system" or "cron" containing job execution summaries (e.g., "Cron job completed", "A cron job")

Example jq filter to exclude heartbeats: ```jq # Exclude heartbeat messages | select( (.message.content? | type == "array" and (.message.content | map(.text?) | join("") | test("Read HEARTBEAT\.md"; "i") | not)) and (.message.content? | type == "array" and (.message.content | map(.text?) | join("") | test("^\\sHEARTBEAT_OK\\s$"; "i") | not)) ) ``` - Edge case - Midnight boundary: For late-night activity that spans midnight, expand the timestamp range to include spillover windows (for example, previous day 23:00-24:00) and still filter per-message by timestamp. - Merge sources: Combine content from both memory files and session logs. Memory files capture explicit user reflections; session logs capture conversational flow and media. Use both to build complete context. - Fallback: If memory is sparse, reconstruct context from session logs, then update memory so future runs use the enriched memory. Incorporate historical context via memory_search (skip if embeddings unavailable)

1. Moment Identification: Identify "journal-worthy" content: critical decisions, emotional shifts, milestones, or shared media. See references/media-handling.md for photo processing. This step generates the moments data structure that plugins depend on.

Image Processing (CRITICAL): - For each extracted image, generate descriptive alt-text via Vision Analysis - Categorize images (food, selfie, screenshot, document, etc.)

Filter Finance Screenshots (NEW): Payment screenshots (WeChat Pay, Alipay, etc.) should NOT be included in the journal narrative. These are tool images, not life moments.

Detection criteria (check any): 1. OCR keywords: "支付成功", "支付完成", "微信支付", "支付宝", "订单号", "交易单号", "¥" + amount 2. Context clues: Image sent with nearby text containing "记账", "支付", "付款", "转账" 3. Visual patterns: Standard payment app UI layouts (green WeChat, blue Alipay)

Handling rules: - Mark as finance_screenshot type - Route to Ledger plugin (if enabled) for transaction recording - EXCLUDE from journal main narrative unless explicitly described as part of a life moment (e.g., "今天请朋友吃饭" with payment screenshot) - Never include raw payment screenshots in daily journal images section

- Match images to moments (e.g., breakfast photo → breakfast moment) - Store image metadata with moments for journal embedding

1. Pattern Recognition: Detect recurring themes, mood fluctuations, and energy levels. Map these to growth opportunities using references/skill-recommendations.md.

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