phoenixclaw

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:
• User Configuration: Check for ~/.phoenixclaw/config.yaml. If missing, initiate the onboarding flow defined in references/user-config.md.
• 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:
• # 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
• )
• for dir in "$HOME/.openclaw/sessions" \
• "$HOME/.openclaw/agents" \
• "$HOME/.openclaw/cron/runs" \
• "$HOME/.agent/sessions"; do
• [ -d "$dir" ] || continue
• find "$dir" -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 < $end)
• '
• ```
• Read **all matching files** regardless of their numeric naming (e.g., file_22, file_23 may be earlier in name but still contain today's messages).
• **EXTRACT IMAGES FROM SESSION LOGS**: Session logs contain `type: "image"` entries with file paths. You MUST:
• 1. Find all image entries (e.g., `"type":"image"`)
• 2. Keep only entries where message `timestamp` is in the target date range
• 3. Extract the `file_path` or `url` fields
• 4. Copy files into `assets/YYYY-MM-DD/`
• 5. Rename with descriptive names when possible
• **Why session logs are mandatory**: `memory_get` returns **text only**. Image metadata, photo references, and media attachments are **only available in session logs**. Skipping session logs = missing all photos.
• **Activity signal quality**: Do not treat heartbeat/cron system noise as user activity. Extract user/assistant conversational content and media events first, then classify moments.
• **FILTER HEARTBEAT MESSAGES (CRITICAL)**: Session logs contain system heartbeat messages that MUST be excluded from journaling. When scanning messages, SKIP any message matching these criteria:
• 1. **User heartbeat prompts**: Messages containing "Read HEARTBEAT.md" AND "reply HEARTBEAT_OK"
• 2. **Assistant heartbeat responses**: Messages containing ONLY "HEARTBEAT_OK" (with optional leading/trailing whitespace)
• 3. **Cron system messages**: Messages with role "system" or "cron" containing job execution summaries (e.g., "Cron job completed", "A cron job")
• Example jq filter to exclude heartbeats:
• ```jq
• # Exclude heartbeat messages
• | select(
• (.message.content? | type == "array" and
• (.message.content | map(.text?) | join("") |
• test("Read HEARTBEAT\.md"; "i") | not))
• and
• (.message.content? | type == "array" and
• (.message.content | map(.text?) | join("") |
• test("^\\s*HEARTBEAT_OK\\s*$"; "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)
• 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):
• 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
• Context clues: Image sent with nearby text containing "记账", "支付", "付款", "转账"
• 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
• Pattern Recognition: Detect recurring themes, mood fluctuations, and energy levels. Map these to growth opportunities using references/skill-recommendations.md.
• Plugin Execution: Execute all registered plugins at their declared hook points. See references/plugin-protocol.md for the complete plugin lifecycle:
• pre-analysis → before conversation analysis
• post-moment-analysis → Ledger and other primary plugins execute here
• post-pattern-analysis → after patterns detected
• journal-generation → plugins inject custom sections
• post-journal → after journal complete
• Journal Generation: Synthesize the day's events into a beautiful Markdown file using assets/daily-template.md. Follow the visual guidelines in references/visual-design.md. Include all plugin-generated sections at their declared section_order positions.
• Embed curated images only, not every image. Prioritize highlights and moments.
• Route finance screenshots to Ledger sections (receipts, invoices, transaction proofs).
• Use Obsidian format from references/media-handling.md with descriptive captions.
• Generate image links from filesystem truth: compute the image path relative to the current journal file directory. Never output absolute paths.
• Do not hardcode path depth (../ or ../../): calculate dynamically from daily_file_path and image_path.
• Use copied filename as source of truth: if asset file is image_124917_2.jpg, the link must reference that exact filename.
• Timeline Integration: If significant events occurred, append them to the master index in timeline.md using the format from assets/timeline-template.md and references/obsidian-format.md.
• Growth Mapping: Update growth-map.md (based on assets/growth-map-template.md) if new behavioral patterns or skill interests are detected.
• Profile Evolution: Update the long-term user profile (profile.md) to reflect the latest observations on values, goals, and personality traits. See references/profile-evolution.md and assets/profile-template.md.

⏰ Cron & Passive Operation

PhoenixClaw is designed to run without user intervention. It utilizes OpenClaw's built-in cron system to trigger its analysis daily at 10:00 PM local time (0 22 * * *). Setup details can be found in references/cron-setup.md. Mode: Primarily Passive. The AI proactively summarizes the day's activities without being asked.

Rolling Journal Window (NEW)

To solve the 22:00-24:00 content loss issue, PhoenixClaw now supports a rolling journal window mechanism: Problem: Fixed 24-hour window (00:00-22:00) misses content between 22:00-24:00 when journal is generated at 22:00. Solution: scripts/rolling-journal.js scans from last journal time → now instead of fixed daily boundaries. Features: Configurable schedule hour (default: 22:00, customizable via ~/.phoenixclaw/config.yaml) Rolling window: No content loss even if generation time varies Backward compatible with existing late-night-supplement.js Configuration (~/.phoenixclaw/config.yaml): schedule: hour: 22 # Journal generation time minute: 0 rolling_window: true # Enable rolling window (recommended) Usage: # Default: generate from last journal to now node scripts/rolling-journal.js # Specific date node scripts/rolling-journal.js 2026-02-12

💬 Explicit Triggers

While passive by design, users can interact with PhoenixClaw directly using these phrases: "Show me my journal for today/yesterday." "What did I accomplish today?" "Analyze my mood patterns over the last week." "Generate my weekly/monthly summary." "How am I doing on my personal goals?" "Regenerate my journal." / "重新生成日记" [!warning] Manual Invocation = Full Pipeline When users request journal generation/regeneration, you MUST execute the complete 9-step Core Workflow above. This ensures: Photos are included (via session log scanning) Ledger plugin runs (via post-moment-analysis hook) All plugins execute (at their respective hook points) Common mistakes to avoid: ❌ Only calling memory_get (misses photos) ❌ Skipping moment identification (plugins never trigger) ❌ Generating journal directly without plugin sections

References (references/)

user-config.md: Initial onboarding and persistence settings. cron-setup.md: Technical configuration for nightly automation. plugin-protocol.md: Plugin architecture, hook points, and integration protocol. media-handling.md: Strategies for extracting meaning from photos and rich media. session-day-audit.js: Diagnostic utility for verifying target-day message coverage across session logs. visual-design.md: Layout principles for readability and aesthetics. obsidian-format.md: Ensuring compatibility with Obsidian and other PKM tools. profile-evolution.md: How the system maintains a long-term user identity. skill-recommendations.md: Logic for suggesting new skills based on journal insights.

Assets (assets/)

daily-template.md: The blueprint for daily journal entries. weekly-template.md: The blueprint for high-level weekly summaries. profile-template.md: Structure for the profile.md persistent identity file. timeline-template.md: Structure for the timeline.md chronological index. growth-map-template.md: Structure for the growth-map.md thematic index.