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:
6. User heartbeat prompts: Messages containing "Read HEARTBEAT.md" AND "reply HEARTBEAT_OK"
7. Assistant heartbeat responses: Messages containing ONLY "HEARTBEAT_OK" (with optional leading/trailing whitespace)
8. 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.

1. 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

1. 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.

1. 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.

1. Growth Mapping: Update growth-map.md (based on assets/growth-map-template.md) if new behavioral patterns or skill interests are detected.

1. 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

📚 Documentation Reference

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.