Update Timeline Skill

Update analysis/events-chronological.md with deep understanding of all project communications.

Source Registry

This skill uses a source registry to detect and parse different data formats.

Loading Sources

•Read sources/_registry.json from this skill directory
•For each registered source, check if detectPattern matches any files
•Track which sources are present

Unknown Source Detection

After checking registered sources, scan unknownDataPaths for directories not in the registry:

markdown

Warning: Unknown data sources detected:
- data/messaging/telegram/ (not in registry)
- data/raw/linear/ (not in registry)

To add support, document the format in source-formats.md and add to sources/_registry.json

Continue processing known sources, but warn about unknown ones.

Execution Steps (FOLLOW EXACTLY)

Step 1: Load State (EXACT)

code

Read `.claude/state/timeline-state.json`
If file doesn't exist → treat all files as new
Parse JSON → store in memory as `state`

Step 2: Discover Sources (EXACT)

code

Run: Glob("data/transcripts/hyprnote/*/_meta.json") → hyprnote_sessions
Run: Glob("data/transcripts/notion/*.md") → notion_files (exclude readme.md)
Run: Glob("data/messaging/whatsapp/*/_chat.txt") → whatsapp_chats
Run: Glob("data/messaging/hailer/*.md") → hailer_files (exclude readme.md)

Step 3: Build Unified Source List (EXACT)

code

For each hyprnote session:
  - Read _meta.json → extract created_at
  - Add to sources: {type: "hyprnote", date: created_at, path: folder_path}

For each notion file:
  - Extract date from filename (YYYY-MM-DDTHH_MM_SSZ.md)
  - Add to sources: {type: "notion", date: extracted_date, path: file_path}

For whatsapp:
  - Add to sources: {type: "whatsapp", date: "continuous", path: folder_path}

For hailer:
  - Add to sources: {type: "hailer", date: "continuous", path: folder_path}

Sort sources by date DESCENDING (newest first)

Step 4: Identify Changes (EXACT)

code

For each source in sorted list:
  If type == "hyprnote" or type == "notion":
    If path NOT in state.processed_files[type] → mark as NEW
    Else if file mtime > state.processed_files[type][path].processed_at → mark as CHANGED

  If type == "whatsapp":
    Read first 100 chars of _chat.txt
    If chars != state.whatsapp.file_hash → mark as CHANGED (re-process ALL)

  If type == "hailer":
    For each file in hailer_files:
      Read first 100 chars
      If chars != state.hailer.files[path].file_hash → mark as CHANGED (re-process ALL)

Step 5: Process Each Source (PRESCRIPTIVE + INTERPRETIVE)

For Hyprnote (EXACT steps, then INTERPRET):

code

1. Read _meta.json → extract title, created_at
2. Read _transcript.json → extract transcripts[0].words array
3. Reconstruct text: words.map(w => w.text).join('').trim()
4. Group words by channel field (speaker separation)
5. Check if _memo.md exists → if yes, read it
6. [INTERPRET] Write 2-4 sentence summary using transcript + memo
7. [INTERPRET] Extract decisions, action items, key insights
8. [INTERPRET] Identify participants and roles
9. [INTERPRET] Rate relevance (High/Medium/Low)

For Notion (EXACT steps, then INTERPRET):

code

1. Read entire .md file
2. [INTERPRET] Identify speakers from text patterns
3. [INTERPRET] Write 2-4 sentence summary
4. [INTERPRET] Extract decisions, action items, key insights
5. [INTERPRET] Identify participants and roles
6. [INTERPRET] Rate relevance

For WhatsApp (EXACT steps, then INTERPRET):

code

1. Read entire _chat.txt
2. Parse messages: regex [MM/DD/YY, HH:MM:SS] Sender: Message
3. List all attachments in folder
4. For each image attachment → Read image, describe visual content
5. For each document (.pdf, .md, .docx) → Read and summarize
6. [INTERPRET] Group messages into topic clusters (time gaps >24h, topic shifts)
7. [INTERPRET] For each cluster: write summary, list key events
8. [INTERPRET] Rate relevance per cluster

For Hailer (EXACT steps, then INTERPRET):

code

1. Read ALL .md files in folder
2. Parse message format (Username, timestamp, content)
3. Combine into chronological order
4. [INTERPRET] Group into topic clusters
5. [INTERPRET] Write summary, extract key points
6. [INTERPRET] Identify participants
7. [INTERPRET] Rate relevance

Step 6: Generate Output (EXACT)

code

1. Sort all processed entries by date ASCENDING (oldest first for output)
2. Group by month
3. Write to analysis/events-chronological.md using output-template.md format
4. Include YAML frontmatter with counts

Step 7: Validate Output (EXACT CHECKLIST)

code

Before finishing, verify each entry has:
- [ ] Substantive summary (not just "Meeting occurred" or title repeat)
- [ ] At least one of: decision, action item, or key insight
- [ ] Participants listed
- [ ] Relevance rating

For Hyprnote with _memo.md:
- [ ] Memo content incorporated in entry

For WhatsApp:
- [ ] All images have visual descriptions
- [ ] All documents have content summaries
- [ ] Clusters have date ranges

If any check fails → fix before proceeding

Step 8: Update State (EXACT)

code

Update state.last_updated = current ISO timestamp
For each processed source:
  Add/update entry in state.processed_files with:
    - processed_at: current timestamp
    - deep_processed: true
    - summary: first 100 chars of generated summary
Write state to .claude/state/timeline-state.json

Supporting Files

•Processing Rules - Deep understanding guidelines
•Source Formats - Parsing reference
•Output Template - Entry format specification