Weekly Project Review

v1.9.1 — Stricter YAML config parsing (loud failure on malformed .claude/CLAUDE.md); helper scripts for multi-tab Google Doc writes (replace-dashboard.py, insert-weekly-log.py); RTF/PDF transcript normalization (normalize-transcripts.py); hollow-transcript handling per-meeting; document-comment processing (read, apply corrections, reply, resolve).

Generate a comprehensive weekly summary for the current research project, pulling from multiple sources.

Public reference, not a complete install. This file describes a workflow that uses three external Python helpers: normalize-transcripts.py, replace-dashboard.py, and insert-weekly-log.py. Those helpers are not included in this public repository. Set up and review suitable helpers and account connections before running this workflow.

Overview

This skill collects data from WhatsApp, meeting transcripts, and Gmail, then generates:

1. Tab 1 content: High-level project dashboard (updated directly in Google Doc)
2. Tab 2 content: Detailed weekly log with thematic synthesis across all sources

Prerequisites

Before fetching, normalizing, archiving or writing anything, verify the three helper files at the configured WEEKLY_REVIEW_HELPERS path and the intended account/project configuration. If any helper is missing, stop and report the missing dependency. Do not invent a bundled scripts directory, silently skip that work, or substitute a different account. The public file alone does not provide those dependencies.

The skill expects three env vars on each machine (set in ~/.zprofile or equivalent):

export GOOGLE_WORKSPACE_MCP_USER_EMAIL="<your@email>"     # account to authenticate as
export GOOGLE_WORKSPACE_MCP_CREDENTIALS_FILE="<path>"     # optional; defaults to ~/.google_workspace_mcp/credentials/<email>.json
export WEEKLY_REVIEW_HELPERS="<path>"                     # required for a public adaptation; point to reviewed local helpers

If GOOGLE_WORKSPACE_MCP_USER_EMAIL is unset on a machine that has only one credentials file, scripts fall back to the legacy default. Multi-account machines must set the env var explicitly — the helper scripts do NOT silently glob-pick the first credentials file (this prevents silent wrong-account writes).

The skill also requires every project .claude/CLAUDE.md to carry a single ```yaml block under ## Structured Config (machine-readable). The prefetch parser only reads fenced blocks; loose top-level YAML in prose is ignored.

Instructions

Step 0: Read Project Configuration

• Read .claude/CLAUDE.md from the current project folder
• Extract ALL of: project name, Google Doc ID (google_doc_id), team roster, WhatsApp groups (with JIDs), folder paths, granola_folder or granola_folders, project_keywords, exclude_keywords
• Fields must appear inside ```yaml code blocks; loose top-level YAML is ignored
• Strip surrounding quotes from extracted values
• Extract project_type (default: "quantitative"). If project_type: qualitative, use the qualitative dashboard template
• Extract skip_meeting_log (boolean, default false). If true, Tab 2 generation is skipped entirely
• If config is missing required fields, stop and surface the errors — do not fall back to legacy mode silently

Step 1: Determine Date Range

• Check 10_AI_Collaboration/Weekly_Reviews/ for most recent weekly-review-*.md or whatsapp-summary-*.md file
• Use that date as start_date (or 7 days ago if none exists)
• end_date = today
• Tell user: "Generating weekly review for [start_date] to [end_date]"

Step 1.5: Populate Transcripts (Optional)

If you have a transcript export tool (e.g., granola-fetch, Otter export):

1. Check the project's configured transcripts folder for existing files
2. Fetch transcripts based on config:
   • If config has granola_folders (list): Run your fetcher once per folder, letting your routing config dispatch destinations
   • If config has granola_folder (single string): Run with that folder name
   • If neither field exists: Skip and continue with existing transcripts
   • Strip surrounding quotes from folder names before passing to CLI
3. Normalize transcripts (v1.9 — fixes RTF/PDF handling). Run the bundled normalize-transcripts.py:

   python3 "$WEEKLY_REVIEW_HELPERS/normalize-transcripts.py" \
     --folder "[transcripts_folder]" --json-output --apply
   

   This converts any .rtf (via textutil) or .pdf (via pdftotext) for which there's no full .txt sibling, archives the originals, and replaces hollow .txt stubs when a converted sidecar is available. The script is idempotent and a no-op when there's nothing to do.
4. Do NOT prompt for confirmation — transcript fetching and normalization are non-destructive.

Step 2: Collect Data (with graceful degradation)

2a. WhatsApp Messages

• For each group in whatsapp_groups:
  • Search for group using WhatsApp MCP (list_chats)
  • Do NOT rely on list_chats metadata for last-message dates — metadata is often stale
  • ALWAYS fetch actual messages using list_messages regardless of metadata
  • Filter to messages within date range
• If WhatsApp MCP fails: retry once. If still unavailable: Note "WhatsApp unavailable - skipped" and continue
• Save raw messages to 10_AI_Collaboration/Weekly_Reviews/whatsapp-summary-[date].md

2b. Meeting Transcripts

Read all transcript files from the configured transcripts folder. Support multiple formats: Granola export, manual markdown, Zoom .vtt files.

Hollow transcript check (apply per-transcript):

After reading each transcript, classify it before using it:

• FULL: ≥5,000 characters AND contains conversational speaker content (dialogue lines, speaker labels, or .vtt Zoom format)
• HOLLOW: <5,000 characters OR content starts with ### markdown headers OR primary structure is bullet points with no speaker attribution

For each hollow transcript:

• Note it in Data Source Alerts: "[Meeting name] ([date]): AI notes only — full transcript not yet processed. Detailed meeting record omitted."
• Do NOT include hollow transcripts in Detailed Meeting Records. Use only for background context.

If ALL transcripts in the period are hollow AND there are no other substantive sources: Stop and ask the user whether to retry later or proceed without detailed meeting records.

2c. Gmail Threads

• Date range: Same as other sources
• Filter criteria (emails must match at least one):
  • Sender OR recipient matches any team member email in roster
  • Subject or body contains project keywords from project_keywords
• Exclusions: Automated emails, calendar invites (.ics), newsletters, bulk mail; threads matching exclude_keywords
• Gmail Retrieval Strategy: Use individual get_gmail_message_content calls (avoid batch size limits). If batch fails: fall back to individual fetches
• Feedback loop: At end of Gmail section, list included/excluded counts for noise control
• If Gmail MCP unavailable: Note "Gmail unavailable - skipped" and continue

2d. Sensitive Content Filtering

The weekly summary may be shared with the full team. Screen for and exclude sensitive content from ALL sources:

Exclude: Critical performance comments, hiring/firing discussions, pay/compensation, personnel-specific funding decisions, anything inappropriate for full team.

Screening by source type:

• General team meetings: Low sensitivity — include freely
• PI-only or PI + Research Manager threads: Screen MORE AGGRESSIVELY but include non-sensitive content
• 1-on-1 transcripts: Review for sensitive topics before including

When in doubt: Omit specific sensitive content and note "[Some content omitted — PI review recommended]"

Include freely: Funding strategy, project decisions, action items, research design, logistics, strategic direction.

Step 2e: Verify Key Metrics Against Authoritative Sources

Verify all quantitative claims against authoritative sources before generating content.

Priority order for factual claims:

1. Research Design and Progress document (if maintained) — check for verified figures
2. Earlier weekly summaries — if they post-date the research design doc
3. Current week's sources — only if #1 and #2 unavailable; FLAG if uncertain

Watch for red flags: round numbers, hedged statements, conflicting numbers, numbers without context.

Step 2f: Read Previous Dashboard as Baseline

The Project Status Dashboard is a living document — read current state before writing.

1. Read the current dashboard content from the Google Doc (Tab 1)
2. This is your BASELINE — not a blank slate
3. PRESERVE previous content unless explicitly superseded, factually outdated, or explicitly removed by user
4. MODIFY existing sections with new information; ADD new items; REMOVE only when completed/superseded

Step 2g: Process Google Doc Comments

Read unresolved comments and apply corrections before synthesis.

1. Call mcp__google_workspace__read_document_comments on the project's Google Doc
2. Filter to unresolved comments only (skip any marked [RESOLVED])
3. For each unresolved comment, classify based on content:
   • Correction: contains "should be", a number, "wrong", "change to", "actually", "not X but Y", or a clear factual fix. Apply it.
   • Question/ambiguous: contains "?", "should we", "is this right", or intent is unclear. Surface in Step 7 output.
4. Apply corrections:
   • Tab 1 comments: incorporate into the dashboard baseline (modify before synthesis)
   • Tab 2 comments: apply as direct edits to the Google Doc during Step 6
5. Reply + resolve each applied correction: reply with "Applied in weekly review [DATE]. [Brief description of change]." then resolve via resolve_document_comment
6. If classification is ambiguous, default to question (surface to user rather than misapply)
7. If read_document_comments fails: note "Comments unavailable — skipped" and continue
8. If zero unresolved comments: no output, no additional calls — move on

Note: The API returns comments across all tabs. Use the comment's anchor text to determine which tab/section it belongs to.

Step 3: Synthesize

Detail and Length Requirements: Scale to meeting length:

• Brief check-ins (under 20 min): 0.5-1 page
• Standard meetings (20-60 min): 1-3 pages
• Long strategy sessions (60+ min): 2-4 pages

Guiding principle: Include more detail when in doubt. A reader who wasn't at the meeting should understand WHAT was decided, WHY, WHO said it, and the context.

Priority-Related Content Detection: Watch for "strategic priority", "operational", "critical path", "green/yellow/red", "urgent", "success factor" language. Note status changes.

FORMATTING RULES:

• No markdown tables — use bullet/sub-bullet format for all structured data
• Bold labels followed by content on same line or as sub-bullets
• Bold team member names throughout both Tab 1 and Tab 2 (track bold ranges separately; do not embed ** markdown markers in the text — formatting is applied via API)
• Paraphrase rather than quoting. Do not include direct quotes or "Key Quotes" sections.
• ASCII emoji placeholders: Use [RED], [GREEN], [YELLOW] instead of real emoji (red/green/yellow circles) in generated text. Real emoji break Google Docs index calculations. They are swapped to real emoji in Phase 5.

Visual Separators:

• Between weekly summaries: heavy double-line separator
• Within weekly summaries: medium single-line separator

Language Guidelines: Plain, accessible language. Avoid jargon. Use "challenges" not "blockers", "unclear" not "TBD". Prefer concrete descriptions over abstract terms.

Generate Tab 1 (Dashboard): Start from previous dashboard, update with this week's info. Structure:

• Project overview and status
• Strategic objectives and progress
• Operational objectives serving each strategic goal
• Team member to-do items
• Critical success factors with status (green/yellow/red)
• Funding pipeline

If skip_meeting_log: true: Skip Tab 2 generation entirely. Proceed to Step 4. Still use transcripts and WhatsApp data for Tab 1 dashboard content.

Generate Tab 2 (Weekly Log) (skip if skip_meeting_log: true): New entry prepended (reverse chronological). Structure:

• Date range header
• Weekly Activity Summary (thematic synthesis across all sources)
• Detailed Meeting Records (full summaries of each meeting)

Step 4: Save Local Copies

Skip if nosave argument.

• Weekly Summary: Save to 10_AI_Collaboration/Weekly_Reviews/weekly-review-[date].md

Step 5: Write to Google Doc (No Review Gate)

Proceed directly to Step 6 — do NOT display content in terminal for review. User reviews formatted content in the Google Doc.

Step 6: Update Google Doc

If skip_meeting_log: true: Skip the Weekly Log Prepend phase entirely. If tab1only: Only execute the Dashboard Replacement phase. Skip Weekly Log Prepend. If tab2only: Only execute the Weekly Log Prepend phase. Skip Dashboard Replacement.

Precondition: The Google Doc must have all 3 markers (=== PROJECT STATUS DASHBOARD ===, === DASHBOARD END ===, === WEEKLY SUMMARIES START ===). If DASHBOARD END is missing, STOP and report — do not fall back to the old 2-marker approach.

6-Phase Protocol:

1. Preparation — Generate text with [RED]/[GREEN]/[YELLOW] ASCII placeholders; strip ** markdown bold markers (bold is applied via API, not text)
2. Dashboard Replacement — v1.9 — call the bundled replace-dashboard.py. Handles multi-tab tabId correctly (the MCP's batch_update_doc does not):

   python3 "$WEEKLY_REVIEW_HELPERS/replace-dashboard.py" \
     --document-id "$GOOGLE_DOC_ID" --tab-id "t.0" \
     --start-marker "=== PROJECT STATUS DASHBOARD ===" \
     --end-marker "=== DASHBOARD END ===" \
     --content-file /tmp/dashboard-replacement-$(date +%s).txt \
     --json-output --apply
   

   Parse the JSON output: status is applied, noop, or error. Stop on error.
3. Verification — Confirm all 3 markers intact after dashboard write
4. Weekly Log Prepend — v1.9 — call the bundled insert-weekly-log.py:

   python3 "$WEEKLY_REVIEW_HELPERS/insert-weekly-log.py" \
     --document-id "$GOOGLE_DOC_ID" --tab-id "t.0" \
     --marker "=== WEEKLY SUMMARIES START ===" \
     --content-file /tmp/weekly-log-$(date +%s).txt \
     --json-output --apply
   

5. Emoji Swap — Use find_and_replace_doc (this MCP tool DOES correctly accept tab_id) to replace [RED]/[GREEN]/[YELLOW] with the real circle emoji
6. Formatting — Apply heading sizes and team-name bolding using API-reported indices (from inspect_doc_structure), not calculated offsets

Phase 6 is required. The document is unreadable without proper header sizes and bold formatting. After Phases 1-5, proceed to Phase 6 and apply at minimum: (a) Tier 2 baseline format reset (11pt non-bold on full range), then (b) Tier 1 section header formatting (16pt/14pt/12pt bold).

Formatting scope: FULL DOCUMENT. Even when running tab1only or tab2only, Phase 6 formats the entire document. Formatting is idempotent.

Step 7: Final Confirmation

1. Confirm: "Weekly review complete. Google Doc updated."
2. Provide link: "Check Google Doc: [link]"
3. If corrections were applied from comments: list them briefly
4. If any comments were classified as questions/ambiguous: list them with anchor text so the user can address manually
5. Note any other issues or manual fixes needed

Step 8: Archive Processed Transcripts

After the weekly review is complete:

1. Create archive folder if needed: [project]/10_AI_Collaboration/Transcripts/archive/
2. Move all processed transcript files to archive (both Granola and Zoom for same meeting)
3. Confirm: "Archived X transcript(s) to Transcripts/archive/"
4. Note any files left behind (outside date range, unparseable)

Legacy-format files are archived as-is. No automatic renaming.

Arguments

$ARGUMENTS can include:

• nosave — Don't save intermediate files
• tab1only — Only generate Tab 1 dashboard
• tab2only — Only generate Tab 2 detailed log
• days:N — Override date range to last N days
• since:YYYY-MM-DD — Override start date
• skipwhatsapp — Skip WhatsApp even if available
• skipemail — Skip email even if configured
• skiparchive — Don't archive transcripts after processing

Examples

/weekly-review                    # Full review, default date range
/weekly-review days:14            # Last 14 days
/weekly-review tab1only           # Quick dashboard update
/weekly-review skipwhatsapp       # Skip WhatsApp if it's being flaky

Customization Points

To set up this skill for your workflow:

1. Folder paths: The default 10_AI_Collaboration/Weekly_Reviews/ path reflects one project structure. Update to match your own — e.g., ~/Research/Project/reviews/ or docs/weekly-reviews/.

2. Data sources: The skill pulls from WhatsApp, meeting transcripts, and Gmail by default. Remove or add sources in the data collection steps to match your available integrations.

3. Google Doc format: The 3-marker system requires markers in your Google Doc. See Step 6 for setup.

4. Dashboard template: The default is quantitative. Set project_type: qualitative in your project config for a qualitative dashboard.

5. Transcript formats: Support for Granola, Zoom .vtt, manual markdown, RTF/PDF normalization. Add new format parsers as needed.

6. Helper scripts location: The original workflow expects normalize-transcripts.py, replace-dashboard.py, and insert-weekly-log.py. This public repository does not include them. Set WEEKLY_REVIEW_HELPERS to the directory containing your reviewed local helpers and verify all three files before use.

Error Handling

• If project config missing: "No .claude/CLAUDE.md found. Please run from a configured project folder."
• If config has malformed YAML: stop and surface the errors. Do not fall back silently.
• If WhatsApp unavailable: Continue with other sources, note in output
• If no transcripts folder: Create it, note "No transcripts folder found - created"
• If Google Doc update fails: Save Tab 1 content locally, provide manual instructions
• Flag any ambiguities: "Unclear who said X - please clarify"

Performance Logging

After completing all steps above:

echo "$(date +%Y-%m-%d),weekly-review,TOOL_CALLS,NOTES" >> ~/.claude-assistant/logs/skill-performance.csv

Replace TOOL_CALLS with your exact count. No ~ prefix. Replace NOTES with brief volume info (e.g., "3 transcripts 12 emails 2 whatsapp"). Do not skip this step.