staging-golden-tier

name	staging-golden-tier
description	Process raw Co-Design Program transcripts from the Google Drive Staging_Transcripts folder into Golden Tier format. Use when asked to clean transcripts, promote staging files, or update the Golden Tier collection.
summary	Raw transcript → Golden Tier pipeline: download, clean, format, verify.
metadata	{"version":"1.0.0","openclaw":{"category":"productivity","requires":{"bins":["gws","python3","pandoc"]}}}

staging-golden-tier

Process raw Co-Design Program interview transcripts from Staging_Transcripts on Google Drive into the curated Golden Tier collection.

Prerequisites

• gws CLI authenticated as [e-mail] with Drive scope
• python3 available
• pandoc available (for .docx conversion)
• Golden Tier location: ~/Documents/bella_assist/gws_copy/shared_drives/BellaAssist_-_Product/Co-Design_Program/Golden_Tier/
• Staging Drive folder ID: 1W169BsPkexomhf6Qeyz-6dFOkTn-Z2UW

Reference files

• references/golden-tier-spec.md — format spec (frontmatter schema, speaker format, naming convention, cleaning checklist)
• references/known-speakers.md — speaker name-to-role mapping, Gemini display names, common transcription errors

Read both references before starting any processing.

Workflow

Phase 1 — Inventory

List the staging folder and cross-reference against the manifest.

# List staging files
gws drive files list --params '{"q": "\"1W169BsPkexomhf6Qeyz-6dFOkTn-Z2UW\" in parents and trashed=false", "includeItemsFromAllDrives": true, "supportsAllDrives": true, "fields": "files(id,name,modifiedTime,mimeType,size)", "pageSize": 100}'

For each file:

1. Parse the filename to extract person, session type, and stage
2. Check manifest.yaml for an existing entry matching that person + stage
3. Flag duplicates for comparison, mark new sessions for processing

Present the inventory table to the user for approval before proceeding.

Phase 2 — Download & Convert

For each approved file:

# Download .docx files
gws drive files get --params '{"fileId": "<ID>", "alt": "media", "supportsAllDrives": true}' --output "/tmp/staging/<filename>"

# Convert .docx to .md
pandoc -f docx -t markdown --wrap=none "/tmp/staging/<filename>.docx" -o "/tmp/staging/<filename>.md"

For files already in .md format, download directly — no conversion needed.

Phase 3 — Parse & Extract

Identify the source type and extract the transcript content.

Gemini Notes exports (most common in staging):

• The .docx contains both AI summary notes AND an embedded transcript
• The transcript section starts after a line containing Transcript (often preceded by a clipboard emoji)
• Everything before that line is the notes section (Summary, Details, Suggested next steps)
• Extract both sections: transcript → _transcript.md, notes → _notes.md

Read AI exports (.txt files):

• Pure transcript, no notes section
• Format: TIMESTAMP - Speaker Name followed by dialogue on next lines

Pre-cleaned files (already .md with some processing done):

• Validate existing structure against the spec
• Apply remaining cleaning steps as needed

Phase 4 — Clean

Run the deterministic cleanup script first:

python3 scripts/clean_transcript.py "/tmp/staging/<file>.md" -o "/tmp/staging/<file>_cleaned.md"

This handles: pandoc artifacts, Unicode normalisation, empty spacer lines, Gemini disclaimer removal, timestamp anchor cleanup.

Then apply AI judgment (Claude does this directly):

1. Read references/known-speakers.md to map display names to canonical names and roles
2. Standardise speaker labels: map Gemini display names (e.g., Bails18 Wills → keep as-is for Gemini format; resolve Unidentified Speaker where possible from context)
3. For Read AI sources: convert to **Speaker Name** [Role] (H:MM:SS): format
4. For Gemini sources: preserve Speaker Name: text format with standalone timestamp blocks
5. Merge consecutive same-speaker turns only when clearly a single thought interrupted by the transcription
6. Correct transcription errors using the known errors table in known-speakers.md
7. Verify session metadata: confirm the date, participant name, and session type from transcript content (not just filename)

Phase 5 — Format & Place

Generate the YAML frontmatter:

python3 scripts/generate_frontmatter.py \
  --participant "Name" \
  --role "Support Coordinator" \
  --stage 4 \
  --session-type "MVP Testing" \
  --date "2026-04-09" \
  --source "Gemini embedded transcript" \
  --source-file "original.docx" \
  --content-type transcript \
  --has-companion-notes true \
  "/tmp/staging/<file>_cleaned.md"

This prepends the frontmatter and document header, calculates word count, and writes the final file.

Place the output file:

• Transcript: Golden_Tier/{SC_or_Participants}/{name}/{session_id}_{date}_transcript.md
• Notes: Golden_Tier/{SC_or_Participants}/{name}/{session_id}_{date}_notes.md

Phase 6 — Lock & Update Index

# Lock processed files
chmod 444 "Golden_Tier/{path}_transcript.md"
chmod 444 "Golden_Tier/{path}_notes.md"

# Update manifest
python3 scripts/update_manifest.py \
  --manifest "Golden_Tier/manifest.yaml" \
  --transcript "Golden_Tier/{path}_transcript.md" \
  --notes "Golden_Tier/{path}_notes.md"

Also update Golden_Tier/README.md:

• Update the folder structure section if new people added
• Update session counts in person entries
• Update gap register (remove filled gaps, note remaining ones)
• Update totals in the "What is this?" section

IMPORTANT: Every file write to the Golden Tier is a curated corpus modification. Present the diff for user review.

Phase 7 — Final Verification

Re-read each produced Golden Tier file and validate:

Check	Method	Pass criteria
YAML frontmatter	Parse YAML block	All required fields present and correctly typed
Word count	Count body words, compare to frontmatter	Within +/- 5 of word_count field
Speaker format (Read AI)	Regex: ^\*\*[^*]+\*\* \[[^\]]+\] \(\d+:\d{2}:\d{2}\):	Every speaker turn matches
Speaker format (Gemini)	Regex: ^[A-Z][^:]+: after a timestamp block	Consistent speaker labels
No pandoc artifacts	Search for {.underline}, [~~, trailing \	Zero matches
No Gemini noise	Search for "You should review", "Suggested next steps"	Zero matches in transcript files
No unmerged turns	Check for consecutive identical speaker labels	None found (or justified)
File permissions	stat -f %Lp or ls -la	444 (read-only)
File location	Path check	Correct subfolder and naming convention
Manifest consistency	Parse manifest, check for duplicates, verify totals	Clean

Present a verification summary table. Any failures get flagged for manual review.

Safety

• All Google Drive operations follow the GWS write guard: read operations run freely, write operations require explicit user approval
• Golden Tier files are set read-only after processing — to modify, the user must explicitly unlock
• The skill is idempotent: re-running on an already-processed file detects it via the manifest and skips
• Original staging files are NOT deleted from Drive — that requires a separate approval