skills/daily-review-telegram-inbox/SKILL.md
Export Telegram saved messages via tdl, apply GTD + S3 clarify, update project docs with actionable items. Part of the daily review workflow.
npx skillsauth add razbakov/skills skills/daily-review-telegram-inboxInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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⚠️ Migration in progress (razbakov/ikigai#73): Control Center has moved from Notion to GitHub Issues + Project v2. When this skill references creating/reading/updating Notion cards or pages, translate to GitHub equivalents:
- Create card →
gh issue create --repo <project-repo> --title "<title>" --label agent:<name> --body "<S3 body>"thengh project item-add 5 --owner razbakov --url <issue-url>- Read cards →
gh issue list --repo <repo> --label agent:<name> --state open(or across repos viagh search issues "org:razbakov label:agent:<name> state:open")- Update card status → move on board:
gh project item-editwith the Status field, or close viagh issue close- Board columns: Inbox → To do → In progress → To review → Done
- Do not call any
notion-*MCP tools — the Notion MCP is disabled.
Telegram Saved Messages are the GTD inbox — voice notes, ideas, links, reminders captured throughout the day. This step collects, saves raw, and processes them into actionable items. Action items and clusters get deeper analysis using Sociocracy 3.0's Tension → Driver → Requirement pattern.
Invoked as part of /daily-review or independently via /daily-review-telegram-inbox.
~/Projects/ikigaisessions/~/Projects/tdl CLI installed and authenticatedExport since last daily review:
tdl chat export --all --with-content -T time -i $(date -v-1d +%s),$(date +%s) -o /tmp/tdl-saved.json
After export, immediately mark all exported messages with 👀 in Telegram. This tells the user which messages have been picked up for processing.
# Extract timestamps from exported messages
TIMESTAMPS=$(cat /tmp/tdl-saved.json | python3 -c "
import json, sys, datetime
data = json.load(sys.stdin)
msgs = data.get('messages', [])
ts_list = []
for m in msgs:
ts = m.get('date', 0)
dt = datetime.datetime.fromtimestamp(ts).strftime('%Y-%m-%d %H:%M')
ts_list.append(dt)
print(','.join(ts_list))
")
# Mark as seen in Telegram
cd ~/.config/telegram && uvx --python python3 --from telethon python3 react.py --mark-seen "$TIMESTAMPS"
cat /tmp/tdl-saved.json | python3 -c "
import json, sys, datetime
data = json.load(sys.stdin)
msgs = data.get('messages', [])
for m in msgs:
ts = m.get('date', 0)
dt = datetime.datetime.fromtimestamp(ts).strftime('%Y-%m-%d %H:%M')
text = m.get('text', '(no text)')
print(f'--- {dt} ---')
print(text)
print()
"
Important: Never mark voice notes or reflections as "Reference" — they are content ideas. Classify them as Action for razbakov.com or smm-manager (blog post, thread, social media post). Every thought Alex captures is potential content.
S3 Deep Analysis (Action items and Clusters only):
For each item classified as Action, apply S3 Tension → Driver → Requirement:
For Reference, Trash, Someday, Content Ideas that are NOT part of a cluster: skip S3 analysis. GTD classification is sufficient.
After GTD classification, scan all items for shared tensions:
| # | Time | Preview | GTD | Project | Task | Driver (1-line) | Cluster |
|---|------|---------|-----|---------|------|-----------------|---------|
| 1 | 08:05 | WeDance logo... | Action | WeDance | Integrate logo | No visual identity for festival MVP | — |
| 2 | 08:06 | OpenFang... | Reference | ikigai | — | — | Agent Infra |
| 3 | 08:07 | inference.sh... | Reference | ikigai | — | — | Agent Infra |
| 4 | 13:02 | Shape Up... | Action | WeDance | Read & extract | Ad-hoc planning risks MVP | — |
Note: The Driver (1-line) column is blank for non-Action items. The Cluster column is blank for non-clustered items. Omit both columns entirely when there are no Action items or clusters in the batch.
For each actionable item:
README.md (or TODO.md, docs/backlog.yaml if they exist)Save to inbox/YYYY-MM-DD-HH-MM.md (timestamp of the first message in the batch):
## Summary — counts with S3 note: "N actions (M with S3 analysis), N references, N clusters detected"## Messages — per-message blocks with GTD classification
#### S3 Analysis sub-block:
## Clusters (when detected) — grouped S3 analysis:
The daily review file (sessions/YYYY-MM-DD-daily-review.md) should only contain a link to the inbox file, not the full content.
For each processed message that has an external URL (articles, tools, repos, threads) and is classified as Reference, Action (read/evaluate), or Content Idea with a link — create a card on the Reading Inbox Notion board.
Use the Notion MCP create-pages tool with:
40473b3d-377c-4ddf-b7c4-9407bfc65f72Name: Short descriptive titleStatus: "To Read" (for items marked Worth Reading: Yes) or "Unread" (for Maybe/Skim)Source: "Telegram"Type: One of Tool, Article, Thread, Video, Reference, RepoSummary: 1-2 sentence summaryWhy It Matters: Driver summary — 1 sentence connecting item to mission/OKRs (for Action items, sourced from S3 Driver analysis; for Reference items, a brief relevance note)Project: JSON array of project names, e.g. ["ikigai", "WeDance"]Worth Reading: Yes / Skim / Maybe / NouserDefined:URL: The source URLdate:Date Added:start: ISO date (YYYY-MM-DD)date:Date Added:is_datetime: 0For Action items, also include page content with S3 analysis:
## S3 Analysis
### Tension
[1-2 sentences — what dissonance prompted this]
### Driver
<table header-row="true">
<tr>
<td>Conditions</td>
<td>Effect</td>
<td>Relevance</td>
</tr>
<tr>
<td>[observable facts]</td>
<td>[consequences]</td>
<td>[why it matters for mission/OKRs]</td>
</tr>
</table>
### Requirement
> [who] needs [conditions] so that [outcomes]
### Response Options
- [ ] [concrete action A]
- [ ] [alternative approach B]
- [ ] [defer/skip option]
For clustered items: Each card in the cluster gets the same cluster-level S3 content, plus a callout at the top:
<callout icon="🔗" color="purple_bg">
**Cluster: [Name]** — related cards: [list sibling item names]
</callout>
For Reference items (non-clustered): Properties only, no page content. This is the current behavior.
Skip items that: have no URL, are purely task-oriented (no reading needed), are voice notes, or are already done/actioned.
After saving the inbox file, upgrade all 👀 reactions to GTD-specific emojis:
cd ~/.config/telegram && uvx --python python3 --from telethon python3 react.py --file ~/Projects/ikigai/inbox/YYYY-MM-DD-HH-MM.md
This replaces the 👀 "seen" reaction with:
When work on an inbox item produces a PR, edit the original Telegram message to append the link:
# Single PR
cd ~/.config/telegram && uvx --python python3 --from telethon python3 react.py --add-pr '2026-03-21 08:05|https://github.com/razbakov/festival-schedule/pull/23'
# Bulk from JSON
cat > /tmp/prs.json << 'EOF'
[
{"ts": "2026-03-21 08:05", "pr": "https://github.com/.../pull/23", "title": "Add hero image"}
]
EOF
cd ~/.config/telegram && uvx --python python3 --from telethon python3 react.py --add-pr /tmp/prs.json
The original message becomes:
We dance. We need to integrate a logo.
→ PR: Add hero image
https://github.com/razbakov/festival-schedule/pull/23
Information flow: Telegram -> 👀 (seen) -> GTD classify -> S3 analysis (Action/Cluster only) -> inbox/ file (raw + processed + S3) -> Project README (source of truth) -> Notion Reading Inbox (properties + S3 page content for Actions) -> GTD emoji in Telegram -> PR link in Telegram -> PROJECTS.md (cache) -> Daily Review (link only).
The GTD + S3 summary table, S3 analysis blocks for Action items/clusters, and a list of project docs that were updated.
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