daily-review-v2

name	daily-review-v2
description	Claude Code activity tracking with SQLite/Turso storage — standup, weekly rollups, project reports, brag doc, friction tracking, goal tracking, heatmap, export. Data syncs to Turso for remote access. For full workday recap across all tools, use /daily-recap-v2 instead.
user_invocable	true

Daily Review v2 (SQLite-backed)

Comprehensive activity tracking and productivity intelligence built from Claude Code conversation history. Stores all data in SQLite with Turso sync for remote access.

Key Difference from /daily-review

This skill writes structured data to SQLite (~/.claude/activity.db) via the activity-db CLI instead of markdown files. The database syncs to Turso for remote access. Terminal output is still markdown for readability.

User Identity Resolution

Before starting, resolve the current user's identity dynamically:

• Full name: Run git config user.name
• Username: Run whoami
• Email: Run git config user.email

activity-db CLI

All database operations go through the CLI at ~/.claude/bin/activity-db/. Always run commands from that directory using uv run python main.py <command>.

# Write an activity (device_id auto-detected from hostname; override with --device-id)
uv run python main.py write --date YYYY-MM-DD --category <category> --source <source> --title "..." [--detail "..."] [--url "..."] [--metadata '{}'] [--tag work|personal] [--device-id <hostname>]

# Write a summary (versioned — re-runs never overwrite, they append a new version; scoped by device_id)
uv run python main.py summary --date YYYY-MM-DD --period daily|weekly|monthly|project --type review|recap|standup|brag --content "..." [--device-id <hostname>]

# Query activities
uv run python main.py query --date YYYY-MM-DD
uv run python main.py query --start YYYY-MM-DD --end YYYY-MM-DD
uv run python main.py query --category <category> --source <source>
uv run python main.py query --sql "SELECT ..."
uv run python main.py query --format table

# Query summaries (defaults to latest version per (date, period, type))
uv run python main.py query-summaries --date YYYY-MM-DD --type review
uv run python main.py query-summaries --date YYYY-MM-DD --type review --all-versions   # see history
uv run python main.py query-summaries --date YYYY-MM-DD --type review --version 2      # pin a specific version

# Stats
uv run python main.py stats

# Export as markdown
uv run python main.py export-md --date YYYY-MM-DD

# Force sync
uv run python main.py sync

Categories

project_activity, skill_usage, accomplishment, pr, jira, gdoc, confluence, reference, prompt

Sources

claude, github, jira, slack, confluence, gdocs, extracted

Tags

work (default), personal

Subcommands

Parse the user's arguments to determine which mode to run:

Command	Mode	Description
/daily-review-v2	daily	Generate today's daily log
/daily-review-v2 YYYY-MM-DD	daily	Generate log for a specific date
/daily-review-v2 YYYY-MM-DD YYYY-MM-DD	daily	Generate logs for a date range
/daily-review-v2 standup	standup	Generate copy-paste standup notes
/daily-review-v2 week	weekly	Weekly rollup for the current week
/daily-review-v2 week YYYY-MM-DD	weekly	Weekly rollup for the week containing that date
/daily-review-v2 project <name>	project	All-time report for a specific project
/daily-review-v2 compare this-week last-week	compare	Compare two time periods
/daily-review-v2 compare YYYY-MM-DD YYYY-MM-DD	compare	Compare two specific dates
/daily-review-v2 friction	friction	Accumulated friction report across all days
/daily-review-v2 goals	goals	View/manage weekly goals
/daily-review-v2 goals set <goal>	goals	Add a new weekly goal
/daily-review-v2 goals done <goal>	goals	Mark a goal as complete
/daily-review-v2 heatmap	heatmap	Time-of-day activity heatmap
/daily-review-v2 heatmap YYYY-MM	heatmap	Heatmap for a specific month
/daily-review-v2 export [date] [format]	export	Export as JSON or condensed text
/daily-review-v2 brag	brag	Regenerate the full brag doc

Filters (can be combined with any subcommand):

Flag	Description
--work	Only include work projects
--personal	Only include personal projects

---

Data Source

All modes read from ~/.claude/history.jsonl. Each line is JSON with:

• timestamp (epoch ms)
• project (full path — extract last segment as project name)
• display (the user's prompt text)
• sessionId

Use Python via Bash tool for all data extraction. Base parsing:

import json, datetime, os, re
from collections import defaultdict

def load_history():
    entries = []
    with open(os.path.expanduser('~/.claude/history.jsonl'), 'r') as f:
        for line in f:
            try:
                entry = json.loads(line.strip())
                dt = datetime.datetime.fromtimestamp(entry['timestamp'] / 1000)
                entry['_dt'] = dt
                entry['_date'] = dt.strftime('%Y-%m-%d')
                entry['_time'] = dt.strftime('%H:%M')
                entry['_hour'] = dt.hour
                entry['_dow'] = dt.strftime('%A')
                entry['_project'] = entry.get('project', 'unknown').split('/')[-1]
                entry['_project_path'] = entry.get('project', 'unknown')
                entry['_tag'] = 'personal' if '/personal/' in entry.get('project', '') else 'work'
                entry['_display'] = entry.get('display', '')
                entries.append(entry)
            except:
                continue
    return entries

Project tagging

Each project is tagged as personal or work based on its path:

• Personal: project path contains /personal/, or the path is under ~/Projects/ AND the device hostname does not match the work pattern (*.linkedin.biz or other work-laptop signature)
• Work: project path is under ~/Documents/code/ (the LinkedIn work tree), or any path on a work-tagged device, or any path mentioning a known work repo
• Mixed (vault frontmatter only): if a day has both work and personal tagged activities, set the vault file's frontmatter tag: mixed. Individual DB rows keep their own work/personal tag.

This heuristic intentionally handles the common case where personal projects (e.g. ~/Projects/aura, ~/Projects/brain-vault) live outside any personal/ directory but are unambiguously personal because the user is on their personal laptop.

Reference extraction

Extract all URLs and identifiers from prompt text:

• GitHub PRs: https://github.com/.*/pull/\d+
• Google Docs/Slides/Sheets: https://docs.google.com/...
• Jira/Atlassian: https://.*.atlassian.net/...
• Jira tickets: [A-Z]+-\d{3,6} pattern
• Never fabricate URLs — only extract what's actually present in the prompt text

---

Mode: Daily (default)

Step 0a: Sync health check

Before any DB writes, verify the activity-db Turso replica isn't wedged. The libsql embedded replica can fail silently for weeks.

cd ~/.claude/bin/activity-db && uv run python main.py stats 2>&1 | tail -5

If you see wal_insert_frame failed or a libsql::sync error: back up ~/.claude/activity.db*, delete the local replica, and let it re-replicate from Turso on next connection. Don't proceed until stats returns cleanly.

Step 0b: Cross-check GitHub for web-app blind spot

Before parsing history.jsonl, check whether the target date has GitHub artifacts (PRs, commits) authored by the user but no local Claude Code history. This catches Claude Code web-app sessions, which never write to ~/.claude/history.jsonl but still produce real commits + PRs.

Run in parallel with Step 1:

# PRs authored / touched by the user on the target date (timezone-aware — use ISO date span)
gh search prs --author=@me --created=YYYY-MM-DD..YYYY-MM-DD --json number,title,state,url,createdAt,updatedAt,closedAt,repository --limit 50

# Commits in known user repos on the target date (use --date=iso for tz preservation)
for repo in ~/Projects/*/.git; do
  cd "$(dirname "$repo")" && git log --author="<full_name>" --since=YYYY-MM-DD --until=YYYY-MM-DD+1 --format="%ad | %h | %s" --date=iso
done

Web-app signatures to look for:

• Commit author email is [e-mail]
• Branch name starts with claude/
• Multiple commits within a few minutes (web-app sessions are fast)
• Empty ~/.claude/history.jsonl for the date despite real PR / commit artifacts

If the date has GitHub activity but no local history, generate a web-app recap instead of (or in addition to) the local-history review. Use --device-id claude-code-web on activity-db writes and a -web.md suffix on the vault file. Skip Step 1's history.jsonl parsing for that date and reconstruct the timeline from commits + PR metadata.

Step 1: Parse history and extract activities

Parse history.jsonl for the target date. Extract projects, prompts, skills, references, time blocks — same logic as /daily-review.

If history.jsonl is empty for the date AND Step 0 found no GitHub activity either, skip the date entirely — no review file, no DB summary. Don't write empty placeholders.

Step 2: Write structured data to SQLite

For each project found, write an activity:

uv run python main.py write \
  --date YYYY-MM-DD \
  --category project_activity \
  --source claude \
  --title "<project_name>" \
  --detail "<key activities, newline separated>" \
  --metadata '{"prompts": N, "time_range": "HH:MM - HH:MM", "total_day_prompts": N, "active_hours": "HH:MM - HH:MM", "estimated_time": "Xh Ym"}' \
  --tag work|personal

For each skill used:

uv run python main.py write \
  --date YYYY-MM-DD \
  --category skill_usage \
  --source claude \
  --title "<skill_name>" \
  --detail "<project_name>" \
  --tag work|personal

For each reference (URL, Jira ticket):

uv run python main.py write \
  --date YYYY-MM-DD \
  --category pr|jira|gdoc|confluence|reference \
  --source extracted \
  --title "<identifier>" \
  --url "<full_url>" \
  --tag work

For each accomplishment:

uv run python main.py write \
  --date YYYY-MM-DD \
  --category accomplishment \
  --source claude \
  --title "<specific impact statement>" \
  --tag work|personal

Step 3: Generate markdown output

Generate the same markdown format as /daily-review and display it to the user in the terminal. The output should include:

Header

# Daily Activity Log — YYYY-MM-DD (DayName)

**Total prompts:** N (across M project(s))
**Active hours:** HH:MM - HH:MM
**Estimated active time:** Xh Ym
**Work:** Xh Ym (N prompts) | **Personal:** Xh Ym (N prompts)

Time Distribution

Same time block table as /daily-review with 30-min gap threshold and total row.

Project Breakdown

For each project (sorted by prompt count descending):

• Project name, tag, prompt count, time range
• Key activities — representative prompts (cleaned, truncated to 150 chars, max 15 per project)
• Skills used — any /skill commands detected
• References — all extracted URLs and Jira ticket IDs

Impact & Accomplishments

Write specific, concrete impact statements — not echoed prompts. Every accomplishment should answer "what changed and why it matters."

Rules:

• Never echo back the user's prompt text as an accomplishment. "i think we have some daily recap..." is not an accomplishment.
• Detect concrete outcomes: PR shipped/reviewed, bug fixed, feature implemented, investigation completed, decision made, doc written, tooling improved.
• Each statement should follow the pattern: What was done + why it matters or what it unblocked.
• On light days (< 10 prompts), write 1-2 real statements or omit the section entirely. A short day with no concrete output should say "Light session — exploratory/setup work, no shipped artifacts."
• On heavy days, cap at 6-8 statements — pick the ones with real impact, not every small edit.

Transcript-derived sections (requires prior enrichment)

Before rendering, run uv run python main.py enrich-from-transcripts --date YYYY-MM-DD to ensure today's sessions have transcript-derived rows in activities. Then query and render the following sections. Only include each section if there's data. If no enrichment data exists, skip all of them silently.

Files Touched

uv run python main.py query --sql "SELECT json_extract(metadata, '$.file_path') AS file_path, COUNT(*) AS edits FROM activities WHERE date='YYYY-MM-DD' AND category='file_edit' GROUP BY file_path ORDER BY edits DESC"

Render as a list grouped by project/repo, with edit counts. Strip the home directory prefix for readability. Only include source code and meaningful files — skip temp files (/tmp/*), lock files, and .pyc.

Commands Run

uv run python main.py query --sql "SELECT json_extract(metadata, '$.command') AS cmd, COUNT(*) AS n FROM activities WHERE date='YYYY-MM-DD' AND category='command' GROUP BY cmd ORDER BY n DESC LIMIT 15"

Show top distinct bash commands by frequency. Cluster related commands (e.g., "17 pytest runs", "11 flake8/ruff cycles") rather than listing each invocation.

Tool Usage Summary

uv run python main.py query --sql "SELECT json_extract(metadata, '$.tool') AS tool, COUNT(*) AS n FROM activities WHERE date='YYYY-MM-DD' AND category IN ('tool_use','file_edit','command','mcp_call','plan') GROUP BY tool ORDER BY n DESC"

Show total tool invocations, broken down by tool name.

MCP Call Summary (cross-tool work)

uv run python main.py query --sql "SELECT json_extract(metadata, '$.mcp_server') AS server, json_extract(metadata, '$.mcp_tool') AS tool, COUNT(*) AS n FROM activities WHERE date='YYYY-MM-DD' AND category='mcp_call' GROUP BY server, tool ORDER BY n DESC LIMIT 10"

Critical Review

This is the most important section. Be brutally honest — the user specifically wants highly critical, insightful feedback that helps them optimize how they work. Don't sugarcoat.

Analyze the day's data across these dimensions and call out anything worth flagging:

Time & Focus

• How fragmented was the day? Count project switches. More than 5 switches in a 3-hour block = thrashing. Name the projects and suggest which should have been batched or deferred.
• Were there long gaps between prompts that suggest getting stuck, distracted, or blocked? Quantify them.
• Did any single project get less than 15 minutes of attention? That's too short to make real progress — was it worth the context switch?
• Was there a deep work block (2+ hours on one project)? If not, flag it. Deep work is where real output happens.

Output vs. Effort

• Compare prompt count to concrete output (PRs, features shipped, bugs fixed, decisions made). High prompts with low output = spinning wheels.
• Were there repeated attempts at the same thing? (e.g., 17 pytest runs, multiple "can you fix" prompts on the same issue). Identify the root cause — was the approach wrong, was there a missing skill, or was this unavoidable iteration?
• Did the user paste content that Claude could have fetched itself? (e.g., pasting error output instead of running the command, pasting docs instead of using MCP). Each paste is manual work that might be automatable.

Decision Quality

• Were there signs of indecision? ("should we...", "what do you think...", "i'm not sure if..."). Indecision isn't bad, but if it recurred on the same topic across multiple prompts, the user may need to timebox decisions.
• Were there scope creep moments? (started on X, ended up doing Y, Z). Flag them.
• Were there "going in circles" patterns? (asking the same question rephrased, or reverting changes).

Automation & Process

• What manual work was repeated that could be automated? (e.g., running the same sequence of commands, manually checking the same dashboards).
• Are there missing skills or tools that would have saved time? Be specific — "a pre-commit hook for X" or "a script that does Y" not "consider automation."
• Was the user doing work that could be delegated to a CI/CD pipeline, a cron job, or a background process?

Compared to Recent Days (if data available)

• Query the last 7 days from the DB: SELECT date, COUNT(*) FROM activities WHERE date >= date('YYYY-MM-DD', '-7 days') GROUP BY date
• Is today's output higher or lower than the trend? Is the project mix shifting?
• Are the same friction points recurring across days?

Format as a numbered list of specific, actionable observations. Each item should have:

1. The observation — what you noticed, with data
2. Why it matters — the cost of not addressing it
3. What to do about it — a concrete next step

Aim for 3-6 items. Quality over quantity. Skip this section entirely only if the day was under 5 prompts.

Step 4: Store the generated summary

uv run python main.py summary \
  --date YYYY-MM-DD \
  --period daily \
  --type review \
  --content "<full markdown output>"

Step 5: Write to Obsidian vault

If the brain-vault repo exists at ~/Projects/brain-vault/, save the generated markdown to Activity/.

File naming depends on device:

• Detect device type by checking hostname:
  • If hostname contains .linkedin.biz or matches the work laptop pattern → work device
  • Otherwise → personal device
• Work device file names (default, no suffix):
  • Activity/YYYY-MM-DD.md
• Personal device file names (suffixed):
  • Activity/YYYY-MM-DD-personal.md

Frontmatter — always include:

---
type: activity
date: YYYY-MM-DD
period: daily
device: work|personal
tags:
  - activity
  - daily-log
---

Do NOT overwrite — if the target file already exists, skip this step silently. The vault file is a snapshot; regeneration updates the DB summary (versioned), not the vault file.

Wikilinks — wrap all project names in [[wikilinks]] (e.g., [[brain-vault]], [[neo-workflow]]) so Obsidian graph view and backlinks connect activity logs to project notes.

Step 6: Enrich Project Notes

For each project that had 5+ prompts in the day, update its project note in ~/Projects/brain-vault/Projects/.

If the project note doesn't exist, create it:

---
type: project
status: active
category: personal|work
last_seen: YYYY-MM-DD
---

## Overview

<!-- auto:daily-context:start -->
<!-- auto:daily-context:end -->

## Notes

If the project note already exists, update the <!-- auto:daily-context:start/end --> block. If that block doesn't exist, add it after ## Overview (or after ## Auto-generated context if that section exists). Never touch content outside the auto block.

The auto block should contain a running log of what was done on this project, appended to (not replaced). Format:

<!-- auto:daily-context:start -->

### YYYY-MM-DD
- **What:** 1-2 sentence summary of what was done
- **How:** Key technical details, tools used, commands, architecture decisions
- **Problems solved:** Non-obvious solutions worth remembering (e.g., "Tika race condition on cold boot — services need staggered startup")
- **Status:** Current state (deployed, PR open, WIP, blocked on X)
- **Links:** [[related-project]], PR URLs, doc URLs

### YYYY-MM-DD (previous entry preserved)
...

<!-- auto:daily-context:end -->

Also update frontmatter:

• Set last_seen to today's date
• If status: stub and there's now real content, promote to status: active

This is critical for the vault's purpose as an external brain — project notes should answer "what is this project and what have I done with it?" not just "how many times was it mentioned."

Step 7: Extract Ideas & Backlog

Scan the day's prompts for signals of ideas, future plans, or things to explore:

• "i'd like to...", "maybe we should...", "another feature i'm thinking of..."
• "we should add...", "it would be cool if...", "i want to explore..."
• "can we automate...", "is there a way to..."
• Questions about tools/services not yet used

Append any found items to ~/Projects/brain-vault/Backlog/ideas.md. If the file doesn't exist, create it:

---
type: backlog
---

# Ideas & Backlog

Pending ideas extracted from daily activity. Review periodically — promote to project notes or archive.

Each idea entry:

## YYYY-MM-DD — <short title>
- **From:** [[project-name]] session
- **Idea:** What the user described wanting
- **Context:** Why it came up
- **Status:** open

Do NOT duplicate — check if a similar idea already exists before adding. Append only genuinely new ideas.

Step 8: Update brag doc

Append new accomplishments to ~/.claude/daily-logs/BRAG-DOC.md (same as /daily-review).

Immutability

Before writing, check if a summary already exists for this date:

uv run python main.py query-summaries --date YYYY-MM-DD --type review

If it exists, DO NOT overwrite. Tell the user and ask if they want to regenerate.

---

Mode: Standup

Same as /daily-review standup. Pull yesterday's data from the DB:

uv run python main.py query --date YYYY-MM-DD --format json

Generate copy-paste standup message to screen (not stored).

---

Mode: Weekly Rollup

Same logic as /daily-review week. Query the full week's data:

uv run python main.py query --start YYYY-MM-DD --end YYYY-MM-DD

Store as:

uv run python main.py summary --date YYYY-MM-DD --period weekly --type review --content "..."

---

Mode: Project Focus Report

Query all activities for a specific project:

uv run python main.py query --sql "SELECT * FROM activities WHERE title = '<project>' OR detail LIKE '%<project>%' ORDER BY date"

---

Mode: Compare

Query both periods and compare metrics side-by-side. Use SQL aggregations:

uv run python main.py query --start <period_a_start> --end <period_a_end>
uv run python main.py query --start <period_b_start> --end <period_b_end>

---

Mode: Friction Tracker

Same friction analysis as /daily-review. Scan all history for friction signals, write results:

uv run python main.py summary --date YYYY-MM-DD --period monthly --type review --content "..."

---

Mode: Goals

Use the goals table in SQLite instead of goals.json:

# View goals
uv run python main.py query --sql "SELECT * FROM goals WHERE week = '2026-W14'"

# Add goal (use Python to insert directly)
# Mark done (update status and completed date)

---

Mode: Heatmap

Same as /daily-review heatmap. Parse history.jsonl for time data and generate ASCII heatmap to screen.

---

Mode: Export

# JSON export
uv run python main.py query --date YYYY-MM-DD --format json

# Markdown export
uv run python main.py export-md --date YYYY-MM-DD

---

Mode: Brag Doc Regeneration

Query all accomplishments:

uv run python main.py query --category accomplishment

Regenerate ~/.claude/daily-logs/BRAG-DOC.md from structured data.

---

Important Notes

• activity-db is the single write path — all data goes through the CLI
• Terminal output is still markdown — the user sees the same format as /daily-review
• SQLite is the source of truth — not markdown files
• Turso syncs automatically — every write syncs to remote
• Real references only — never fabricate URLs or Jira IDs
• Python for data parsing — use Python via Bash tool for history.jsonl parsing
• Dates: Determine today's date from the system. Use the user's local timezone.
• Run activity-db from its directory: Always cd ~/.claude/bin/activity-db && uv run python main.py <command>
• Summaries are versioned, not overwritten — re-running summary for the same (date, period, type) appends a new version (v2, v3, …) instead of replacing the prior one. The query-summaries command defaults to the latest version; pass --all-versions to see history or --version N to pin one. Prior versions are preserved so regenerations never destroy earlier content.