---

name: timecamp description: Use when the user asks about time tracking, time entries, tasks, timers, or anything related to TimeCamp. Triggers on keywords like "timecamp", "time entries", "timer", "tracking", "hours", "timesheet", "tasks list", "start timer", "stop timer", "activities", "computer activities". metadata: openclaw: emoji: ⏱️ requires: env: ["TIMECAMP_API_KEY"]

TimeCamp Skill

Two tools: CLI for quick personal actions (timer, entries CRUD) and Data Pipeline for analytics/reports.

Bootstrap (clone if missing)

Before using either tool:

1. Ask user where repos should live (default: ~/utils, but any location is valid).
2. If repos are missing in that chosen location, ask for confirmation to clone.

Example flow and commands:

# Ask first:
# "I don't see TimeCamp repos locally. Clone to ~/utils, or use a different location?"

REPOS_DIR=~/utils  # replace if user picked a different path
mkdir -p "$REPOS_DIR"

if [ ! -d "$REPOS_DIR/timecamp-cli/.git" ]; then
  git clone https://github.com/timecamp-org/timecamp-cli.git "$REPOS_DIR/timecamp-cli"
fi

if [ ! -d "$REPOS_DIR/good-enough-timecamp-data-pipeline/.git" ]; then
  git clone https://github.com/timecamp-org/good-enough-timecamp-data-pipeline.git "$REPOS_DIR/good-enough-timecamp-data-pipeline"
fi

Tool 1: TimeCamp CLI (personal actions)

CLI at ~/utils/timecamp-cli, installed globally via npm link.

Tool 2: Data Pipeline (analytics & reports)

Python pipeline at ~/utils/good-enough-timecamp-data-pipeline. Use this for all analytics, reports, and bulk data fetching.

Run command

cd ~/utils/good-enough-timecamp-data-pipeline && \
uv run --with-requirements requirements.txt dlt_fetch_timecamp.py \
  --from YYYY-MM-DD --to YYYY-MM-DD \
  --datasets DATASETS \
  --format jsonl \
  --output ~/data/timecamp-data-pipeline

Available datasets

Formats: ``jsonl`

Output structure

Files land in ~/data/timecamp-data-pipeline/timecamp/*.jsonl.

Examples

cd ~/utils/good-enough-timecamp-data-pipeline && \
uv run --with-requirements requirements.txt dlt_fetch_timecamp.py \
  --from 2026-02-11 --to 2026-02-14 \
  --datasets entries,users,tasks \
  --format jsonl --output ~/data/timecamp-data-pipeline

cd ~/utils/good-enough-timecamp-data-pipeline && \
uv run --with-requirements requirements.txt dlt_fetch_timecamp.py \
  --from 2026-01-01 --to 2026-02-14 \
  --datasets computer_activities,users,application_names \
  --format jsonl --output ~/data/timecamp-data-pipeline

cd ~/utils/good-enough-timecamp-data-pipeline && \
uv run --with-requirements requirements.txt dlt_fetch_timecamp.py \
  --from 2026-01-01 --to 2026-02-14 \
  --datasets computer_activities,users,application_names,entries,tasks \
  --format jsonl --output ~/data/timecamp-data-pipeline

Analytics with DuckDB

Query the persistent data store directly.

DUCKDB=~/.duckdb/cli/latest/duckdb
DATA=~/data/timecamp-data-pipeline/timecamp

# Hours per person
$DUCKDB -c "
SELECT user_name, round(sum(TRY_CAST(duration AS DOUBLE))/3600.0, 1) as hours
FROM read_json_auto('$DATA/entries*.jsonl')
GROUP BY user_name ORDER BY hours DESC
"

# Hours per person per day
$DUCKDB -c "
SELECT user_name, date, round(sum(TRY_CAST(duration AS DOUBLE))/3600.0, 1) as hours
FROM read_json_auto('$DATA/entries*.jsonl')
GROUP BY user_name, date ORDER BY user_name, date
"

# Top applications by time (join activities with app names)
$DUCKDB -c "
SELECT COALESCE(an.full_name, an.application_name, an.app_name, 'Unknown') as app,
       round(sum(ca.time_span)/3600.0, 2) as hours
FROM read_json_auto('$DATA/computer_activities*.jsonl') ca
LEFT JOIN read_json_auto('$DATA/application_names*.jsonl') an
  ON ca.application_id = an.application_id
GROUP BY 1 ORDER BY hours DESC LIMIT 20
"

# People who logged < 30h in a given week
$DUCKDB -c "
SELECT user_name, round(sum(TRY_CAST(duration AS DOUBLE))/3600.0, 1) as hours
FROM read_json_auto('$DATA/entries*.jsonl')
WHERE date BETWEEN '2026-02-03' AND '2026-02-07'
GROUP BY user_name
HAVING sum(TRY_CAST(duration AS DOUBLE))/3600.0 < 30
ORDER BY hours
"

Pattern

1. Check existing data range with DuckDB, if data is missing, fetch it with the pipeline, if it's already there, use it
2. Query with DuckDB: $DUCKDB -c "SELECT ... FROM read_json_auto('$DATA/entries*.jsonl') ..."

Important Notes

• Duration (entries) is in seconds (3600 = 1h)
• time_span (activities) is also in seconds
• applications_cache.json in pipeline dir caches app name lookups
• For JSONL output, DuckDB glob *.jsonl catches all files for all datasets

Safety

• Confirm before adding, updating, or removing entries
• Show the command before executing modifications
• When stopping a timer, show what was running first