taskclf 0.4.2

Local-first task classifier that infers your work type from computer activity signals

taskclf — Task Type Classifier from Local Activity Signals

Train and run a personal task-type classifier (e.g. coding / writing / meetings) using privacy-preserving computer activity signals such as foreground app/window metadata and aggregated input statistics (counts/rates only).

This project is intentionally scoped as a personalized classifier (single-user first). The architecture keeps:

• Collectors (platform/tool dependent) isolated behind adapters
• Features as a versioned, validated contract
• Models as bundled artifacts with schema checks
• Inference as a small, stable loop that emits task segments and daily summaries

Goals

• Fast iteration: first useful model in < 1 week of data
• Privacy: no raw keystrokes, no raw window titles persisted
• Stability: feature schema versioning + schema hash gates
• Extensibility: add new collectors and models without breaking consumers

Non-Goals

• Universal (multi-user) generalization out of the box
• Storing or analyzing raw typed content
• "Perfect" labeling UI (start minimal, iterate later)

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Labels (v1)

Eight core labels defined in schema/labels_v1.json:

ID	Label	Description
0	Build	Writing or implementing structured content in editor/terminal
1	Debug	Investigating issues, terminal-heavy troubleshooting
2	Review	Reviewing technical material or diffs with light edits
3	Write	Writing structured non-code content
4	ReadResearch	Consuming information with minimal production
5	Communicate	Asynchronous coordination (chat/email)
6	Meet	Synchronous meetings or calls
7	BreakIdle	Idle or break period

Labels are stored as time spans (not per-keystroke events). Users can remap core labels to personal categories via a taxonomy config (see configs/user_taxonomy_example.yaml).

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Data Flow Overview

Structures (pipelines)

• ETL pipeline reads raw → produces features parquet
• Training pipeline reads features + labels → produces model
• Inference pipeline reads new events → emits predictions + segments

Batch (repeatable)

1. Ingest: pull ActivityWatch export → data/raw/aw/
2. Feature build: events → per-minute features → data/processed/features_v1/
3. Label import: label spans → data/processed/labels_v1/
4. Build dataset: join features + labels, split by time → training arrays
5. Train: fit model → models/<run_id>/
6. Evaluate: metrics, acceptance checks, calibration
7. Report: daily summaries → artifacts/

Online (real-time)

Every N seconds:

• read the last minute(s) of events
• compute the latest feature bucket
• predict + smooth (with optional calibration and taxonomy mapping)
• append predictions → artifacts/

At end-of-day:

• produce report

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Privacy & Safety

This repo enforces the following:

• No raw keystrokes are stored (only aggregate counts/rates).
• No raw window titles are stored by default.
  • Titles are hashed or locally tokenized; you can keep a local mapping if you choose.
• Dataset artifacts stay local-first.

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Quick Start

Requirements

• Python >= 3.14
• uv installed

Install

uv tool install taskclf

For development (from source):

uv sync
uv run taskclf --help

Ingest (ActivityWatch)

uv run taskclf ingest aw --input /path/to/activitywatch-export.json

This parses an ActivityWatch JSON export, normalizes app names to reverse-domain identifiers, hashes window titles (never storing raw text), and writes privacy-safe events to data/raw/aw/<YYYY-MM-DD>/events.parquet partitioned by date.

Options:

• --out-dir — output directory (default: data/raw/aw)
• --title-salt — salt for hashing window titles (default: taskclf-default-salt)

Build features

uv run taskclf features build --date 2026-02-16

Import labels

uv run taskclf labels import --file labels.csv

Or add individual label blocks:

uv run taskclf labels add-block \
  --start 2026-02-16T09:00:00 --end 2026-02-16T10:00:00 --label Build

Or label what you're doing right now (no timestamps needed):

uv run taskclf labels label-now --minutes 10 --label Build

This queries ActivityWatch for a live summary of apps used in the last N minutes and creates the label span automatically.

Export labels to CSV

uv run taskclf labels export --out my_labels.csv

Train

uv run taskclf train lgbm --from 2026-02-01 --to 2026-02-16

Run batch inference

uv run taskclf infer batch --model-dir models/<run_id> --from 2026-02-01 --to 2026-02-16

Run online inference

uv run taskclf infer online --model-dir models/<run_id>

Starts a polling loop that queries a running ActivityWatch server, builds feature rows from live window events, predicts task types using a trained model, smooths predictions, and writes running outputs to artifacts/. Press Ctrl+C to stop; a final daily report is generated on shutdown.

Options:

• --poll-seconds — seconds between polls (default: 60)
• --aw-host — ActivityWatch server URL (default: http://localhost:5600)
• --smooth-window — rolling majority window size (default: 3)
• --title-salt — salt for hashing window titles (default: taskclf-default-salt)
• --out-dir — output directory (default: artifacts)
• --label-queue / --no-label-queue — auto-enqueue low-confidence predictions for manual labeling
• --label-confidence — confidence threshold for auto-enqueue (default: 0.55)

Run baseline (no model needed)

uv run taskclf infer baseline --from 2026-02-01 --to 2026-02-16

Rule-based classifier useful for day-1 bootstrapping before you have a trained model.

Produce report

uv run taskclf report daily --segments-file artifacts/segments.json

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CLI Reference

All commands: uv run taskclf --help

Group	Commands	Purpose
ingest	aw	Import ActivityWatch exports
features	build	Build per-minute feature rows
labels	import, add-block, label-now, show-queue, project	Manage label spans and labeling queue
train	build-dataset, lgbm, evaluate, tune-reject, calibrate, retrain, check-retrain	Training, evaluation, and retraining pipeline
taxonomy	validate, show, init	User-defined label groupings
infer	batch, online, baseline, compare	Prediction (ML, rule-based, comparison)
report	daily	Daily summaries (JSON/CSV/Parquet)
monitor	drift-check, telemetry, show	Feature drift and telemetry tracking
(top-level)	tray	System tray labeling app with activity transition detection
(top-level)	ui	Web UI for labeling, queue, and live prediction streaming

Full CLI docs: docs/api/cli/main.md

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Repo Layout

• src/taskclf/ — application code (adapters, core, features, labels, train, infer, report, ui)
• schema/ — versioned JSON schemas for features and labels
• configs/ — configuration files (model params, retrain policy, taxonomy examples)
• docs/ — API reference and guides (served via make docs-serve)
• data/ — raw and processed datasets (local, gitignored)
• models/ — trained model bundles (one folder per run)
• artifacts/ — predictions, segments, reports, evaluation outputs
• tests/ — test suite

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Model Artifact Contract

Every saved model bundle (models/<run_id>/) contains:

• the model file
• metadata.json: feature schema version + hash, label set, training date range, params, dataset hash
• metrics.json: macro/weighted F1, per-class metrics
• confusion_matrix.csv
• categorical encoders (if applicable)

Inference refuses to run if the schema hash mismatches the model bundle.

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Development

Common tasks are in the Makefile:

make lint        # ruff check .
make test        # pytest
make typecheck   # mypy src
make docs-serve  # local preview at http://127.0.0.1:8000
make docs-build  # static site in site/

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License

TBD (local-first personal project by default).