Task Forest

Task Forest

Purpose

Maintain the current workspace's task structure, proposals, history, and progress. Produce one standalone HTML deliverable that a first-time reader can understand without opening another task list or internal file.

Use the user's language for task content, proposals, reports, and HTML. Default to Chinese when unknown.

Portability

This skill is agent-agnostic. Install the whole task-forest directory in any host-supported skill location, then resolve scripts, references, and assets from the directory containing this SKILL.md. Do not assume a specific agent name, skill root, home-directory layout, shell, path separator, or operating system.

Use an available Python 3 launcher on the host (python3, python, or py -3). The scripts use the Python standard library and support macOS, Linux, and Windows. To label task history with the calling agent, set COMPASS_AGENT_NAME or AGENT_NAME, or pass --actor; otherwise the neutral value agent is used.

Core Rules

1. Read and write task-forest data only through scripts/task_forest.py; never hand-edit .agent-workbench/task-forest/ canonical files.
2. Use one primary child_of parent per node, contributes_to for secondary ownership, and depends_on for prerequisites.
3. Save graph changes as a proposal and wait for user confirmation before proposal-apply --yes.
4. Keep low-confidence inference in a question or proposal. Record material execution drift as a deviation.
5. Write visible task titles and purposes in plain language. A reader must understand what the task delivers, why it exists, and what has been completed without knowing internal codes such as P04 or reading another file.
6. Export one HTML surface: exports/task-forest.html. It shows done, in_progress, and their necessary child_of ancestors, with history playback. Do not expose internal discussions, evidence, queues, filesystem paths, sessions, or proposal content in the HTML.
7. Keep HTML interactions read-only. Formal changes always return through the proposal workflow.
8. Keep task data and discovery metadata repo-local by default. Cross-workspace discovery is optional: enable it only after the user explicitly opts in by setting TASK_FOREST_ENABLE_GLOBAL_REGISTRY=1. This writes lightweight workspace paths and health summaries to AGENT_WORKBENCH_DB, or to ~/.agent-workbench/agent-workbench.sqlite3 when that variable is unset; it never stores task content.

Main Workflow

When initializing, updating, or closing a session:

1. Run init.
2. Read list --json and todo --json.
3. Identify the global goal served by the session and the task structure that must remain visible.
4. When the workspace has an authoritative task list, preserve its meaningful goal -> phase -> module -> concrete task hierarchy and sibling order for every done or in_progress task. Include necessary ancestors, omit wholly unstarted branches, and attach extra fixes under the feature they improve. Never collapse several phases into one node or rely on edge creation order. If one sibling needs display_order, set a unique numeric value for the whole sibling group; partial, duplicate, or invalid values must fail validation.
5. Make every visible node independently understandable. Use a clear title plus summary or purpose; add outcomes or acceptance criteria when they clarify delivery. Treat internal codes as secondary labels, not as the task name.
6. Show and save a proposal. Do not apply it before confirmation.
7. After confirmation, run proposal-apply --yes, validate, and export.
8. Return the proposal path and the single HTML path.

Use $task-clarifier when user intent or the target global goal is genuinely unclear.

Commands

Resolve <skill-dir> from this file and use an available Python 3 executable. The examples use python3; substitute the host's available launcher when needed.

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python3 <skill-dir>/scripts/task_forest.py init
python3 <skill-dir>/scripts/task_forest.py list --json
python3 <skill-dir>/scripts/task_forest.py todo --json
python3 <skill-dir>/scripts/task_forest.py proposal-save --proposal-file /path/to/proposal.json
python3 <skill-dir>/scripts/task_forest.py proposal-apply <proposal-id> --yes
python3 <skill-dir>/scripts/task_forest.py validate
python3 <skill-dir>/scripts/task_forest.py export

The default workspace is the current directory. Use --workspace only when another workspace is explicit. Use --root only when the caller explicitly selected a non-default task-forest root.

Global registry integration is off by default. TASK_FOREST_DISABLE_GLOBAL_REGISTRY=1 remains an explicit override when a host sets the enable flag globally.

Outputs

The user-facing artifacts are:

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proposals/<proposal_id>.json
exports/task-forest.html

The exporter also maintains three internal compatibility files for gap-router and local-agent-control-room:

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exports/task-forest.graph.json
exports/task-forest.todos.json
exports/task-forest.timeline.json

Do not present those JSON files as delivery artifacts unless the user explicitly asks for machine-readable data.

References and Validation

• Read references/schema.md for node fields, proposal actions, and canonical invariants.
• Read references/goal-alignment.md only when judging global-goal fit or competing candidate plans.
• Read references/node-types.md only when node classification is unclear.
• Read references/concurrency.md before resolving stale proposals or concurrent writes.
• Read references/html-visualization-contract.md when changing or validating the HTML.
• Read references/integration-contract.md only when changing JSON or registry compatibility.

For HTML or exporter changes, run:

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python3 <skill-dir>/scripts/validate_task_forest_export.py --skill-dir <skill-dir>

The HTML remains a derived, read-only view. Canonical task data and proposal history stay repo-local.