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Daily Task Manager

CommunityPopular
garrytan
daily-task-manager

Task lifecycle management with stable task IDs. Add, complete, defer, remove, and review tasks with deterministic action routing and fail-closed ambiguity handling. Maintains a running task list as a brain page.

Overview

Publishergarrytan
Repositorygbrain
Skill namedaily-task-manager
Stars
30.1K
Forks
4.5K
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by garrytan on GitHub. Read the source before you install it.

Installation

Install the Daily Task Manager AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/garrytan/gbrain.git /tmp/gbrain
mkdir -p .claude/skills
cp -r /tmp/gbrain/plugin-variants/gbrain-daily/skills/daily-task-manager .claude/skills/daily-task-manager
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Daily Task Manager in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Daily Task Manager on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Daily Task Manager is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Daily Task Manager

Contract

This skill guarantees:

  • Tasks stored as a brain page (ops/tasks.md) with structured format and a stable id per task
  • Task lifecycle: add → in-progress → complete | defer | remove
  • Priority levels: P0 (urgent), P1 (today), P2 (this week), P3 (backlog)
  • Completed tasks archived with completion date; deferred tasks carry a target date + reason
  • Mutations never drop unrelated tasks or unknown sections
  • Every action returns the structured result below (Returns)

Returns

After every action, report a structured result so callers (including sub-agents) can chain reliably:

{action, task_id, status: ok|not_found|ambiguous|needs_confirmation, priority, date, page: "ops/tasks.md", saved: true|false}

For review, return the grouped active-task list instead of a single task_id. When invoked with the trigger "task list json", return a JSON array of task objects {id, description, priority, due, status} instead of markdown.

Tool Interface

Use ONLY the declared tools. get_page("ops/tasks.md") to read, put_page("ops/tasks.md", …) to write, add_timeline_entry for the audit trail, search for cross-referencing. Do not shell out to gbrain CLI verbs from this skill; the tools are the interface. (When the user runs this manually outside an agent, the CLI equivalents are gbrain get ops/tasks / gbrain put ops/tasks — equivalents only, not the skill's interface.)

Action Routing

Map user intent deterministically before touching state:

  • "add / remind me to / put X on my list" → add
  • "done with X / finished X / completed X / ✅ X" → complete
  • "push X / defer X / move X to next week" → defer
  • "delete X / remove X / kill task X" → remove (explicit delete words only — never infer remove)
  • "what are my tasks / task list / what's on my plate (today)" → review ("today" filters to P0+P1)

Phases

  1. Load. get_page("ops/tasks.md"). First run: if the page does not exist, create it from the Output Format template, then proceed.
  2. Validate. Determine the action via Action Routing. If required fields are missing (see per-action rules), ask ONE concise clarification before mutating state. Never fabricate priorities, due dates, or defer reasons.
  3. Identify the target task (complete/defer/remove): match by id when given; otherwise fuzzy-match description against ACTIVE tasks only. Zero matches → return not_found, do not mutate. Multiple matches → list candidates with IDs, return ambiguous, do not mutate.
  4. Execute:
    • Add: Require a description. Priority: use the user's stated/clearly-implied level; otherwise default to P3 and say so in the reply + timeline entry. Due date only if supplied or explicit in the user's words. Mint a new task ID (t-YYYYMMDD-NN, NN = next free ordinal that day). Add a timeline entry.
    • Complete: Mark [x], move to Completed with (completed: YYYY-MM-DD).
    • Defer: Require a target date/timeframe AND a reason; ask if missing. Move to Deferred preserving original text, ID, and priority unless the user changes them.
    • Remove: Destructive — require explicit confirmation unless the user's message already contains it. Prefer suggesting complete or defer.
    • Review: Read-only. Never mutates. Active tasks grouped by priority, IDs shown.
  5. Save. put_page("ops/tasks.md") after any mutation. Diff-mindset: touch only the affected lines; preserve all other content, including sections this skill doesn't recognize.

Edge Cases

  • First run: page missing → create from template before acting; status: ok, note "initialized".
  • Malformed page: if ops/tasks.md exists but doesn't match the schema, do NOT rewrite it wholesale. Append/edit within it minimally, preserve unknown content verbatim, and flag the malformation in the reply.
  • Retry/duplicate add: if an identical description already exists in active tasks, do not add a duplicate — report the existing task ID instead.
  • Dates: ISO 8601 (YYYY-MM-DD) everywhere. Compute "today"/"next week" with code/clock, never guess.
  • Page identifier: always ops/tasks.md (with extension) in tool calls; this is the single canonical location.
  • Single-writer assumption (concurrency limitation). The task cycle is read-modify-write: get_page("ops/tasks.md") → edit → put_page("ops/tasks.md"). put_page replaces the WHOLE page and has no compare-and-swap, so two mutations that interleave are last-writer-wins: the second put_page overwrites the first's change (a completed task reappears, an added task vanishes), and the t-YYYYMMDD-NN minting can hand the same ordinal to two concurrent adds (duplicate IDs). Serialize task edits — never run parallel task mutations (multiple subagents, concurrent chat turns) against ops/tasks.md. If a mutation might race, re-get_page immediately before put_page and re-derive the next free ordinal from the freshly-read page.

Output Format

Persisted page format

Each task carries a stable ID so later actions can target it safely:

markdown
# Tasks

## P0 — Urgent
- [ ] <!-- id: t-20260115-01 --> {task description} (due: {date})

## P1 — Today
- [ ] <!-- id: {task-id} --> {task description} (due: {date optional})

## P2 — This Week
- [ ] <!-- id: {task-id} --> {task description} (due: {date optional})

## P3 — Backlog
- [ ] <!-- id: {task-id} --> {task description}

## Deferred
- [ ] <!-- id: {task-id} --> {task description} (deferred until: {date}; reason: {reason})

## Completed
- [x] <!-- id: {task-id} --> {task description} (completed: {date})

User-facing response

After a mutation: one concise line — action, task ID, priority/status, relevant date, saved-or-not. For review: active tasks grouped by priority. Keep replies compact; avoid tables on narrow chat surfaces.

Anti-Patterns

Each with its corrective action:

  • Adding a task without priority → default P3 and SAY the default was applied (never silent).
  • Mutating on an ambiguous reference → stop, list candidates with IDs, ask.
  • Completing without a completion date → always stamp (completed: YYYY-MM-DD).
  • Deferring without target date + reason → ask for both first.
  • Removing without explicit confirmation → confirm first; offer complete/defer instead.
  • Overwriting the page wholesale / dropping unknown sections → minimal diff edits only.
  • Using undeclared tools or CLI verbs → get_page/put_page/search/add_timeline_entry only.
  • Fabricating due dates, priorities, or reasons → never invent required fields; ask.
  • Unbounded list growth → when Backlog exceeds ~20 items, prompt a weekly review.
  • Storing tasks outside the brain page → everything lives in ops/tasks.md (searchable).
  • Running parallel task mutations against ops/tasks.md → last-writer-wins whole-page put_page silently loses updates and mints duplicate IDs; serialize edits, re-read immediately before writing.

Design Rationale (failure modes this version closes)

  • Interface drift: an earlier version declared get_page/put_page as tools but instructed CLI verbs in the body — models picked one at random. The declared tools are now the interface; CLI is relegated to a human-equivalent note.
  • Unmatchable tasks: without task IDs, "complete the deploy task" against two similar tasks silently mutated the wrong one. Stable t-YYYYMMDD-NN IDs + fail-closed ambiguity handling fix this.
  • First-run crash: assuming ops/tasks.md exists made a missing page undefined behavior. Create-from-template on first run fixes this.
  • Wholesale overwrite risk: "write updated task list" invited full-page rewrites that drop concurrent edits. Minimal-diff mandate + preserve-unknown-content rule fix this.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Daily Task Manager AI skill do?

Task lifecycle management with stable task IDs. Add, complete, defer, remove, and review tasks with deterministic action routing and fail-closed ambiguity handling. Maintains a running task list as a brain page.

Why use Daily Task Manager on TypingMind?

Because you install it once and use it with any model. Daily Task Manager is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Daily Task Manager in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/garrytan/gbrain/tree/master/plugin-variants/gbrain-daily/skills/daily-task-manager. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Daily Task Manager?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Daily Task Manager?

As many as you like. As long as a model supports skills, you can use Daily Task Manager with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Daily Task Manager AI skill free?

Yes. It is published on GitHub by garrytan under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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