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Tasx

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vicnaum
tasx

File-based task tracker for projects: tasks live as markdown files in a hidden, git-ignored .tasx/ folder, state = folder location (root = inbox, in-progress/, waiting/, done/, cancelled/, decisions/ for open choices), agents manage tasks by moving the files; plus a zero-dependency local board UI (tasx serve) where the user changes states, answers decisions, and leaves comments that are written straight back into the md files and nudge the owning agent via agent-chat. Use when: (1) the user asks to set up / init a task tracker or tasks folder in a project, (2) the user or agent needs to create, list, move, complete, cancel, or comment on tasks in a repo that has a .tasx/ folder, (3) the user asks 'what's in progress', 'what needs me', 'what's stale', or wants a task board / dashboard served, (4) starting a work session in a repo with .tasx/ or a legacy tasks/ tree (run tasx doctor and reconcile), (5) a task is blocked on the user's feedback or on a decision — file it as waiting/ or a decision instead of asking and losing the thread, (6) generalizing/migrating older ad-hoc task folders (myhdd-style) onto the shared convention. Triggers on: tasks folder, task tracker, task board, tasx, kanban, what's in progress, needs my feedback, stale tasks, task dashboard.

Overview

Publishervicnaum
Repositoryvics-agent-skills
Skill nametasx
Stars
53
Forks
5
Bundled files
3
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.

  • 3 bundled files

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

  • Open source

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

Installation

Install the Tasx 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.
    https://github.com/vicnaum/vics-agent-skills/tree/main/skills/tasx
  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/vicnaum/vics-agent-skills.git /tmp/vics-agent-skills
mkdir -p .claude/skills
cp -r /tmp/vics-agent-skills/skills/tasx .claude/skills/tasx
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Tasx 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 Tasx 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 Tasx 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.

tasx — file-based tasks, folder = state

Markdown files are the source of truth; the folder a file sits in IS its state. No database, no daemon: ls .tasx/in-progress/ is a kanban, mv is a status change, and the board UI is just a viewer/editor over the same files. The folder is hidden and git-ignored per repo (tasx init adds it to .git/info/exclude), so tasks never leak into commits of the project you're working in. The CLI lives at scripts/tasx (installed on PATH as tasx).

Layout

.tasx/               INBOX — new/untriaged tasks as *.md  (git-ignored)
  README.md          the convention doc (written by `tasx init`)
  in-progress/       actively worked — set `owner:` to your agent-chat name
  waiting/           blocked — `waiting-on:` header says on what (see below)
  done/              completed (kept; `tasx archive` rolls old ones into done/YYYY-MM/)
  cancelled/         dropped — say why in the file
  decisions/         open choices: `option:` lines; picking writes `choice:` → decided

Task file = optional header lines (id:, kind:, owner:, group:, seq:, title:, waiting-on:, option:, choice:), blank line, then a markdown body (What / Why / Next steps / Refs), optionally ending with ## Comments. Headerless files work too (id = filename, title = first # heading) — legacy myhdd-style folders parse as-is, blocked/ is read as waiting/. Full format spec: references/format.md.

Commands

bash
tasx init [--gitignore]            # create .tasx/ tree + README + CLAUDE.md/AGENTS.md pointer,
                                   #   git-ignore it (.git/info/exclude; --gitignore = shared .gitignore),
                                   #   rename a legacy tasks/ tree to .tasx/
tasx new "Title" [--owner me] [--group area] [--body "..."] [--id slug]
tasx new "Which renderer?" --decision --option "a | raster" --option "b | webgl"
tasx list [--all]                  # kanban to stdout (needs-you / in-progress / inbox / waiting)
tasx move <id> <state> [--waiting-on "you|decision <id>|external: <thing>"]
tasx comment <id> "text" --as <your-agent-name>    # timestamped; nudges the owner
tasx doctor                        # staleness + hygiene report — run at session start
tasx archive [--days 30]           # roll old done/ files into done/YYYY-MM/
tasx serve [--port N]              # board UI on a stable per-project port (127.0.0.1)

Commands find the nearest .tasx/ walking up from cwd ($TASX_TASKS overrides). A pre-rename tasks/ folder is still found when it looks like a tasx tree (convention README or a state subfolder) — tasx doctor nags, tasx init renames it.

Agent contract

Working in a repo that has a .tasx/ folder means playing by these rules:

  1. Session start: run tasx doctor. Reconcile what you own — finished things go to done, stalled things get a comment saying why, tasks owned by a dead agent get taken over or moved back to the inbox.
  2. Starting work: tasx move <id> in-progress and set owner: to your agent-chat name (edit the header or create the task with --owner). One owner per task. Don't work on tasks owned by a live agent — agent-chat status <name>.
  3. Blocked on the user: don't ask and idle. Move it: tasx move <id> waiting --waiting-on you, put the question in the file (or tasx comment). If it's a real fork in the road, create a decision (tasx new ... --decision --option ...) and set --waiting-on "decision <id>" — the user answers with one click on the board and you get nudged via agent-chat.
  4. Finishing: tasx move <id> done + a short tasx comment <id> "what shipped, where" --as <name>. Cancelling requires a why.
  5. New work discovered mid-task: don't silently expand scope — tasx new it into the inbox and continue.
  6. Session end: nothing should be in in-progress/ under your name unless you (or a respawn of you) are actually continuing it. Move it back or comment its state.
  7. Keep files lightweight; body sections What / Why / Next steps / Refs. Comments are append-only with author + timestamp — never rewrite others' comments.

The board (tasx serve)

For the human, not for agents (agents use the CLI/files). Serves scripts/board.html on a stable per-project port. Reads nothing but the md files; every edit writes back: status dropdown physically moves the file (git mv-aware), decision radios write choice:, comments append to ## Comments — and each of those nudges the owning agent through agent-chat send --to <owner> (when agent-chat is installed), so answering on the board resumes the work. Comments send only on an explicit button or ⌘⏎ (drafts survive re-renders, nothing fires on blur). On needs-you cards the primary action is answer & resume: append the answer, move the task back to in-progress (inbox if unowned), clear waiting-on:, one nudge — the human never needs to touch the state dropdown just to hand a question back. UI shows: pinned Needs you section + count in the tab title, in-progress with owner liveness dots (agent-chat status) and freshness ages, ⚠ stale chips (in-progress >24h, waiting-on-you >3d, inbox >14d), a "since your last visit" digest, and a collapsed done/cancelled archive grouped by day.

Run it in background when asked: nohup tasx serve > /tmp/tasx-board.log 2>&1 & then read the port from the log's first line.

Install (new machine / new agent CLI)

Symlink the skill folder into the agent's skill dirs and put tasx on PATH:

bash
ln -s ~/github/vics-agent-skills/skills/tasx ~/.claude/skills/tasx
ln -s ~/github/vics-agent-skills/skills/tasx ~/.codex/skills/tasx
ln -s ~/github/vics-agent-skills/skills/tasx ~/.cursor/skills/tasx
ln -s ~/github/vics-agent-skills/skills/tasx/scripts/tasx ~/bin/tasx   # or any PATH dir

Python 3.8+ stdlib only. agent-chat integration is optional and auto-detected. Optionally export TASX_USER=<yourname>: board comments get authored with it, and waiting-on: <yourname> counts as needs-you alongside the generic you/user/human.

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 Tasx AI skill do?

File-based task tracker for projects: tasks live as markdown files in a hidden, git-ignored .tasx/ folder, state = folder location (root = inbox, in-progress/, waiting/, done/, cancelled/, decisions/ for open choices), agents manage tasks by moving the files; plus a zero-dependency local board UI (tasx serve) where the user changes states, answers decisions, and leaves comments that are written straight back into the md files and nudge the owning agent via agent-chat. Use when: (1) the user asks to set up / init a task tracker or tasks folder in a project, (2) the user or agent needs to cre...

Why use Tasx on TypingMind?

Because you install it once and use it with any model. Tasx 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 Tasx in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/vicnaum/vics-agent-skills/tree/main/skills/tasx. 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 Tasx?

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 Tasx?

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

Is the Tasx AI skill free?

It is published on GitHub by vicnaum. Check the repository for licensing terms. 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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