Manage Taskboard logo

Manage Taskboard

CommunityPopular
chuspeeism
manage-taskboard

Manage Codex Taskboard issues and taskctl setup when the request names Codex Taskboard, e-taskboard, or taskctl, or the conversation already establishes that board as the target. Not for GitHub, Phabricator, other external trackers, or unrelated product docs.

Overview

Publisherchuspeeism
Repositorydashi-taskboard
Skill namemanage-taskboard
Stars
3.1K
Forks
449
Bundled files
2
LicenseApache-2.0
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.

  • 2 bundled files

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

  • Open source

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

Installation

Install the Manage Taskboard 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/chuspeeism/dashi-taskboard.git /tmp/dashi-taskboard
mkdir -p .claude/skills
cp -r /tmp/dashi-taskboard/skills/manage-taskboard .claude/skills/manage-taskboard
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Manage Taskboard 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 Manage Taskboard 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 Manage Taskboard 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.

Manage Taskboard

This skill serves the local-first Codex Taskboard product, including its configured LAN and cloud services.

Apply the workflow below only to work explicitly targeting Codex Taskboard or already established as belonging to it in the conversation. An issue identifier, repository, or generic request to manage tasks, sync status, or add comments does not establish that scope. For GitHub, Phabricator, or another external tracker, use that system's tools and workflow; do not query, claim, or mirror its issues in Taskboard unless the user asks for that board operation. When the target is unclear, clarify it before running taskctl.

Within that scope, use taskctl for every project, issue, relation, and comment operation. Consume its JSON output. Use the exact issue identifier returned by the taskboard or supplied in the prompt. Never assume, derive, or rewrite an identifier prefix.

Open only the relevant section of references/cli.md when command syntax is needed.

Select the CLI and active service

  • Use the exact taskctl binary and Taskboard URL supplied by the task or injected runtime. Do not replace them with a global CLI, the default port, or another board.
  • On macOS, when no binary is injected and the desktop app is installed, use '/Applications/Codex Taskboard.app/Contents/Resources/bin/taskctl' issue get ID --json. Keep the single quotes because the path contains a space. The packaged wrapper reads the active launcher runtime; do not search the filesystem for another CLI or reconstruct the tokenized URL.
  • On Linux, when no binary is injected and Codex was started by the desktop app, use taskctl issue get ID --json. The desktop app adds its packaged wrapper to the managed Codex PATH; do not search the filesystem for another CLI or reconstruct the tokenized URL.
  • If that exact command reaches a sandbox restriction on the loopback service, retry the same command with the required permission. Do not switch binaries or endpoints.

Terminology: local companion

In this product, companion means the device-local loopback service used for cloud mode (Codex/Git/Skill/MCP, path mapping, Basic Auth proxy). Related names: local companion, loopback companion, CODEX_TASKBOARD_COMPANION_URL, cloud-companion.json, LOCAL_COMPANION_REQUIRED.

When writing Chinese, keep the English word or use 本地 companion / 本地配套服务 / 环回代理. Never translate as 伴侣 or invent 伴侣 API. Ordinary task/comment/attachment HTTP routes (/api/tasks, /api/comments, /api/attachments, …) are the Taskboard HTTP API (or local server API)—not “companion API”.

Core workflow

  1. For an existing issue, first run issue get and comment list. Also run attachment list --task. On the first handoff, omit --after and read the full results. Keep the separate nextCursor from each list. When the same task resumes, run issue get again, then pass each saved cursor to its matching list with --after so only new or modified entries are returned. Comment lists include the attachments on returned comments; use attachment list --comment with its own cursor when a known comment attachment list can grow. Read the description and latest comments before deciding whether to start. Treat comments as current requirements, including returned work. If they say to wait, not execute, or not start now, stop and report without changing the status.
  2. Treat backlog as not approved for execution. Unless the user explicitly authorizes that issue, do not claim it, move it to another status, or perform task work; its assignee alone is not authorization. If work may start, claim it before reading code, downloading attachments, analyzing the implementation, or doing any other task work. Move a claimable todo to in_progress with its current version; do not continue until the move succeeds. If it is already in_progress, continue only when it is bound to the current conversation. Never move an issue claimed by another conversation.
  3. If the move conflicts because the version is stale, run issue get and comment list again. Retry once with the latest version only when the issue is still a claimable todo, is not bound to another conversation, is not archived, and its description and latest comments are unchanged. If it was claimed, its status or requirements changed, it is archived, the service is unavailable, a permanent API error occurs, or the retry fails, stop and report. Never loop or take over another agent's claim.
  4. For a new durable requirement, run context current. Treat its project as a workspace match only when project.workspacePath is the current directory or one of its ancestors. An unmatched local project is the documented fallback, not proof that the requirement belongs in the global project. If the user named a target project or the working directory identifies one, run project list, select that exact project by id or name, and stop to ask if the result is ambiguous. Search existing project issues before creating one in that confirmed project, then pass its explicit id to issue create. Update a matching issue instead of creating a duplicate. Use the fallback only when the user explicitly wants the global project. Do not track trivial requests.
  5. Execute only the requested work in the issue's branch or worktree when one is bound.
  6. Verify the requested operation path. Add a comment with the changes, verification result, outcome, and remaining risks. Read the issue again, then move it to in_review with its current version.
  7. Move an issue to done only after the user explicitly accepts it or asks to complete it. Use blocked when work cannot continue and canceled when it will not continue.

Other operations

  • Run taskctl project readme get [PROJECT_ID] to inspect project architecture, constraints, and conventions before planning or executing complex tasks.
  • Keep the project README focused on root overview and conventions; store detailed multi-page documentation in the local repository's docs/ folder.
  • Preserve existing issue scope when adding requirements or acceptance details.
  • Add only relations that the work requires. Use parent for contained work, blocks or blocked_by for dependencies, and related for close association.
  • For Codex controller attribution, let taskctl read CODEX_THREAD_ID or pass the exact Codex conversation ID with --thread-id. This value alone is not a complete task binding. For Claude Code, Pi, AGY, or Grok session traceability, pass --agent-platform claude|pi|agy|grok --session-id ID instead; see CLI session traceability. External metadata is not a Codex ownership binding and never substitutes for the five fields below.
  • Any issue that the current conversation claims or continues must store a complete threadBinding: threadId, codexProjectId, codexProjectKind, codexHostId, and workspacePath. For an unbound local issue launched with injected Taskboard context, use the current CODEX_THREAD_ID, the injected project id and workspace path, local project kind, and local host id. Pass all five explicit --binding-* options on the claim and every later issue move that retains ownership. If any identity field is unavailable, stop before moving the issue to in_progress; never create a legacy binding containing only threadId.
  • When an issue already has a complete threadBinding, preserve its exact five saved values on every status write. Do not rebuild or replace it from the current context, and never take over a binding owned by another conversation.
  • Use the latest returned version with --if-version for concurrent updates. On conflict, read the issue again and reconcile before retrying.
  • Download and inspect an inline ![alt](api/attachments/<id>/content) image only when it is needed to understand the requirement.

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 Manage Taskboard AI skill do?

Manage Codex Taskboard issues and taskctl setup when the request names Codex Taskboard, e-taskboard, or taskctl, or the conversation already establishes that board as the target. Not for GitHub, Phabricator, other external trackers, or unrelated product docs.

Why use Manage Taskboard on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/chuspeeism/dashi-taskboard/tree/main/skills/manage-taskboard. 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 Manage Taskboard?

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 Manage Taskboard?

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

Is the Manage Taskboard AI skill free?

Yes. It is published on GitHub by chuspeeism under the Apache-2.0 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇