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Task

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codeaholicguy
task

AI DevKit · Track dev-lifecycle / structured-debug progress on a durable task with the ai-devkit task CLI. Use to record phase, progress, next step, blockers, and validation evidence.

Overview

Publishercodeaholicguy
Repositoryai-devkit
Skill nametask
Stars
1.6K
Forks
252
Bundled files
1
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.

  • 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 codeaholicguy on GitHub. Read the source before you install it.

Installation

Install the Task 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/codeaholicguy/ai-devkit.git /tmp/ai-devkit
mkdir -p .claude/skills
cp -r /tmp/ai-devkit/skills/task .claude/skills/task
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Task 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 Task 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 Task 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.

Task Progress Tracking

Record development progress on a durable task: phase, progress, next step, blockers, and validation evidence.

Requires the optional task command. Use npx ai-devkit@latest for task and agent commands. Before recording task events, run a real read probe:

bash
npx ai-devkit@latest task list --json
# or, when a task name is known:
npx ai-devkit@latest task list --name <task-name> --json

Only treat task tracing as available when the read probe exits 0. If it fails, continue without task logging and include the failed command plus stderr/stdout summary in the final report. Do not block the user's work just because optional task tracing is unavailable or unusable.

Core idea

  • One task per work item. Create it once; advance its phase field as work moves through the lifecycle or debug workflow.
  • <id> can be a task name. Every command below accepts the task name in place of a task id, resolving to the latest non-terminal task. Prefer <task-name> so agents do not track task ids.
  • Choose stable names. For lifecycle work, use the feature key as the task name. For debugging or review work, choose a short kebab-case task name.
  • Emit at checkpoints, not streaming. Phase transitions, task toggles, immediate next-step changes, fresh evidence, blockers discovered/resolved. A handful of calls per session.
  • Sequence mutations. Never run task mutation commands in parallel for the same task. Each mutation reads the current task snapshot and writes it back; parallel writes can clobber snapshot fields even though events append. Run create/assign/phase/next/progress/evidence/blocker/artifact/close commands one at a time, then read back with show --events --json when the final state matters.
  • Attribution is explicit. Identify self once, then pass actor flags on mutation commands.

Identify self

Use agent-management when attribution is needed:

  1. Run the agent-management self-identification workflow with npx ai-devkit@latest agent list --json.
  2. Match the current agent entry from that list. Prefer an exact session match when available; otherwise use the unambiguous entry for the current project/worktree.
  3. Build actor flags from the matched entry: --agent <agent-name> --agent-type <agent-type> --pid <pid> --session <sessionId>. Map JSON fields directly: name -> --agent, type -> --agent-type, pid -> --pid, and sessionId -> --session.
  4. If identity is ambiguous, do not guess. Continue task logging without actor flags rather than fabricating attribution.
  5. Add --agent <agent-name> --agent-type <agent-type> --pid <pid> --session <sessionId> to every mutation command once known. If a task already exists, run npx ai-devkit@latest task assign <task-name> --agent <agent-name> --agent-type <agent-type> --pid <pid> --session <sessionId> --json once so the task snapshot has current ownership.
  6. If actor identity is unknown, run the same mutation commands without the four actor flags.

Canonical commands

When self identity is known, add all four actor flags to every mutation command: --agent <agent-name> --agent-type <agent-type> --pid <pid> --session <sessionId>.

bash
# Create the task once (capture taskId from --json if needed)
npx ai-devkit@latest task create --title "<title>" --name <task-name> --phase requirements --agent <agent-name> --agent-type <agent-type> --pid <pid> --session <sessionId> --json

# If the task already exists, assign current ownership once when known
npx ai-devkit@latest task assign <task-name> --agent <agent-name> --agent-type <agent-type> --pid <pid> --session <sessionId> --json

# Mark real work as active after create/resume
npx ai-devkit@latest task status <task-name> active --agent <agent-name> --agent-type <agent-type> --pid <pid> --session <sessionId> --json

# Advance phase as the lifecycle moves on
npx ai-devkit@latest task phase <task-name> implementation --agent <agent-name> --agent-type <agent-type> --pid <pid> --session <sessionId> --json

# Progress (use --text; positional text is ignored)
npx ai-devkit@latest task progress <task-name> --text "Implementing task CLI" --agent <agent-name> --agent-type <agent-type> --pid <pid> --session <sessionId> --json

# Next step
npx ai-devkit@latest task next <task-name> "Run validation" --agent <agent-name> --agent-type <agent-type> --pid <pid> --session <sessionId> --json

# Blockers
npx ai-devkit@latest task status <task-name> blocked --agent <agent-name> --agent-type <agent-type> --pid <pid> --session <sessionId> --json
npx ai-devkit@latest task blocker <task-name> add "Waiting for review" --agent <agent-name> --agent-type <agent-type> --pid <pid> --session <sessionId> --json
npx ai-devkit@latest task blocker <task-name> resolve <blocker-id> --agent <agent-name> --agent-type <agent-type> --pid <pid> --session <sessionId> --json
npx ai-devkit@latest task status <task-name> active --agent <agent-name> --agent-type <agent-type> --pid <pid> --session <sessionId> --json

# Validation evidence - record after a fresh verify/tdd/test run
npx ai-devkit@latest task evidence <task-name> --passed --command "npm test" --exit-code 0 --summary "tests passed" --agent <agent-name> --agent-type <agent-type> --pid <pid> --session <sessionId> --json

# Reference an artifact (never copies the file)
npx ai-devkit@latest task artifact <task-name> docs/ai/testing/foo.md --kind test-report --description "Testing notes" --agent <agent-name> --agent-type <agent-type> --pid <pid> --session <sessionId> --json

# Read current status / list
npx ai-devkit@latest task show <task-name> --json
npx ai-devkit@latest task list --name <task-name> --json

# Close at lifecycle end
npx ai-devkit@latest task close <task-name> completed --agent <agent-name> --agent-type <agent-type> --pid <pid> --session <sessionId> --json

When to emit (by workflow)

  • dev-lifecycle - real read probe first; create at start when no non-terminal task exists for the feature; assign once when actor is known; set status active when real work starts or resumes; phase on every phase transition; next after phase planning; progress after planning/implementation task toggles; show at resume; close completed only after final verification/review is done.
  • verify / tdd / dev-testing - evidence after fresh proof (this is what makes "last validation" trustworthy). Use --failed when it fails.
  • structured-debug - reuse the same commands: evidence for repro results, next for the next hypothesis, blocker add/resolve, progress.
  • Any phase - blocker add when blocked, resolve when clear; next to state the immediate next step. Set status blocked when an open blocker stops progress, and set status active again after the blocker is resolved.

Tips

  • Add --json when an agent must parse output (create/show/list). Omit for human-readable checks.
  • Don't restate obvious nearby files or transient state; keep summaries short.
  • Good task records let a later reader answer: who worked on it, which phase it reached, what changed, what is next, what verified the claim, and what blocked or changed scope. Do not log every command; do log those checkpoints.

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 Task AI skill do?

AI DevKit · Track dev-lifecycle / structured-debug progress on a durable task with the ai-devkit task CLI. Use to record phase, progress, next step, blockers, and validation evidence.

Why use Task on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/codeaholicguy/ai-devkit/tree/main/skills/task. 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 Task?

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 Task?

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

Is the Task AI skill free?

Yes. It is published on GitHub by codeaholicguy 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.

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