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cwinvestments
work

Use when the user says 'plan', 'todo', 'copy plan', 'append plan', 'resume plan', 'priorities', or 'what's next'.

Overview

Publishercwinvestments
Repositorymemstack
Skill namework
Stars
425
Forks
45
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Work 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/cwinvestments/memstack/tree/master/skills/work
  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/cwinvestments/memstack.git /tmp/memstack
mkdir -p .claude/skills
cp -r /tmp/memstack/skills/work .claude/skills/work
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Work 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 Work 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 Work 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.

📋 Work: Plan Execution Engaged

Track tasks, manage plans, and survive CC compacts with three operating modes.

Activation

When this skill activates, output:

📋 Work: Plan execution engaged.

Then determine which mode to use based on the trigger.

Context Guard

ContextStatus
User says "copy plan", "append plan", "resume plan"ACTIVE: use matching mode
User says "what's next", "todo", "priorities"ACTIVE: quick query mode
User provides a task list or planACTIVE: copy mode
General discussion about planning conceptsDORMANT: do not activate
User is executing a task (not managing the list)DORMANT: do not activate

Step 0: Silent Context Compilation (MANDATORY)

Before executing ANY mode, silently gather current state. Do NOT present findings. Do NOT ask questions. Just internalize:

  1. Read the project's STATE.md (if it exists): current task, blockers, next steps
  2. Read the project's CLAUDE.md (if it exists): conventions, architecture decisions
  3. Check recent diary:
    bash
    python "${CLAUDE_PLUGIN_ROOT}/db/memstack-db.py" get-sessions <project> --limit 3
  4. Check git state:
    bash
    git log --oneline -5
    git diff --stat

This is silent. Synthesize an internal understanding of where the project stands. Then proceed to the triggered mode with full context. The user already knows their project state, don't waste their time repeating it back.

Mode 1: Copy Plan

Trigger: "copy plan" or when a new plan is provided

  1. Parse the entire plan into individual numbered tasks

  2. For each task, save to SQLite:

    Write the payload to a file, then pipe it in with - as the argument. A payload on stdin is never seen by the shell's parser, so a redirection operator inside a task description cannot be read as one.

    bash
    cat task.json | python "${CLAUDE_PLUGIN_ROOT}/db/memstack-db.py" add-plan-task -

    task.json contains:

    json
    {"project":"<name>","task_number":<n>,"description":"<task>","status":"pending"}
  3. Confirm with task count

  4. Also write a markdown copy to memory/projects/{project}-plan.md for human readability

Status values: pending, in_progress, completed, blocked

Mode 2: Append Plan (Update Tasks)

Trigger: "append plan" or when updating task statuses

  1. Read current plan from SQLite:

    bash
    python "${CLAUDE_PLUGIN_ROOT}/db/memstack-db.py" get-plan <project>
  2. Update individual task statuses:

    Same form, even though this payload is only metadata. One rule with no exceptions is easier to follow than a rule you have to judge.

    bash
    cat task-update.json | python "${CLAUDE_PLUGIN_ROOT}/db/memstack-db.py" update-task -

    task-update.json contains:

    json
    {"project":"<name>","task_number":<n>,"status":"completed"}
  3. Add new tasks if needed via add-plan-task

  4. No size limits needed: SQLite handles scale

Mode 3: Resume Plan

Trigger: "resume plan", use after CC compact or new session

  1. Load plan from SQLite:
    bash
    python "${CLAUDE_PLUGIN_ROOT}/db/memstack-db.py" get-plan <project>
  2. Parse the JSON output for task statuses
  3. Output a summary:
    Plan: {project} ({done}/{total} complete)
    
    Completed: [list]
    In Progress: [list]
    Pending: [list]
    Blocked: [list with reasons]
    
    Recommended next: {first pending task}
  4. Continue from the first incomplete task

Quick Commands

  • "what's next": queries plan, returns the single next pending task
  • "priorities": shows top 3 pending items from the plan
  • "todo": shows all pending and in-progress items with status

Inputs

  • Plan text (copy mode) or project name (append/resume)
  • Database: ~/.memstack/memstack.db (via memstack-db.py)
  • Fallback: $MEMSTACK_PATH/memory\projects\ (legacy markdown)

Outputs

  • Formatted task list with status indicators
  • Updated plan file in memory
  • Next recommended action

Example Usage

User: "resume plan for AdminStack"

📋 Work: Plan execution engaged.

Plan: AdminStack (5/9 complete)

[x] 1. Build CC Monitor page
[x] 2. Add setup guide
[x] 3. Fix API key validation
[x] 4. Add refresh feedback
[x] 5. Update guide with curl snippet
[ ] 6. Build cc-reporter.js Node script
[ ] 7. Add WebSocket real-time updates
[ ] 8. Session grouping by project
[!] 9. Mobile polish (blocked: waiting for design specs)

Recommended next: Task 6: Build cc-reporter.js

Level History

  • Lv.1: Base: Single-mode TODO tracking. (Origin: MemStack v1.0, Feb 2026)
  • Lv.2: Enhanced: Three modes (copy/append/resume), 1K-line limit with auto-summarize, context guard, YAML frontmatter. (Origin: MemStack v2.0 MemoryCore merge, Feb 2026)
  • Lv.3: Advanced: SQLite-backed plans with per-task status tracking, no size limits, structured queries. (Origin: MemStack v2.1 Accomplish-inspired upgrade, Feb 2026)
  • Lv.4: Native: CC rules integration (.claude/rules/work.md), always-on task planning awareness without skill file read. (Origin: MemStack v3.0-beta, Feb 2026)
  • Lv.5: Context-aware: Silent context compilation (Step 0): reads STATE.md, CLAUDE.md, recent diary, and git state before any operation. Inspired by Intellegix silent pre-flight pattern. (Origin: MemStack v3.2, Feb 2026)

Frequently asked questions

What does the Work AI skill do?

Use when the user says 'plan', 'todo', 'copy plan', 'append plan', 'resume plan', 'priorities', or 'what's next'.

Why use Work on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/cwinvestments/memstack/tree/master/skills/work. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Work?

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 Work?

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

Is the Work AI skill free?

Yes. It is published on GitHub by cwinvestments 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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