start

anthropics/knowledge-work-plugins · updated Apr 8, 2026

MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.

$npx skills add https://github.com/anthropics/knowledge-work-plugins --skill start
0 commentsdiscussion
summary

If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.

skill.md

Start Command

If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.

Initialize the task and memory systems, then open the unified dashboard.

Instructions

1. Check What Exists

Check the working directory for:

  • TASKS.md — task list
  • CLAUDE.md — working memory
  • memory/ — deep memory directory
  • dashboard.html — the visual UI

2. Create What's Missing

If TASKS.md doesn't exist: Create it with the standard template (see task-management skill). Place it in the current working directory.

If dashboard.html doesn't exist: Copy it from ${CLAUDE_PLUGIN_ROOT}/skills/dashboard.html to the current working directory.

If CLAUDE.md and memory/ don't exist: This is a fresh setup — after opening the dashboard, begin the memory bootstrap workflow (see below). Place these in the current working directory.

3. Open the Dashboard

Do NOT use open or xdg-open — in Cowork, the agent runs in a VM and shell open commands won't reach the user's browser. Instead, tell the user: "Dashboard is ready at dashboard.html. Open it from your file browser to get started."

4. Orient the User

If everything was already initialized:

Dashboard open. Your tasks and memory are both loaded.
- /productivity:update to sync tasks and check memory
- /productivity:update --comprehensive for a deep scan of all activity

If memory hasn't been bootstrapped yet, continue to step 5.

5. Bootstrap Memory (First Run Only)

Only do this if CLAUDE.md and memory/ don't exist yet.

The best source of workplace language is the user's actual task list. Real tasks = real shorthand.

Ask the user:

Where do you keep your todos or task list? This could be:
- A local file (e.g., TASKS.md, todo.txt)
- An app (e.g. Asana, Linear, Jira, Notion, Todoist)
- A notes file

I'll use your tasks to learn your workplace shorthand.

Once you have access to the task list:

For each task item, analyze it for potential shorthand:

  • Names that might be nicknames
  • Acronyms or abbreviations
  • Project references or codenames
  • Internal terms or jargon

For each item, decode it interactively:

Task: "Send PSR to Todd re: Phoenix blockers"

I see some terms I want to make sure I understand:

1. **PSR** - What does this stand for?
2. **Todd** - Who is Todd? (full name, role)
3. **Phoenix** - Is this a project codename? What's it about?

Continue through each task, asking only about terms you haven't already decoded.

6. Optional Comprehensive Scan

After task list decoding, offer:

Do you want me to do a comprehensive scan of your messages, emails, and documents?
This takes longer but builds much richer context about the people, projects, and terms in your work.

Or we can stick with what we have and add context later.

If they choose comprehensive scan:

Gather data from available MCP sources:

  • Chat: Recent messages, channels, DMs
  • Email: Sent messages, recipients
  • Documents: Recent docs, collaborators
  • Calendar: Meetings, attendees

Build a braindump of people, projects, and terms found. Present findings grouped by confidence:

  • Ready to add (high confidence) — offer to add directly
  • Needs clarification — ask the user
  • Low frequency / unclear — note for later

7. Write Memory Files

From everything gathered, create:

CLAUDE.md (working memory, ~50-80 lines):

# Memory

## Me
[Name], [Role] on [Team].

## People
| Who | Role |
|-----|------|
| **[Nickname]** | [Full Name], [role] |

## Terms
| Term | Meaning |
|------|---------|
| [acronym] | [expansion] |

## Projects
| Name | What |
|------|------|
| **[Codename]** | [description] |

## Preferences
- [preferences discovered]

memory/ directory:

  • memory/glossary.md — full decoder ring (acronyms, terms, nicknames, codenames)
  • memory/people/{name}.md — individual profiles
  • memory/projects/{name}.md — project details
  • memory/context/company.md — teams, tools, processes

8. Report Results

Productivity system ready:
- Tasks: TASKS.md (X items)
- Memory: X people, X terms, X projects
- Dashboard: open in browser

Use /productivity:update to keep things current (add --comprehensive for a deep scan).

Notes

  • If memory is already initialized, this just opens the dashboard
  • Nicknames are critical — always capture how people are actually referred to
  • If a source isn't available, skip it and note the gap
  • Memory grows organically through natural conversation after bootstrap
how to use start

How to use start on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add start
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/anthropics/knowledge-work-plugins --skill start

The skills CLI fetches start from GitHub repository anthropics/knowledge-work-plugins and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/start

Reload or restart Cursor to activate start. Access the skill through slash commands (e.g., /start) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.560 reviews
  • Henry Torres· Dec 24, 2024

    I recommend start for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Ren Torres· Dec 20, 2024

    We added start from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Ren Jackson· Dec 12, 2024

    start has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Ira Johnson· Dec 12, 2024

    Solid pick for teams standardizing on skills: start is focused, and the summary matches what you get after install.

  • Pratham Ware· Dec 4, 2024

    Useful defaults in start — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Sakshi Patil· Nov 23, 2024

    start has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Diego Ramirez· Nov 15, 2024

    Solid pick for teams standardizing on skills: start is focused, and the summary matches what you get after install.

  • Jin Jain· Nov 11, 2024

    Keeps context tight: start is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Alexander Smith· Nov 3, 2024

    Useful defaults in start — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Neel Harris· Nov 3, 2024

    I recommend start for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

showing 1-10 of 60

1 / 6