If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionstartExecute the skills CLI command in your project's root directory to begin installation:
Fetches start from anthropics/knowledge-work-plugins and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate start. Access via /start in your agent's command palette.
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 environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
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
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
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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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.
Check the working directory for:
TASKS.md — task listCLAUDE.md — working memorymemory/ — deep memory directorydashboard.html — the visual UIIf 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.
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."
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.
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:
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.
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:
Build a braindump of people, projects, and terms found. Present findings grouped by confidence:
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 profilesmemory/projects/{name}.md — project detailsmemory/context/company.md — teams, tools, processesProductivity 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).
Make data-driven prioritization decisions faster
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
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
mattpocock/skills
I recommend start for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
We added start from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
start has been reliable in day-to-day use. Documentation quality is above average for community skills.
Solid pick for teams standardizing on skills: start is focused, and the summary matches what you get after install.
Useful defaults in start — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
start has been reliable in day-to-day use. Documentation quality is above average for community skills.
Solid pick for teams standardizing on skills: start is focused, and the summary matches what you get after install.
Keeps context tight: start is the kind of skill you can hand to a new teammate without a long onboarding doc.
Useful defaults in start — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
I recommend start for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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