Save important project knowledge based on what the user wants to preserve.
Works with
Save important project knowledge based on what the user wants to preserve.
Analyze what the user is asking to save from the conversation.
[SAVE:<username>:<date>]
<Username> wanted to <goal/problem>.
Claude suggested <approach/solution>.
<Username> decided to <decision made>.
<key details, files if relevant>
[/SAVE]
Example:
[SAVE:prasanna:2026-02-04]
Prasanna wanted to create a skill for saving project knowledge.
Claude suggested using a separate container tag (repo_<hash>) for shared team knowledge.
Prasanna decided to keep it simple - no transcript fetching, just save what user asks for.
Files: src/save-project-memory.js, src/lib/container-tag.js
[/SAVE]
Keep it natural. Capture the conversation flow.
node "${CLAUDE_PLUGIN_ROOT}/scripts/save-project-memory.cjs" "FORMATTED_CONTENT"
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionsuper-saveExecute the skills CLI command in your project's root directory to begin installation:
Fetches super-save from supermemoryai/claude-supermemory 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 super-save. Access via /super-save 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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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
We added super-save from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Registry listing for super-save matched our evaluation — installs cleanly and behaves as described in the markdown.
super-save has been reliable in day-to-day use. Documentation quality is above average for community skills.
Solid pick for teams standardizing on skills: super-save is focused, and the summary matches what you get after install.
super-save reduced setup friction for our internal harness; good balance of opinion and flexibility.
Useful defaults in super-save — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Keeps context tight: super-save is the kind of skill you can hand to a new teammate without a long onboarding doc.
super-save fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Keeps context tight: super-save is the kind of skill you can hand to a new teammate without a long onboarding doc.
Solid pick for teams standardizing on skills: super-save is focused, and the summary matches what you get after install.
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