Structured hiring framework from 94 product leaders to make stronger candidate decisions.
Run in your terminal
Apply 12 core principles covering reference checks, work trials, agency assessment, and T-shaped hiring to evaluate candidates systematically
Use diagnostic questions to understand hiring stage, team gaps, and whether decisions are based on structured rubrics or intuition alone
Challenge common biases like pedigree shortcuts, gut-feel-only decisions, and unicorn hiring; prioritize references, pa
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionevaluating-candidatesExecute the skills CLI command in your project's root directory to begin installation:
Package manager
npx skills add https://github.com/refoundai/lenny-skills --skill evaluating-candidatesFetches evaluating-candidates from refoundai/lenny-skills 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 evaluating-candidates. Access via /evaluating-candidatesin 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
Package manager
npx skills add https://github.com/refoundai/lenny-skills --skill evaluating-candidatesWorks with
0
total installs
0
this week
616
GitHub stars
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upvotes
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
ailabs-393/ai-labs-claude-skills
greedychipmunk/agent-skills
We added evaluating-candidates from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Registry listing for evaluating-candidates matched our evaluation — installs cleanly and behaves as described in the markdown.
evaluating-candidates fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
I recommend evaluating-candidates for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
evaluating-candidates reduced setup friction for our internal harness; good balance of opinion and flexibility.
evaluating-candidates fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
We added evaluating-candidates from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Keeps context tight: evaluating-candidates is the kind of skill you can hand to a new teammate without a long onboarding doc.
Useful defaults in evaluating-candidates — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
evaluating-candidates has been reliable in day-to-day use. Documentation quality is above average for community skills.
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