Rank all agent results for a session. Supports metric-based evaluation (run a command), LLM judge (compare diffs), or hybrid.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionevalExecute the skills CLI command in your project's root directory to begin installation:
Fetches eval from alirezarezvani/claude-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 eval. Access via /eval 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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Rank all agent results for a session. Supports metric-based evaluation (run a command), LLM judge (compare diffs), or hybrid.
/hub:eval # Eval latest session using configured criteria
/hub:eval 20260317-143022 # Eval specific session
/hub:eval --judge # Force LLM judge mode (ignore metric config)
Run the evaluation command in each agent's worktree:
python {skill_path}/scripts/result_ranker.py \
--session {session-id} \
--eval-cmd "{eval_cmd}" \
--metric {metric} --direction {direction}
Output:
RANK AGENT METRIC DELTA FILES
1 agent-2 142ms -38ms 2
2 agent-1 165ms -15ms 3
3 agent-3 190ms +10ms 1
Winner: agent-2 (142ms)
For each agent:
git diff {base_branch}...{agent_branch}.agenthub/board/results/agent-{i}-result.mdPresent rankings with justification.
Example LLM judge output for a content task:
RANK AGENT VERDICT WORD COUNT
1 agent-1 Strong narrative, clear CTA 1480
2 agent-3 Good data points, weak intro 1520
3 agent-2 Generic tone, no differentiation 1350
Winner: agent-1 (strongest narrative arc and call-to-action)
python {skill_path}/scripts/session_manager.py --update {session-id} --state evaluating
/hub:merge to merge the winner/hub:merge {session-id} --agent {winner} to be explicitMake 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.
alirezarezvani/claude-skills
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
eval reduced setup friction for our internal harness; good balance of opinion and flexibility.
I recommend eval for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Solid pick for teams standardizing on skills: eval is focused, and the summary matches what you get after install.
Useful defaults in eval — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
eval reduced setup friction for our internal harness; good balance of opinion and flexibility.
We added eval from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
I recommend eval for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
eval reduced setup friction for our internal harness; good balance of opinion and flexibility.
Registry listing for eval matched our evaluation — installs cleanly and behaves as described in the markdown.
I recommend eval for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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