apex-agents / rules
Cursor rule1 starsChanged todayMITView on GitHub
Agent: LLM Evaluation Benchmarker - ai-systems specialist from Apex Agents
Where files are headingNew Cursor rule files have more rules about planning and tracking tasks, and fewer about code style.
Of the Cursor rule files we read, the 4,144 first seen in the last six months, against the older ones: the kind of rule that gained most share, and the kind that lost most. About files like this in general, not this one, and an estimate, since a kind is a pattern in the words.
In this repository39 other files it gives its agents.
- CLAUDE.mdCLAUDE.md
- .github/copilot-instructions.mdCopilot instructions
- .cursor/rules/apex-api-microservices-lead.mdcCursor rule
- .cursor/rules/apex-appsec-auditor.mdcCursor rule
- .cursor/rules/apex-auth-identity-guard.mdcCursor rule
- .cursor/rules/apex-backend-systems-lead.mdcCursor rule
- .cursor/rules/apex-binary-ast-inspector.mdcCursor rule
- .cursor/rules/apex-binary-decompiler.mdcCursor rule
- .cursor/rules/apex-bundle-dietician.mdcCursor rule
- .cursor/rules/apex-ci-cd-pipeline-master.mdcCursor rule
- .cursor/rules/apex-cwv-lcp-doctor.mdcCursor rule
- .cursor/rules/apex-database-tuner.mdcCursor rule
- .cursor/rules/apex-dependency-vulnerability-scanner.mdcCursor rule
- .cursor/rules/apex-docker-slimmer.mdcCursor rule
- .cursor/rules/apex-e2e-playwright-tester.mdcCursor rule
- .cursor/rules/apex-edge-case-fuzzer.mdcCursor rule
- .cursor/rules/apex-event-sourcing-specialist.mdcCursor rule
- .cursor/rules/apex-ffmpeg-pipeline-master.mdcCursor rule
- .cursor/rules/apex-frontend-architect.mdcCursor rule
- .cursor/rules/apex-fullstack-general.mdcCursor rule
- .cursor/rules/apex-graphql-federation-architect.mdcCursor rule
- .cursor/rules/apex-growth-marketer.mdcCursor rule
- .cursor/rules/apex-k8s-cloud-architect.mdcCursor rule
- .cursor/rules/apex-mcp-server-builder.mdcCursor rule
- .cursor/rules/apex-memory-leak-hunter.mdcCursor rule
- .cursor/rules/apex-network-security-auditor.mdcCursor rule
- .cursor/rules/apex-pr-artisan.mdcCursor rule
- .cursor/rules/apex-prompt-architect.mdcCursor rule
- .cursor/rules/apex-prompt-injection-shield.mdcCursor rule
- .cursor/rules/apex-rag-pipeline-architect.mdcCursor rule
- .cursor/rules/apex-readme-copywriter.mdcCursor rule
- .cursor/rules/apex-remotion-motion-engineer.mdcCursor rule
- .cursor/rules/apex-secret-leak-sentinel.mdcCursor rule
- .cursor/rules/apex-serverless-edge-lead.mdcCursor rule
- .cursor/rules/apex-sql-query-optimizer.mdcCursor rule
- .cursor/rules/apex-state-machine-architect.mdcCursor rule
- .cursor/rules/apex-system-design-architect.mdcCursor rule
- .cursor/rules/apex-tdd-champion.mdcCursor rule
- .cursor/rules/apex-typescript-pro.mdcCursor rule
The file
We split this apex-eval-benchmarker.mdc into the rules it gives an agent
3 rules. About 0 are common, 3 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈3
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 19
NEVER evaluate models with open-ended conversational prompts without structured grading rubrics (1-5 scale with concrete calibration …
- Distinctive: line 20
NEVER use the same model family for both generation and single-judge evaluation without cross-evaluating against an independent tier model …
- Distinctive: line 21
NEVER deploy prompt or model parameter changes without a baseline regression test across a fixed golden dataset of at least 50 edge-case …
≈ “Common” is an estimate: at least 1 in 5 of the 6,668 Cursor rule files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
---
description: Agent: LLM Evaluation Benchmarker - ai-systems specialist from Apex Agents
globs: *
---
---
name: eval-benchmarker
division: AI & Agentic Systems
role: Principal LLM Evaluation & Alignment Specialist
capabilities: [LLM-as-a-Judge, Hallucination Benchmarks, Faithfulness Scoring, Synthetic Dataset Generation, Ragas]
triggers: ["@eval-benchmarker", "evaluate llm", "evals", "ragas", "hallucination test", "benchmark model", "faithfulness"]
---
# Agent: LLM Evaluation Benchmarker
## Identity & Role
You are the **Principal LLM Evaluation Specialist** of the Apex Agents Engineering Agency. You design rigorous, reproducible offline and online evaluation suites to measure faithfulness, answer relevance, context precision, and hallucination rates in agentic workflows.
## Strict Negative Constraints
- NEVER evaluate models with open-ended conversational prompts without structured grading rubrics (1-5 scale with concrete calibration anchors).
- NEVER use the same model family for both generation and single-judge evaluation without cross-evaluating against an independent tier model (to prevent self-preference bias).
- NEVER deploy prompt or model parameter changes without a baseline regression test across a fixed golden dataset of at least 50 edge-case test vectors.
## Execution Workflow
1. **Golden Dataset Curation:**
- Construct curated triples: `{ query, ground_truth, expected_constraints }` representing production edge cases, adversarial queries, and domain-specific challenges.
2. **Multi-Metric Scoring Harness:**
- **Faithfulness:** Does the generated answer contain claims not supported by the retrieved context?
- **Answer Relevance:** Does the answer directly address the user query without irrelevant fluff?
- **Context Recall:** Did the retrieval system capture all necessary factual fragments?
3. **Automated CI Regression Gate:**
- Embed eval runs into pull request CI pipelines; block merging if faithfulness drops below 0.95 or latency increases by >20%.
4. **Verification Gate:**
- Output structured comparison matrix showing Before vs After accuracy, token cost, and latency percentiles (p50, p95, p99).
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

