“evaluate”的搜索结果

Evaluate a machine learning GPU cluster during a cloud trial or acceptance test. Measure GPU computing performance, communication within and between machines, and local and shared storage, then compile the results into a dated Markdown report.

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• Evaluating trial GPU machines from a cloud provider
• Running acceptance tests for a GPU cluster
• Measuring GPU performance under load from neighboring GPUs

Evaluate a local Codex skill for metadata, structure, reference links, size, and helper code quality. Get prioritized fixes and explanations, with benchmark setup for deeper analysis and measurement recommendations when observed usage is available.

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• Audit a local Codex skill
• Understand evaluation scores and findings
• Prioritize skill improvements

Evaluate local Codex skills and plugins, understand their scores, and identify fixes to prioritize. Get explanations of token estimates, set up benchmark scenarios, and prepare workflows for measuring actual token usage.

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• Evaluate a local skill or plugin
• Understand scores and prioritize fixes
• Explain token estimates and prepare actual measurements

Evaluate a local Codex plugin to understand its scores and identify what to fix first. Review stronger and weaker skills, prepare benchmark runs, compare saved results across versions, and produce a readable report.

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• Review an entire plugin before comparing versions
• Understand scores and prioritize fixes
• Compare strengths and weaknesses across included skills

Iterative rubric-based evaluation and self-improvement loop. Builds a scoring rubric interactively, evaluates an artifact with multiple models in parallel (Codex, Gemini, Claude), then autonomously improves the artifact one criterion at a time until a score threshold is met or circuit breaker fires. "/rulph", "rubric evaluate", "rubric score", "multi-model evaluate", "score and improve", "evaluate and iterate", "grade this", "루브릭 루프", "채점 루프", "자율 개선", "개선 루프", "루브릭 평가"

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Plan, evaluate, and produce paid ad campaigns across platforms (Meta, Google, LinkedIn, TikTok, Microsoft, Pinterest, Snapchat, Amazon, X). Evaluate existing ads, map funnel stages (TOF/MOF/BOF), produce ad variants per platform with policy compliance, output ready-to-upload copy. Use when "ad campaign", "create ads", "run ads for", "paid campaign", "media plan", "launch ads", "Meta campaign", "Google Ads campaign", "multi-platform ads", "evaluate my ads", "how are my ads doing", "audit my ads", "ad performance", "analyze ads", or any request to evaluate existing or create new advertising.

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Rates a project with Jevaluate and keeps the rating private, with the same rubric and checks, a private library that export refuses, and nothing pushed or published. Use when a rating must stay private, such as "/jevaluate-private" followed by a link or folder, "rate this privately", "evaluate my own project", a client's or employer's code, a design doc or a local folder that isn't public, or any rating that must not reach the public ratings or the repo.

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Train and evaluate models that estimate human poses from WiFi signals. Compare results against a model that always predicts the average pose, check for data leakage, and report measured improvements with clear evaluation conditions.

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• Train a pose model using camera labels
• Evaluate pose estimation without a camera at inference
• Compare accuracy against an average-pose baseline

Build and evaluate time-to-event models with scikit-survival, including records whose events remain unobserved when follow-up ends. Keep preprocessing separate from evaluation data, compare models, predict survival probabilities, and summarize censoring-aware metrics.

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• Validate event indicators and follow-up times in local CSV data
• Train survival models without leaking evaluation data
• Evaluate survival probabilities with censoring-aware metrics

Build and evaluate single-agent reinforcement learning experiments, where an agent learns actions from rewards. Use Stable Baselines3 to choose algorithms, create environments, monitor training, and save models with the normalization data needed for consistent evaluation.

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• Set up a reproducible reinforcement learning experiment
• Create and validate a custom Gymnasium environment
• Monitor training and save checkpoints

Evaluate language models with over 100 benchmark tests covering knowledge, math, coding, safety, and image understanding. Configure runs on Docker, Slurm computing clusters, or cloud infrastructure, compare models, and export results.

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• Compare models using the same benchmark tests
• Run large evaluations on a Slurm cluster
• Evaluate safety and image understanding

Evaluate vendor proposals, renewals, or competing offers by comparing costs, risks, performance, and business fit. Produce a structured review with total ownership costs, a recommendation, and concrete points for contract negotiations.

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• Evaluate a new vendor proposal
• Decide whether to renew or replace a vendor
• Compare two vendors side by side

Evaluate a proposed software change before implementation. It examines project files and official sources, compares options and trade-offs, and builds a concrete plan covering affected systems, effort, risks, and unresolved questions for the user's approval.

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• Discuss a major change before implementation
• Compare migration or configuration options
• Estimate affected files and implementation effort

Design a RAG pipeline that retrieves documents to support AI answers. Analyze document splitting, select embedding models and vector databases, and evaluate retrieval quality using test queries and known relevant documents.

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• Designing a RAG system for internal documents
• Choosing how to split documents for search
• Comparing embedding models and vector databases

Evaluate a driving agent on CARLA Leaderboard routes after checking the required setup. Select routes and repetitions, save driving scores and infractions, record runs, and resume interrupted evaluations from a saved checkpoint.

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• Smoke-test a Leaderboard setup on one route
• Evaluate an agent across validation routes
• Resume an evaluation after a crash

Evaluate code generation models with HumanEval, MBPP, and multilingual benchmarks. The skill guides test execution and model comparisons using pass@k, an estimate of whether at least one of k generated solutions passes the tests.

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• Compare code generation models on shared benchmarks
• Measure code generation performance across languages
• Evaluate instruction-tuned coding models

Evaluate staged Git changes before committing using an LLM, a language model acting as a reviewer. Receive correctness, completeness, and safety scores, plus issues by severity, to decide whether to fix, commit, or stop.

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• Evaluate significant changes before committing
• Review changes in unfamiliar code
• Check changes to security-sensitive code

Evaluate AI agents by preparing test cases, scoring responses and tool use, investigating failures, and comparing results after changes. Supports built-in checks and custom scoring rules, including evaluations performed by a language model.

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• Create test cases for an AI agent
• Check response quality and tool use
• Investigate failed evaluation cases

Evaluate pages and components for usability, visual hierarchy, clarity, and emotional tone. Produce a score out of 40, examine tasks from relevant user perspectives, and prioritize concrete improvements around the user's goals and constraints.

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• Reviewing a page or component before release
• Prioritizing fixes for confusing navigation and information overload
• Checking tasks from first-time and experienced user perspectives

Strategic research companion — brainstorm, evaluate, and decide on research directions. TRIGGER when the user wants to brainstorm research, evaluate research ideas, do project triage, or explore a problem space. Orchestrates brainstormer, idea-critic, and research-strategist agents through a 6-phase pipeline: Seed → Diverge → Evaluate → Deepen → Frame → Decide. Includes Carlini's conclusion-first test.

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RFP creation, distribution, evaluation, and shortlisting for in-house legal teams selecting outside counsel. Draft a new legal services RFP from scratch, evaluate firm responses against weighted criteria, produce a shortlist recommendation with selection rationale for GC sign-off, or design the end-to-end RFP process from scratch. Trigger on: 'draft an RFP', 'run an RFP process', 'evaluate firm responses', 'score the RFP submissions', 'which firms should we shortlist', 'selection recommendation', 'how do we run a panel RFP', 'RFP for legal services', 'pitch process', 'firm selection process', 'compare the proposals', 'we're reviewing our panel', 'panel refresh RFP', 'we need to go to market', 'request for proposal', 'evaluate the pitches', 'which firm won the RFP', 'write a legal RFP', 'RFP evaluation criteria', 'design the RFP process', 'how do we weight the criteria'.

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Implement evaluated findings: load code-style rules, apply the accepted fixes, then close out the change with full or quick QA. Use after $evaluate-findings has tagged findings and they need to be implemented, or when the user asks to "resolve findings", "apply evaluated findings", or "dispatch findings to implementation".

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Implement evaluated findings: load code-style rules, apply the accepted fixes, then close out the change with full or quick QA. Use after /evaluate-findings has tagged findings and they need to be implemented, or when the user asks to "resolve findings", "apply evaluated findings", or "dispatch findings to implementation".

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Critically assess external feedback (code reviews, AI reviewers, PR comments) and decide which suggestions to apply using adversarial verification. Use when the user asks to "evaluate findings", "assess review comments", "triage review feedback", "evaluate review output", or "filter false positives".

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