3.6K skills · sorted by relevance
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.
Analyze financial statements, compare budgets with actual results, and forecast revenue and cash flow. Estimate company value using DCF, which discounts future cash flows, and produce reports with scenario comparisons and sensitivity tables for business decisions.
Evaluate whether Google SecOps rules detect threats described in articles or supplied text. Generate simulated attack logs, identify coverage gaps, summarize matching rules, and create rules for uncovered threats, with user approval before adding them.
Review BigQuery capacity, table layouts, storage retention, and SQL to improve cost efficiency. Compare billing options, plan partitioned tables, and evaluate search indexes, stored aggregation results, and BI Engine for suitable workloads.
Design evaluation systems that measure agent results across accuracy, completeness, citations, and tool efficiency. Build repeatable tests, compare changes against a baseline, and define quality checks that catch regressions before deployment and monitor production performance.
Write and review launch prompts for agents working autonomously for hours or days. Define successful deliverables, excluded partial results, stopping rules, worker handoffs, and evidence-based review requirements so completion can be assessed against explicit criteria.
Set up a CARLA driving evaluation environment for Leaderboard 1.0, 2.0, or 2.1. Match the simulator and scenario_runner versions, configure Python paths, and verify that the required components load correctly.
Guides iterative improvement of AI agent prompts, skills, and memory using A-Evolve. It explains how to evaluate agents against benchmark tasks, apply changes, validate results, and track or roll back revisions with Git.
Design and improve prompts for language models, then test their accuracy and consistency. Create reusable templates, JSON output schemas that define response structure, evaluation criteria, and test cases while considering token use and model differences.
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.
Create formula-based Excel valuation workbooks for public companies using discounted cash flow, a method that converts future cash flows into present value. Include per-share estimates, assumption sensitivities, source records, and checks that flag model limitations.
Routes listed-company investment research to specialist skills for earnings analysis, valuation, financial model updates, and portfolio risk reviews. Helps organize evidence, assumptions, and investment theses into reports, models, memos, and monitoring trackers.
Improve an evaluated Codex skill by turning plugin-eval findings into prioritized changes. Refine activation descriptions, shorten SKILL.md, move lengthy details into supporting files, fix structural issues, and compare evaluation results before and after the rewrite.
Review papers, research proposals, and literature reviews with the ScholarEval framework. Assess research questions, methods, analysis, writing, and references, then produce evidence-based feedback with prioritized improvements and optional scores for each relevant dimension.
Plan and configure Nemotron customization workflows using existing steps for data preparation, translation, training, conversion, optimization, and evaluation. Check how outputs connect between stages and produce configuration files suited to the supplied models, data, and hardware.
Run Isaac for Healthcare workflows in a simulator using a learned policy or rule-based controller. Verify completed episodes through the final success summary, visible behavior, and recorded data, with checkpoint compatibility checks when a checkpoint is supplied.
[omh] Choosing between coding agents on evidence: compare executor or agent choices on reproducible tasks using quality, cost, time, tool, and evidence metrics. Use when the user says: agent-evaluation, agent evaluation, agent eval, agent benchmark, executor evaluation, executor benchmark, compare agents, compare codex claude.
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Analyze how a private SaaS company's ARR valuation multiple changed across funding rounds, and attribute the compression or expansion to rate cycles and macro selloffs, growth deceleration, narrative shifts (including an AI premium), competition, and investor demand, benchmarked against private-market medians and peers. Use this skill whenever the user asks about valuation compression, ARR multiples, round-to-round valuation or multiple changes, down rounds, or wants to compare a VC-backed software company's funding rounds. Research the rounds rather than answering from memory.
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Use this skill when a user wants to create, run, or analyze evaluation suites for Microsoft 365 Copilot declarative agents with the public @microsoft/m365-copilot-eval CLI. Trigger on intents such as "evaluate my agent", "test my agent", "run my evals", "create eval prompts", "add multi-turn tests", "tune evaluator thresholds", "why is my agent failing", or "set up eval environment variables".
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A comprehensive auditor for any agent skill — including Manus, OpenClaw/ClawHub, Claude, LobeHub, or custom SKILL.md-based skills. Use this skill whenever a user wants to evaluate, audit, review, score, or quality-check an agent skill before publishing, updating, or deploying. Covers two hard veto gates (structural redlines + research integrity redlines), static quality scoring across 25 criteria (ISO 25010 + OpenSSF + Agent), dynamic test input generation, multi-mode execution testing, multi-layer output evaluation with five specialized category rubrics (Evidence Insight / Protocol Design / Data Analysis / Academic Writing / Other), a Research Veto that applies to all four research categories, human eval viewer generation, actionable P0/P1/P2 optimization recommendations, and automatic skill improvement that outputs a polished, production-ready SKILL.md. Also use whenever a user says "audit my skill", "evaluate my skill", "improve my skill", or wants a corrected version after evaluation.
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Configures and runs LLM evaluation using Promptfoo framework. Use when setting up prompt testing, creating evaluation configs (promptfooconfig.yaml), writing Python custom assertions, implementing llm-rubric for LLM-as-judge, or managing few-shot examples in prompts. Triggers on keywords like "promptfoo", "eval", "LLM evaluation", "prompt testing", or "model comparison".
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Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by EleutherAI, HuggingFace, and major labs. Supports HuggingFace, vLLM, APIs.
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Use when evaluating the model and analyzing results for an Agricultural Systems (AgSy) manuscript so it survives expert systems review — independent model evaluation (observed vs. simulated, fit statistics), sensitivity and uncertainty analysis, and trade-off / scenario analysis across the system. Guides evaluation norms; it does not fabricate results or run the model.
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Use when setting up or repairing a Roblox team project outside Studio: Rojo or Script Sync, Wally packages, Rokit pins, luau-lsp type checking, Selene, StyLua, unit tests in Lune or the engine, GitHub Actions CI on pull requests, Open Cloud place publishing, Luau Execution or API key scopes, merging builders' Team Create map with code from files, Studio plugins, or proving a pipeline works. Also when users say "set up Rojo", "CI for my Roblox game", "run tests in CI", "publish from GitHub", "Script Sync stopped syncing", "Rojo deleted my parts", "wally packages not found", "luau-lsp can't find ReplicatedStorage", "selene errors on types", "the release wiped the map" or "my LocalScript doesn't run".
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