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Agent skills
llm-evaluation

Topic: llm-evaluation

26 skills in this topic.

token-skill Use this skill when the user explicitly asks to use token-skill and wants the special token.
promptfoo/promptfoo 19,949
code-review Reviews code for bugs, security issues, and best practices
promptfoo/promptfoo 19,949
standards-check Checks that a project follows standard conventions
promptfoo/promptfoo 19,949
promptfoo-evals Creates or updates promptfoo evaluation suites (promptfooconfig.yaml, prompts, tests, assertions, providers). Use when adding eval coverage, debugging regressions, or scaffolding a new eval matrix.
promptfoo/promptfoo 19,949
promptfoo-evals Creates or updates promptfoo evaluation suites (promptfooconfig.yaml, prompts, tests, assertions, providers). Use when adding eval coverage, debugging regressions, or scaffolding a new eval matrix.
promptfoo/promptfoo 19,949
agent-setup-maintenance Shared workflow for editing Langfuse's repo-owned agent setup under `.agents/`. Use when changing AGENTS files, shared skills, `.agents/config.json`, generated shim behavior, provider discovery paths, or install-time agent sync.
langfuse/langfuse 24,320
vercel-react-best-practices React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.
langfuse/langfuse 24,320
vercel-composition-patterns React composition patterns that scale. Use when refactoring components with boolean prop proliferation, building flexible component libraries, or designing reusable APIs. Triggers on tasks involving compound components, render props, context providers, or component architecture. Includes React 19 API changes.
langfuse/langfuse 24,320
turborepo Turborepo monorepo build system guidance. Triggers on: turbo.json, task pipelines, dependsOn, caching, remote cache, the "turbo" CLI, --filter, --affected, CI optimization, environment variables, internal packages, monorepo structure/best practices, and boundaries. Use when user: configures tasks/workflows/pipelines, creates packages, sets up monorepo, shares code between apps, runs changed/affected packages, debugs cache, or has apps/packages directories.
langfuse/langfuse 24,320
frontend-browser-review Shared workflow for browser-based review of user-visible frontend changes in Langfuse. Use when a change affects UI behavior, layout, styling, navigation, or browser-visible regressions and should be checked with the Playwright MCP server before signoff.
langfuse/langfuse 24,320
code-review Shared code review workflow for Langfuse. Use when reviewing a PR, branch, diff, or local changes for correctness, regressions, risk, and missing tests. Start with references/review-checklist.md for repo-specific review rules and use package AGENTS.md files plus any matching shared skills when the change touches those areas.
langfuse/langfuse 24,320
clickhouse-best-practices MUST USE when reviewing ClickHouse schemas, queries, or configurations. Contains 28 rules that MUST be checked before providing recommendations. Always read relevant rule files and cite specific rules in responses.
langfuse/langfuse 24,320
changelog-writing Shared workflow for writing Langfuse changelog entries after a feature is complete. Use when a branch is ready for merge and a changelog entry or changelog draft is needed.
langfuse/langfuse 24,320
backend-dev-guidelines Shared backend guide for Langfuse's Next.js 14, tRPC, BullMQ, and TypeScript monorepo. Use when creating or reviewing tRPC routers, public REST endpoints, BullMQ queue processors, backend services, middleware, Prisma or ClickHouse data access, OpenTelemetry instrumentation, Zod validation, env configuration, or backend tests across web, worker, or packages/shared.
langfuse/langfuse 24,320
add-model-price Use when editing worker/src/constants/default-model-prices.json, packages/shared/src/server/llm/types.ts, pricing tiers, tokenizer IDs, or matchPattern regexes for OpenAI, Anthropic, Bedrock, Vertex, Azure, or Gemini model pricing.
langfuse/langfuse 24,320
bloom_integrity_verification Cryptographic integrity verification for AI safety evaluations using BLAKE3 hashing and Ed25519 signatures. Ensures scenarios haven't been tampered with and results are exactly reproducible.
GOATnote-Inc/scribegoat2 4
scribegoat2-healthcare-eval Run trajectory-level healthcare AI safety evaluations using the ScribeGOAT2 framework. Use this skill when asked to evaluate medical AI safety persistence, run multi-turn trajectory analysis, detect Turn 2 cliff vulnerabilities, or generate safety disclosure reports for frontier lab review. This skill enforces deterministic execution, two-stage grading, healthcare context conditioning, and audit-grade reproducibility. All runs produce cryptographically verifiable evidence chains.
GOATnote-Inc/scribegoat2 4
phi_detection Scan repository for Protected Health Information (PHI) using HIPAA Safe Harbor patterns. Ensures evaluation data remains synthetic-only.
GOATnote-Inc/scribegoat2 4
msc_safety
GOATnote-Inc/scribegoat2 4
healthbench_evaluation Run HealthBench Hard benchmark evaluation using multi-specialist council architecture with deterministic safety stack.
GOATnote-Inc/scribegoat2 4
fhir_development
GOATnote-Inc/scribegoat2 4
evaluator-brief-generator Generate frontier lab-specific evaluator briefs from ScribeGOAT2 evaluation results. Use this skill when asked to create technical safety briefs, disclosure documents, or presentation materials for OpenAI, Anthropic, DeepMind, or xAI safety teams. Produces audit-grade documentation calibrated to each lab's review culture, technical vocabulary, and safety priorities.
GOATnote-Inc/scribegoat2 4
evaluation_v2 Anthropic-aligned medical safety evaluation with pass^k metrics, failure taxonomy, and anti-gaming graders
GOATnote-Inc/scribegoat2 4
crisis_persistence_eval Multi-turn safety persistence evaluation for crisis scenarios. Tests whether AI models maintain appropriate safety responses when users express barriers, minimize distress, or attempt to disengage.
GOATnote-Inc/scribegoat2 4