Goodeye-Labs

Goodeye-Labs/truesight-mcp-skills

Plugin Claude Code

Plugin marketplace listing 1 plugin: truesight.

Goodeye-Labs/truesight-mcp-skills

Plugin Claude Code

MCP server and agent skills for the Truesight AI quality platform. Score inputs, build evaluations, analyze errors, and review results through natural language.

Goodeye-Labs/truesight-mcp-skills

MCP server Claude CodeCodexCursor +2

MCP server "truesight", hosted remotely at api.truesight.goodeyelabs.com, as configured in Goodeye-Labs/truesight-mcp-skills.

Goodeye-Labs/truesight-mcp-skills

Skill Claude CodeCodex

Fastest route to a deployed live evaluation using a pre-built Truesight template. Use when the user wants a quick start without building judgment configs from scratch.

Goodeye-Labs/truesight-mcp-skills

Skill Claude CodeCodex

Build a custom web interface for trace annotation and review. Use when users need a bespoke review surface for their workflow.

Goodeye-Labs/truesight-mcp-skills

Skill Claude CodeCodex

Scope what quality should be measured, convert it into one or more actionable binary evaluations, deploy those evaluations through Truesight MCP, and generate a companion skill that applies them correctly. Use when a user wants to create new evals, quality checks, guardrails, or pass/fail criteria for AI outputs.

Goodeye-Labs/truesight-mcp-skills

Skill Claude CodeCodex

Systematically identify and categorize failure modes in evaluated traces using Truesight datasets and error-analysis tools. Use when quality issues are unclear, after major pipeline changes, or when incidents indicate drift.

Goodeye-Labs/truesight-mcp-skills

Skill Claude CodeCodex

Audit an existing evaluation workflow and produce severity-ranked findings with concrete next actions. Use when inheriting an eval setup, diagnosing quality regressions, or checking LLM evaluation process maturity.

Goodeye-Labs/truesight-mcp-skills

Skill Claude CodeCodex

Evaluate one or more traces against an existing Truesight live evaluation. Use when a deployed live evaluation already exists and the user wants run outputs with optional handoff to review and promotion.

Goodeye-Labs/truesight-mcp-skills

Skill Claude CodeCodex

Generate synthetic test data for LLM evaluations using dimension-based tuple expansion. Use when the user needs synthetic traces, test cases, eval datasets, or when create-evaluation needs synthetic fallback data.

Goodeye-Labs/truesight-mcp-skills

Skill Claude CodeCodex

Judge flagged trace outputs and promote judged items back to datasets. Use when an evaluation run requires human judgment or when review queue items need to be judged for promotion into the dataset.

Goodeye-Labs/truesight-mcp-skills

Skill Claude CodeCodex

Orchestrator for Truesight MCP skills. Use this when the user needs help choosing the right Truesight workflow or when intent is ambiguous across LLM evaluate, error analysis, review, templates, or evaluation creation.

Goodeye-Labs/goodeye-cli

Skill Claude CodeCodex

Version bump and optional PyPI release for goodeye-cli. Use when bumping the version, cutting a release, or pushing a git tag to trigger PyPI publish.

Goodeye-Labs/goodeye-cli

MCP server Claude CodeCodexCursor +2

Design, save, and run outcome-aligned AI workflows and verifiers, with reliable image output. Remote server at mcp.goodeye.dev.