effort-estimation
Calibrate engineering effort estimates for git commits using a 5-tier rubric. Use whenever you need to translate a diff into an hours estimate for a senior engineer working with or without an AI coding assistant.
A meta-repository for Claude Code users that includes workspace setup (skills, commands, hooks) and an evaluator for your Claude Code configuration
Deep-evaluate a single skill â static analysis, contextual rubric scoring, and A/B redundancy testing. Runs all 3 layers on one skill to determine if it earns its place.
capture-feedback
Capture structured thumbs up/down feedback with context, tags, and optional rubric scores after completing a task.
agent-project-development
This skill should be used when the user asks to "start an LLM project", "design batch pipeline", "evaluate task-model fit", "structure agent project", or mentions pipeline architecture, agent-assisted development, cost estimation, or choosing between LLM and traditional approaches. NOT for evaluating agent quality or building evaluation rubrics (use agent-evaluation), NOT for multi-agent coordination or agent handoffs (use multi-agent-patterns).
autocontext
Iterative strategy generation and evaluation system. Use when the user wants to evaluate agent output quality, run improvement loops, queue tasks for background evaluation, check run status, or discover available scenarios. Provides LLM-based judging with rubric-driven scoring.
article-optimizer
Use when the user wants to improve a specific article under a fixed viral-scoring rubric, iterate on one article to raise its score, or run single-article scoring plus rewrite loops. Best for content optimization experiments where the scoring prompt stays fixed and the article text is the thing being changed.
assessment-design
Evidence-based assessment design with rubrics, feedback strategies, and formative checkpoints. Aligns each assessment to learning objectives using Bloom's taxonomy. Applies Nicol's 7 principles of good feedback practice. Reads from /learning-objectives manifest and extends it with assessment specs. (idstack)
ai-evals
Help users create and run AI evaluations. Use when someone is building evals for LLM products, measuring model quality, creating test cases, designing rubrics, or trying to systematically measure AI output quality.
auto-arena
Automatically evaluate and compare multiple AI models or agents without pre-existing test data. Generates test queries from a task description, collects responses from all target endpoints, auto-generates evaluation rubrics, runs pairwise comparisons via a judge model, and produces win-rate rankings with reports and charts. Supports checkpoint resume, incremental endpoint addition, and judge model hot-swap. Use when the user asks to compare, benchmark, or rank multiple models or agents on a custom task, or run an arena-style evaluation.
multi-model-analysis
Runs 3 AI models in parallel to independently analyze a problem and propose approaches. Compares all proposals against a rubric and selects the best. Can be invoked standalone or called from any agent (issue-resolver, pr-review, etc.).
agents-md-improver
Audit and improve project-rules files (AGENTS.md, CLAUDE.md, .agents/instructions, local overrides) so the agent keeps accurate project context. Use when the user asks to check, audit, review, update, improve, or fix their AGENTS.md or CLAUDE.md, mentions "project rules maintenance" or "agent context optimization", or when the codebase has changed enough that the rules file may be stale. Scans the repository for every rules file, grades each against a quality rubric, outputs a quality report, and applies targeted edits only after user approval.