rubric plugins

by Mohit Aggarwal

Educator skills: Lesson Plan, Rubric Builder, Quiz Generator, Parent Communication, IEP Goal Support, Student Feedback. Build standards-aligned lessons with differentiation, fair grading rubrics, quizzes with answer keys, warm parent messages, measurable SMART IEP goals, and constructive feedback that tells students how to improve. A drafting aid for teachers — not legal advice.

by Jiaxing Yu

Codex-native Teaching Skills suite for university professors: course design, lessons, assessment, mentoring, submission auditing, reflection, and 8 extension skills.

by Alberto Leal

Extract and document writing styles from source texts into reusable style guides. Use when analyzing an author's voice, creating a style guide from a book or document, capturing writing patterns for replication, or building a writing style rubric.

by Alberto Leal

Write content using stored writing styles from the writing-styles/ collection. Use when the user wants to write in a specific voice, apply a stored style, list available writing styles, or evaluate text against a style rubric.

by Melodic Software

LLM evaluation methodology and eval-suite design, based on Anthropic's evaluation guidance. /evals:methodology answers questions on success criteria, eval design, and grading. /evals:design interviews for measurable success criteria and scaffolds a graded eval suite for an LLM app or Claude Code skill. /evals:plugin-eval preflights the CLI and target, prices a suite before running, and reads the with-versus-without delta. /evals:validate checks case files with no model call.

by Jeremy Terhune

Design and evaluate instructional content with GUIDE — 10 learning-science archetypes and 60 theory-grounded dimensions mapped to the ADDIE lifecycle. Bundles the GUIDE skill plus a 13-agent ADDIE pipeline (router, 10 archetype specialists, curriculum-alignment gate, synthesizer, reviser) for end-to-end design and evaluation.

Доменный плагин AI/ML-исследователя: RL-среды оценки знания концептов (реестр Ариадны E1_define…E8_plan_experiment). Одна среда = задача-формат (определение, формула, классификация, извлечение карточки, связь, контраст, починка карточки, план эксперимента) + verifier-паттерн (детерминированный код) + опциональный LLM-судья по рубрике и веса judge_weight/verifiable_weight, плюс k_samples — число сэмплов на задачу для RL-дисперсии. Разбирает контракт verifier'а (float 0..1 либо dict {gate, components, evidence}), шкалу судьи 0–4 и её перевод в task_score, калибровку судьи (Spearman ρ ≥ 0.75), известные дефекты verifier'ов (E4 константный 0.5, E8 заглушка 0.0, E5-адаптер from/to→source/target) и как собрать из сред честный сигнал для оценки знания концептов, arena-лигу и reward для RL. Триггеры: «RL-среда», «RL-окружение», «E1_define», «E4_extract», «verifier», «верификатор ответа», «judge_weight», «verifiable_weight», «k_samples», «оценка знания концептов», «рубрика судьи», «LLM-судья», «arena лига», «task_score», «калибровка судьи», «reward-сигнал для RL».

by Anselm Hahn

Reworks a Claude Code plugin to the official Anthropic standard the way a failed part is reworked to spec: a calibrated review of its skills, agents, commands, workflow prompts, hooks, scripts, report viewers, manifest, README and the developer docs that wire it in, then a tier-gated fix pass that applies only what a haiku or sonnet remediator can do without judgement, blind-verified, under a PreToolUse hook that denies every edit outside the fix lock. Nothing is graded until the finder passes a sealed hold-out; opus- and human-tier findings are surfaced, never auto-applied. Read-only except analysis/nacharbeit/ and, during a fix pass, the files its lock names.

by Thong Phan

Turn committed, project-sized outcomes into evidence-backed rolling-wave plans after an explicit five-question intake.

by Fars Saadeldin

Reverse-engineer a marking rubric into the handful of criteria that actually move the mark, then audit a draft against them.

by Tamas Mezei

Supplies criteria groups B–G for grading a Markdown prompt that steers Claude — model-specific prompting, general prompting, hallucination guards, output consistency, injection defenses, and prompt-leak defenses. Returns the criteria; the caller scores against them.