Syllabus

Generates a curated supplementary reading list from any course syllabus using Consensus academic search.

Research & Data / Researchresearch-dataresearch
by AgentVoltv1.0.0Published 1y ago1 install
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Before & after

Same prompt, same agent — the only difference is the skill.

PromptGenerate a supplementary reading list for my database design course using academic papers.
Beforegeneric AI agent
Creating a reading list for a database design course is a good idea to supplement the core material. You'd want to find recent papers on topics like query optimization, schema design, transaction handling, and distributed systems. I could help you search for academic papers and create a reading list, but it would take some time to find relevant sources and summarize them. What level is the course—undergrad, graduate, or professional development? And what areas of database design do you want to focus on?
Afterwith Syllabus
READING LIST GENERATED: Database Design Course

COURSE METADATA INTAKE
  Syllabus: Database Systems (Graduate level)
  Topics parsed: Query optimization, schema design, ACID, indexing, distributed databases
  Student audience: 25 CS Masters students + 5 industry professionals
  Prior courses detected: Basic SQL, discrete math
  Reading level: Rigorous (academic papers expected)
  Year range: 2022-2026 (recent methods valued)

CONSENSUS SEARCH RESULTS: 42 papers identified
  Keyword queries executed:
    - "query optimization machine learning" (4 papers)
    - "schema design normalization" (6 papers)
    - "distributed transaction protocol" (8 papers)
    - "B-tree index variants" (3 papers)
    - "cardinality estimation" (5 papers)
  De-duplicated: 37 unique papers after filtering

FINAL READING LIST: .docx document

  FORMAT: Grouped by module (tied to syllabus learning outcomes)

  MODULE 1: Query Optimization (4 papers)
    Paper 1: "Machine Learning for Cardinality Estimation" (2024)
      Type: Recent methodology
      Summary: Neural networks outperform traditional statistics for join cardinality; 3-page plain-language explainer
      Discussion Q: How would ML-based estimates handle adversarial data distributions?
      Link: clickable Consensus URL

    Paper 2: "Cost Models at Scale" (2023)
      Summary: Cost models break on TPCx-DS; heuristic fixing
      Discussion Q: Design your own cost model for column-oriented databases

  MODULE 2: Distributed Transactions (5 papers)
    Paper 3: "Consensus Without Consensus" (2024)
      Primer: Byzantine agreement requires 3f+1 replicas; alternatives explored
      Discussion Q: Which consistency model suits your application—why trade off?

  MODULE 3: Indexing & Storage (3 papers)

  ... (5 more modules, 25 total papers)

  SUPPLEMENTARY: 2 industry engineering blog posts (Uber, Netflix)
    Context: Apply academic concepts to real-scale systems

DOC FEATURES
  - Clickable Consensus links (single sign-on with .edu email)
  - Discussion questions tied to Bloom's taxonomy (synthesis level)
  - Pre/post-reading quizzes (not included; link to Consensus quiz feature)
  - Plain-language summaries (200 words each, calibrated to audience)
  - Citation metadata (author, year, journal)
  - Difficulty tags: Foundation / Intermediate / Advanced

ESTIMATED READING TIME: 40 hours for full list (assign 10-12 papers for semester)

Deliverable: syllabus_supplement_database_design_2026.docx (ready to share with students)

About this skill


name: syllabus description: Use when Generates a curated supplementary reading list from any course syllabus using Consensus academic search.

Syllabus

Generates a curated supplementary reading list from any course syllabus using Consensus academic search. Grill-me intake (syllabus input format + course audience + year range) plus a grouping forcing-options checkpoint before any search runs — so the reading list matches the course's level and recency need. Parses the syllabus to extract topics and learning outcomes, searches Consensus for recent peer-reviewed papers per topic, and produces a professionally formatted .docx with clickable Consensus links, plain-language summaries calibrated to audience level, and Bloom-higher-order discussion questions tied to course learning goals. Use when the user uploads a syllabus, course outline, or curriculum document and wants supplementary readings (e.g., 'create a reading list from this syllabus', 'find recent papers for my course') — even casual mentions with a syllabus attached should trigger this skill.

What you get

  • Public GitHub repo (alirezarezvani/claude-skills)
  • the syllabus skill folder with SKILL.md. Part of a 337-skill / 30-agent / 70-command install.

Customize your output

  • Fork the repo and adapt the skill's instructions and references to your workflow.

Example output

Activates automatically when your request matches Syllabus; chains with the other skills, agents, and commands in the collection.

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Creators, builders, and teams using Claude Code.

SKILL.md preview

SKILL.md
---
name: syllabus
description: Use when a course syllabus or curriculum document is provided and a supplementary reading list of recent peer-reviewed papers needs to be produced for it.
version: 1.0.0
category: Research & Data / Research
author: AgentVolt
license: proprietary
tags:
  - research-data
  - research
---

# Syllabus

Parses a syllabus into topics and learning outcomes, searches academic literature per topic, and produces a formatted reading list matched to the course's level.

## When to use

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