/cs-syllabus

disable-model-invocation: true
name: "cs-syllabus"
description: "/cs-syllabus <syllabus-file-or-paste> — Generate curated supplementary reading list from any course syllabus. 3-Q grill-me (input format + audience + year range) + grouping checkpoint → Consensus searches per section with applied-domain weaving → .docx via bundled JS script with audience-calibrated summaries + Bloom higher-order discussion questions."

/cs-syllabus — Course Supplementary Reading List

**Command:** `/cs-syllabus <syllabus-file-or-paste>`

The `cs-syllabus` persona produces a `.docx` reading list of recent peer-reviewed research per course section.

When to Run

• Adding supplementary readings to an existing course
• Updating a syllabus with current research
• Checking what's recent in your field for course planning
• Even casual mentions when a syllabus is attached

Forcing Intake (3 Questions, One at a Time)

| Q | Asks | Notes | |---|---|---| | Q1 | Syllabus input: file path / pasted content / image | refuses missing syllabus | | Q2 | Course audience: undergrad-intro / undergrad-advanced / grad-masters / grad-doctoral / professional / mixed | drives summary jargon + discussion-question complexity | | Q3 | Year range: 1 / 2 / 5 years | drives `year_min` on every Consensus search; default 2 |

What You Get

reading_list_<course-slug>_<YYYY-MM-DD>.docx

Structure:
- Title page (course name, subtitle, date)
- Introduction (with Consensus app link)
- Course Learning Outcomes (boxed section)
- Sections (6-12, from grouping):
    Each section = numbered papers, each with:
      - Clickable hyperlinked title
      - Author / journal / year (italic)
      - Summary (plain language, audience-calibrated)
      - Discussion Question (Bloom higher-order, tied to learning outcome)
- Footer (generation metadata)

Grouping Checkpoint (After Phase 2)

After parsing the syllabus, the skill **halts** with a forcing-options prompt:

Proposed sections: [list with item counts]. Pick one:
  1. Looks good — proceed with these sections
  2. Merge sections [X] and [Y]
  3. Split section [X] into two
  4. Add a section for [topic]
  5. Remove section [X]

This is the last cheap moment to correct course before search budget is consumed. **Refuses to start Phase 3 without explicit user choice.**

Discipline

• **One intake Q per turn.** Never bundle.
• **Halt at grouping checkpoint.** No Phase 3 without user.
• **Sequential Consensus.** 1 q/sec.
• **Applied-domain weaving** — search "enzyme kinetics food processing" not just "enzyme kinetics". Boosts paper relevance dramatically.
• **Audience-calibrated summaries** — undergrad-intro defines every term; grad-doctoral assumes technical fluency.
• **Bloom higher-order discussion questions** — apply / analyze / evaluate. NOT recall ("what did the authors find?").
• **Source discipline** — only Consensus session results. Training knowledge labeled.
• **Three-count tracking** — sent / received / cited.
• **Bundled JS DOCX generator** — don't inline 300 lines of layout code.

Quality Bars

Summary

| ✅ Good | ❌ Bad | |---|---| | "This review maps how different diets — Mediterranean, Nordic, vegetarian — reshape the types of fat molecules circulating in your blood, with implications for heart disease risk." | "This paper reviews lipidomic profiles across dietary interventions and their cardiometabolic implications." (Too jargon-heavy) |

Discussion Question

| ✅ Good | ❌ Bad | |---|---| | "If dietary fat quality can reshape your lipoprotein lipidome, what does this suggest about the biochemical basis for dietary guidelines recommending unsaturated over saturated fats?" | "What did the authors find?" (Just recall) |

Workflow

# Phase 0 intake (Q1-Q3)
python ${CLAUDE_PLUGIN_ROOT}/skills/syllabus/scripts/citation_tracker.py --action start --session NAME

# Phase 1 parse (PDF/DOCX/text/image-appropriate reader)
# Phase 2 group + CHECKPOINT (wait for user)
python ${CLAUDE_PLUGIN_ROOT}/skills/syllabus/scripts/topic_grouper.py --topics-file /tmp/topics.json

# Phase 3 search (sequential Consensus 1 q/sec, applied-domain weaving)
# Phase 4 write summaries + discussion questions
python ${CLAUDE_PLUGIN_ROOT}/skills/syllabus/scripts/discussion_question_validator.py --questions-file /tmp/qs.json

# Phase 5 generate .docx via bundled script
node ../skills/syllabus/scripts/generate_reading_list.js \
  --input /tmp/data.json \
  --output /path/to/reading_list_<course>_<date>.docx

# Phase 6 deliver
python ${CLAUDE_PLUGIN_ROOT}/skills/syllabus/scripts/citation_tracker.py --action close --session NAME

Trigger Phrases

• "syllabus reading list"
• "find papers for my course"
• "create a reading list from this syllabus"
• "recent research for my class"
• "supplementary readings"
• "find journal articles for these topics"
• "what recent papers cover this material"
• "any new research on these course topics"
• "update my syllabus with recent papers"
• Casual mentions when syllabus is attached

Anti-Patterns Rejected

• Parallelizing Consensus calls (rate limit)
• Searching topics without applied-domain angle (poor relevance)
• Padding sections with fabricated entries when Consensus thin
• Generic discussion questions ("What did the authors find?")
• Jargon-heavy summaries unsuitable for course audience
• Skipping group-and-confirm step
• Truncating Consensus URLs in hyperlinks
• Inlining 300 lines of docx-generation JavaScript in skill body

Related

• Agent: [`cs-syllabus`](../agents/cs-syllabus.md)
• Skill: [`syllabus`](../skills/syllabus/SKILL.md)
• Source spec: `megaprompts/10-syllabus-megaprompt.md` (maintainer-local draft spec — gitignored, not in the public repo)
• Siblings: `/cs-litreview`, `/cs-grants`, `/cs-patent`, `/cs-dossier`, `/cs-pulse`

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**Version:** 1.0.0 **Source:** Path-B direct conversion of `megaprompts/10-syllabus-megaprompt.md`