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Course Builder

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ericrisco
course-builder

Use when turning "I want to teach X" into a defensible course skeleton — measurable outcomes (Bloom + ABCD), assessment that proves each one, sequenced modules, and an outcome×module×assessment matrix — for a workshop, bootcamp, cohort or onboarding track. NOT making one concept land emotionally with story or analogy (that is course-storytelling).

Overview

Publisherericrisco
Repositoryrsc-harness
Skill namecourse-builder
Stars
239
Forks
24
Bundled files
5
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by ericrisco on GitHub. Read the source before you install it.

Installation

Install the Course Builder AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/ericrisco/rsc-harness/tree/main/skills/course-builder
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/ericrisco/rsc-harness.git /tmp/rsc-harness
mkdir -p .claude/skills
cp -r /tmp/rsc-harness/skills/course-builder .claude/skills/course-builder
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Course Builder in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Course Builder on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Course Builder is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Course Builder — The Course Is a Contract

Outcomes promise what the learner can DO. Assessment is the proof. Modules are the build order. If the three don't line up, you have a content dump, not a course. You design them backward — outcomes first, assessment second, content last — and you emit a matrix that makes the alignment checkable.

This skill owns the skeleton: the measurable outcomes, the assessment that certifies each one, the module sequence that builds toward them, and the alignment matrix tying it all together. It does not own the teaching of any one concept.

The litmus. If the question is "what must the learner be able to DO, how do we prove it, and in what order do we build it?" → here. If it's "how do I make THIS concept click and stick?" → ../course-storytelling/SKILL.md.

Route elsewhere when: auditing a finished course for gaps, redundancy, anachronisms with a written report → ../review/SKILL.md; turning a designed module into slides/decks → ../presentations/SKILL.md (+ ../design/SKILL.md for the visual system); sales/landing copy and launch emails → ../marketing/SKILL.md (you design the learning contract, never the pitch); fact-checking or sourcing the subject matter → ../research-ops/SKILL.md (you structure what the user already knows); a repeatable internal procedure with steps but no outcomes/assessment → ../sop-builder/SKILL.md (an SOP tells someone the steps; a course changes what they can DO and proves it).

The grounding gate (read first — STOP if unmet)

You cannot design a course backward without knowing the destination. Do not write a single outcome until you have all three. Why: an outcome scoped for a senior engineer in a 90-minute workshop is wrong for a beginner in a 12-week cohort — same topic, different contract.

  1. WHO — the learner and their current level (absolute beginner? practitioner? what can they already do?).
  2. WHAT transformation — what must they be able to DO at the end that they cannot do now.
  3. FORMAT + constraints — duration, modality (live cohort vs self-paced), group size, prerequisites, certification stakes.

If a 02-DOCS/wiki/teaching/ profile exists (the convention shared with ../course-storytelling/SKILL.md), read it first and reuse it. Otherwise interview in ONE batch — ask all three at once, do not dribble questions. Incomplete grounding = STOP and ask. Depth, the interview script, and scope right-sizing by format → references/grounding-and-scoping.md.

The build workflow (one backward pass)

Run it in order. Do not jump to modules. UbD has exactly three stages in order — desired results → acceptable evidence → learning experiences (Wiggins & McTighe, Understanding by Design). Content designed first is a dump with no destination, and "I already have these slides, build a course around them" inverts the contract: outcomes decide what content survives.

text
Stage 0  Grounding gate         WHO + WHAT transformation + FORMAT. Incomplete → STOP.
Stage 1  Outcomes               3–8 course-level outcomes. Measurable Bloom verb + ABCD.
                                Verb banlist enforced. Right-sized to format.
Stage 2  Assessment             For EACH outcome, the evidence that proves it.
                                Verb-match the outcome. Place formative + summative.
                                Check content validity (covers the breadth of outcomes).
Stage 3  Modules + sequence     One focus per module, prerequisite order. Each module
                                maps to >=1 outcome. No orphans, no unproven outcomes.
Stage 4  Alignment matrix       Emit outcome x module x assessment. The checkable artifact.
Stage 5  QA gate                Run scripts/verify.sh over the curriculum doc. Fix warnings
                                or justify them. Then hand modules to course-storytelling.

Stage 1 — Writing measurable outcomes

Pick the verb at the level the learner must actually operate. Recall ≠ build. The revised taxonomy (Anderson & Krathwohl, 2001) is six ascending levels of verbs, each supplying observable action verbs — a verb you can't observe, you can't assess.

text
Level         What the learner does          Sample verbs
Remember      recall facts                   list, define, name, label, recall
Understand    explain in own words           explain, summarize, classify, compare
Apply         use in a new situation         apply, use, implement, solve, run
Analyze       break apart, find relations    analyze, differentiate, debug, diagram
Evaluate      judge against criteria         evaluate, critique, justify, prioritize, review
Create        produce something new          design, build, compose, construct, ship

Banlist — never "understand", "know", "learn about", "appreciate", "be aware", "be familiar". They name a private mental state, not an observable behavior. Replace with what the learner does: list, explain, build, debug, evaluate, design.

Every outcome is ABCD-complete — a bare verb without a condition and a degree is not yet assessable ("write code" vs "given a failing test, write code that makes it pass"):

text
[Audience]   the learner
[Behavior]   <Bloom verb> + <object>
[Condition]  given <situation / tools / inputs>
[Degree]     <criterion that counts as success>

Bad → Good (the banlist verb is the tell):

markdown
Bad:  Students will understand REST APIs.
Good: Given a spec, the learner builds a REST endpoint that returns the correct
      status code for 3 named error cases (400, 404, 500).

Bad:  Learners will know SQL joins.
Good: Given two tables, the learner writes a query joining them that returns the
      expected rows for 2 of 2 test cases.

Bad:  Participants will appreciate good test design.
Good: Given a 20-line module, the learner writes 3 tests that cover the happy path
      and 2 edge cases, all passing.

Full per-level verb tables, the banlist, more worked ABCD examples, and course-level vs module-level granularity → references/outcomes-and-blooms.md.

Stage 2 — Designing aligned assessment

Assessment is the proof, not an afterthought. For every outcome, ask: what would I have to SEE the learner do to believe they achieved it? — and make that the assessment. Place both kinds: an outcome with no evidence is a promise you never keep, and a course with only the final exam gives the learner no feedback loop.

text
                 Formative (during)              Summative (at the end)
Job              feedback, guide improvement     certify mastery / accountability
Stakes           low / none                      high — the grade, the cert
Examples         quizzes, skill checklists,      capstone project, final exam,
                 drafts, peer review, exit        portfolio, graded build
                 tickets
Bloom fit        Remember/Understand/Apply        Apply/Analyze/Evaluate/Create

Two hard checks:

  • Verb match (constructive alignment, Biggs). The assessment must require the same verb as the outcome. Outcome "build" → assessment is a build, not a multiple-choice quiz. Outcome "evaluate" → assessment asks for a judgement with justification, not recall. If the outcome says "design" and the quiz tests "recall", the assessment proves the wrong thing.
  • Content validity. The set of assessments must cover the breadth of the outcomes — every outcome touched, none over-weighted into a vanity exam. Competency-based design maps this via the matrix and certifies demonstrated mastery, not seat time.

The full formative↔summative menu mapped to Bloom levels, the content-validity / blueprint checklist, and the verb-match rule worked end-to-end → references/assessment-design.md.

Stages 3–4 — Sequencing modules + the alignment matrix

Order modules by prerequisite (you can't build before you can run), give each one focus, and map each to ≥1 outcome. Then emit the matrix — this is the artifact scripts/verify.sh checks.

markdown
| Outcome                          | Module(s)        | Assessment (F=formative, S=summative) |
|----------------------------------|------------------|---------------------------------------|
| O1 build a REST endpoint         | M2, M3           | F: M2 quiz · S: capstone API          |
| O2 debug a failing request       | M4               | F: M4 debug drill · S: capstone API   |
| O3 evaluate an API's error model | M5               | F: M5 peer review · S: capstone rubric|

Read the matrix two ways: down a column finds orphan modules (a module in no row → content the contract never asked for, so cut it or write the outcome it serves); across the outcome list finds unproven outcomes (an outcome with an empty assessment cell → design evidence). A complete matrix has no empty cells.

Decision table (branch only where the flow actually splits)

text
Situation                         Then
Live cohort                       Schedule synchronous formative checkpoints; peer
                                  assessment is cheap and valuable.
Self-paced                        Formative must be self-graded/auto-graded (quizzes,
                                  tests that run); no instructor in the loop.
Short workshop (<= half day)      1-2 outcomes, mostly Apply; one summative artifact, no exam.
Full course / bootcamp            5-8 outcomes spanning up to Create; staged formative +
                                  a capstone summative.
Knowledge outcome                 Assess with explanation/application, not recognition alone.
Skill outcome                     Assess with a performance/build, never a quiz.
Attitude/disposition outcome      Assess with reflection + observed behavior; hardest to
                                  prove — keep few and honest.

Anti-patterns

BadWhy it failsDo instead
Content-first: "build a course around my slides"Inverts the contract; content with no destinationWrite outcomes first; let them decide what content survives
Vanity outcome: "students will understand X"Names a private state, not observable → unassessableUse a measurable Bloom verb in ABCD form
Orphan module: a module mapped to no outcomeContent the contract never asked forCut it, or write the outcome it serves
Unproven outcome: an outcome with no assessmentA promise you never verifyDesign evidence that requires the outcome's verb
Verb mismatch: outcome "design", quiz tests "recall"Proves the wrong thingMake the assessment require the outcome's verb
Summative-only: just a final examNo feedback loop during learningPlace formative checkpoints throughout
Scope creep: bootcamp outcomes in a workshopNone are actually achievable in the timeRight-size outcome count to the format

Stage 5 — QA gate and hand-off

scripts/verify.sh checks your curriculum's STRUCTURE and ALIGNMENT (measurable verbs, the matrix, proven outcomes, formative + summative). Fix its warnings or justify them.

Then hand each module to ../course-storytelling/SKILL.md to make the teaching land (epiphany, named models, grounded analogies). You built what and in what order; that skill makes it stick. The boundary is executable: this skill's verify.sh does not judge whether the teaching lands, and course-storytelling's checks narrative, not structure.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Course Builder AI skill do?

Use when turning "I want to teach X" into a defensible course skeleton — measurable outcomes (Bloom + ABCD), assessment that proves each one, sequenced modules, and an outcome×module×assessment matrix — for a workshop, bootcamp, cohort or onboarding track. NOT making one concept land emotionally with story or analogy (that is course-storytelling).

Why use Course Builder on TypingMind?

Because you install it once and use it with any model. Course Builder is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Course Builder in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ericrisco/rsc-harness/tree/main/skills/course-builder. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Course Builder?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Course Builder?

As many as you like. As long as a model supports skills, you can use Course Builder with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Course Builder AI skill free?

Yes. It is published on GitHub by ericrisco under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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