iblai-vibe-course-create

---

name: iblai-vibe-course-create description: Use this skill when a user asks to create, draft, scaffold, generate, or publish a course on ibl.ai / OpenEdX — including programmatic outlines, unit/component generation, or edits to an AI-generated course. Invoke to drive the ibl.ai Course Creation API end-to-end: create the task, build the course on EdX, generate the outline, draft unit content, review/edit structure, and publish. Do NOT invoke for enrollment, grading, agent configuration, or analytics queries — those are handled by other skills.

/iblai-vibe-course-create

Overview

This skill drives the IBL.AI Course Creation API to programmatically produce courses on an OpenEdX deployment. The API combines AI-generated structure (sections → subsections → units → components) with direct EdX writes, so EdX is the single source of truth for every structural operation. There are no intermediate database models for sections, subsections, units, or components — the API proxies every read/write to EdX.

Two workflow styles are supported:

1. Stepwise (recommended for agents) — create the task, create the empty course on EdX, generate the outline, review, edit structure if needed, draft unit content, verify, publish.
2. Automated pipeline — one call (/start/) runs the entire pipeline as a background Celery task; the agent polls for completion.

Prefer stepwise when the agent (or a user) wants to review or modify intermediate output. Prefer the pipeline when the brief is tight and no human-in-the-loop review is expected.

Configuration

All endpoints are rooted at:

{base_url}/api/ai-mentor/orgs/{org}/users/{user_id}/

Variable	Purpose	Env var
base_url	Server URL (e.g. https://base.manager.iblai.app)	IBLAI_BASE_URL
org	Platform / tenant key	IBLAI_ORG
user_id	Authenticated user's username	IBLAI_USER_ID
api_key	API token used as Authorization: Token <api_key>	IBLAI_API_KEY
mentor_id	(Optional) Agent UUID for memory-aware generation	IBLAI_MENTOR_ID

Every request MUST include:

Authorization: Token {api_key}
Content-Type: application/json   (for JSON bodies)

EdX Block Hierarchy (critical)

Course
└── Section       (chapter)
    └── Subsection    (sequential)
        └── Unit          (vertical)
            └── Component   (html | problem | video)

Each block has an xblock_id EdX locator:

block-v1:{org}+{course}+{run}+type@{type}+block@{slug}

The segment type@<type> identifies the block kind: course, chapter, sequential, vertical, html, problem, video. Parent/child relationships are enforced by the API:

Child	Valid parent
section	course
subsection	section
unit	subsection
html	unit
problem	unit
video	unit

Violating hierarchy returns 400.

What the generated course looks like in edX Studio (course outline and a single unit with components):

Task Lifecycle

A CourseCreationTask stores the generation brief and tracks progress. Its status transitions:

Pending → Scheduled → In Progress → Success | Failed | Cancelled

• Pending after POST /tasks/
• Scheduled after GET /tasks/{id}/start/
• In Progress while the Celery worker runs
• Success once the outline + content + publish have all completed
• Failed on any unrecoverable error (check task detail for error / reason)

Primary Endpoints

All paths below are appended to the base root {base_url}/api/ai-mentor/orgs/{org}/users/{user_id}/.

Task endpoints

Action	Method	Path
Create task	POST	course-creation/tasks/
List tasks	GET	course-creation/tasks/
Task detail / status	GET	course-creation/tasks/{task_id}/
Delete task	DELETE	course-creation/tasks/{task_id}/
Create course on EdX	POST	course-creation/tasks/{task_id}/create-course/
Generate outline (AI)	POST	course-creation/tasks/{task_id}/create-course-outline/
Start full pipeline	GET	course-creation/tasks/{task_id}/start/
Cancel pipeline	GET	course-creation/tasks/{task_id}/cancel/

Course endpoints

Action	Method	Path
List courses	GET	course-creation/course/
Course by task	GET	course-creation/course/?task={task_id}
Course detail	GET	course-creation/course/{course_id}/
Delete course record	DELETE	course-creation/course/{course_id}/
Outline (no component content)	GET	course-creation/course/{course_id}/outline/
Full structure	GET	course-creation/course/{course_id}/full-structure/
Full structure + HTML	GET	course-creation/course/{course_id}/full-structure/?include_content=true
Draft content for all units	GET	course-creation/course/{course_id}/draft-content-for-all-units/
Draft content for one unit	POST	course-creation/course/{course_id}/draft-content-for-unit/
Create xblock	POST	course-creation/course/{course_id}/create-xblock/
Update xblock	POST	course-creation/course/{course_id}/update-xblock/
Delete xblock	POST	course-creation/course/{course_id}/delete-xblock/
Reorder children	POST	course-creation/course/{course_id}/reorder-children/
Publish to EdX	POST	course-creation/course/{course_id}/sync-to-edx/
Student progress	GET	course-creation/course/{course_id}/student-progress/?target_username={username}

File endpoints

Action	Method	Path
Upload file	POST	course-creation/files/ (multipart)
List files	GET	course-creation/files/
Delete file	DELETE	course-creation/files/{id}/

Request Payloads

Create task (POST /course-creation/tasks/)

{
  "name": "Introduction to Python Programming",
  "description": "A beginner-friendly course covering Python fundamentals: variables, primitive data types, control flow (if/else, for, while), functions, error handling, and basic data structures (lists, tuples, dicts, sets). Emphasize hands-on coding with small, runnable exercises. Avoid object-oriented programming — that belongs in a follow-up course.",
  "target_audience": "First-year undergraduates with no prior programming experience. English is a second language for ~40% of the cohort.",
  "publish_course": true,
  "provider": "openai",
  "model": "gpt-4o-mini",
  "desired_number_of_sections": 4
}

Field	Required	Default	Notes
name	Yes	—	Course display name
description	Yes	—	Detailed scope — see Writing the description below
target_audience	Yes	—	User profile
publish_course	No	true	Auto-publish on sync
provider	No	"openai"	LLM provider
model	No	"gpt-4o-mini"	Larger models give better structure; gpt-4o or gpt-4.1 for complex courses
desired_number_of_sections	No	4	3–6 is typical; higher values produce thinner sections

Create xblock (POST /course-creation/course/{course_id}/create-xblock/)

{
  "parent_xblock_id": "<parent locator>",
  "xblock_type": "section | subsection | unit | html | problem | video",
  "display_name": "Readable title",
  "content": "<optional, only for html/problem/video>",
  "position": "last"
}

html content is HTML. problem content is EdX OLX problem XML. video accepts HTML wrapping a video embed.

Update xblock

{
  "xblock_id": "<locator>",
  "display_name": "Optional new title",
  "content": "<optional, only for html/problem>"
}

Omit either field to leave it unchanged. Sending neither returns 400.

Delete xblock

{ "xblock_id": "<locator>" }

Deletions cascade (section → subsections → units → components) and are irreversible.

Reorder children

{
  "xblock_id": "<parent locator>",
  "children": ["<child locator 1>", "<child locator 2>", "..."]
}

You must list every current child. To move a block to a different parent, include it in the new parent's children array — EdX will detach it from its former parent automatically.

Recommended Workflow (Stepwise)

1. POST   /course-creation/tasks/                                      → task_id
2. POST   /course-creation/tasks/{task_id}/create-course/              → course_id, course xblock_id
3. POST   /course-creation/tasks/{task_id}/create-course-outline/      → sections/subsections/units on EdX
4. GET    /course-creation/course/{course_id}/full-structure/          → review generated structure
5. (Optional) create/update/delete/reorder xblocks to fix the outline
6. GET    /course-creation/course/{course_id}/draft-content-for-all-units/  → background content drafting
7. Poll /full-structure/?include_content=true until every unit has components
8. (Optional) update individual components via /update-xblock/
9. POST   /course-creation/course/{course_id}/sync-to-edx/             → publish

Polling for content completion

/draft-content-for-all-units/ is asynchronous. Poll either the task status (GET /tasks/{task_id}/) or re-fetch /full-structure/?include_content=true and check that every unit's components array is non-empty. A typical course (3–5 sections, ~15 units) takes 1–3 minutes.

Back-off pattern: start at 20s, double up to 120s, cap at 5 minutes total wait before surfacing a timeout to the user.

Alternative: Fully Automated Pipeline

1. POST /course-creation/tasks/                → task_id
2. GET  /course-creation/tasks/{task_id}/start/
3. Poll /course-creation/tasks/{task_id}/      → wait for status == "Success"
4. GET  /course-creation/course/?task={task_id}→ resolve course_id

Use this only when the user explicitly asks for a "one-shot" course and does not want to approve the outline.

File Attachments (reference material)

Upload up to 4 files, 20 MB each, via multipart to /course-creation/files/:

POST /course-creation/files/
Content-Type: multipart/form-data

course_creation_task=<task_id>
file=<binary>

Supported content: PDFs, Word docs, Markdown, plain text, slide decks. The AI uses these as reference when generating outline and unit content. Upload files before calling create-course-outline or start — files attached later are ignored.

Best Practices for Writing the Course Brief

The quality of description and target_audience dominates output quality. Before calling POST /tasks/, draft the brief following the template below. If the user's brief is vague, ask clarifying questions first — do not send an underspecified task, because outline regeneration is wasteful.

The 5-field brief template

1. Subject & scope — what is in, and (explicitly) what is out.
2. Learning outcomes — 3–7 verbs-as-outcomes ("by the end, users can build, diagnose, compare…").
3. Target audience — prior knowledge, language level, motivation, typical study time/week.
4. Pedagogy — tutorial vs. project-based vs. assessment-heavy; how much theory vs. practice.
5. Format constraints — number of sections, approximate length, any required topics.

Good vs. weak briefs

Weak (produces generic, thin course):

description: "A course on Python." target_audience: "Beginners."

Strong (produces coherent, teachable course):

description: "A project-based introduction to Python 3.11 focused on data-wrangling for non-engineers. Cover variables, control flow, functions, file I/O, and the csv + json modules. Every section ends with a mini-project using a realistic dataset (airline delays, climate records). Exclude object-oriented programming, decorators, async, and type hints — a follow-up course covers those. Use an encouraging, low-jargon tone." target_audience: "Working analysts (marketing, ops, journalism) with spreadsheet fluency but no programming background. ~4 hours/week, 6 weeks total."

Refinement rules (apply before submitting the task)

• State exclusions explicitly. LLMs over-include. Saying "do not cover OOP" is more effective than listing only what to include.
• Convert vague adjectives into measurable ones. "Beginner" → "assumes high-school algebra, no coding"; "advanced" → "assumes 2+ years professional experience with X".
• Name the artefact type. "Tutorial course with hands-on exercises", "exam-prep course with graded quizzes after every subsection", "capstone course culminating in a single graded project".
• Right-size desired_number_of_sections. 3 = short workshop, 4–5 = term course, 6–8 = bootcamp. Going above 8 usually produces weak sections.
• Pick the model to match complexity. gpt-4o-mini is fine for most intro courses; use gpt-4o / gpt-4.1 when the description mentions formal reasoning, technical depth (graduate-level math, law, medicine), or multiple cross-referenced modules.
• Attach reference files for domain specificity. If the course must align with a textbook, syllabus, curriculum standard, or style guide, upload it. One good PDF outperforms several paragraphs of description.

Clarifying-question checklist

If any of these is unknown after the user's initial brief, ask before creating the task:

1. Who is the user? (background + motivation)
2. What should they be able to do by the end?
3. How long is the course? (weeks, sections, hours)
4. Tutorial, assessment-heavy, or project-based?
5. Any topics to exclude or must-cover topics?
6. Reference material to upload?
7. Publish immediately or keep in draft?

Reviewing AI Output

After the outline (step 3) and again after content drafting (step 6), always pull /full-structure/?include_content=true and verify:

• Every unit has ≥1 component. Empty units are a drafting failure — re-run draft-content-for-unit for that locator.
• Section/subsection titles are non-redundant. LLMs sometimes produce "Introduction" as both section 1 and section 1's first subsection — rename via update-xblock.
• Assessment problems have valid OLX. Open the content field; it must contain a <problem>…</problem> wrapper with at least one response input. Malformed OLX silently fails on publish.
• HTML components don't leak prompts. Look for telltale phrases ("As an AI…", "Here is the lesson…"). If found, re-draft that unit.
• Total length is coherent. A course targeted at 4 hours/week for 6 weeks should have roughly 24 units of substantive content; adjust by adding/removing sections.

Curl Recipes

export BASE=https://base.manager.iblai.app/api/ai-mentor/orgs/$IBLAI_ORG/users/$IBLAI_USER_ID
export AUTH="Authorization: Token $IBLAI_API_KEY"

# 1. Create task
TASK_ID=$(curl -s -X POST "$BASE/course-creation/tasks/" \
  -H "$AUTH" -H "Content-Type: application/json" \
  -d '{
    "name": "Intro to Python for Analysts",
    "description": "Project-based intro ... (full brief)",
    "target_audience": "Working analysts ...",
    "desired_number_of_sections": 5,
    "provider": "openai",
    "model": "gpt-4o"
  }' | jq -r .id)

# 2. Create blank course on EdX
RESP=$(curl -s -X POST "$BASE/course-creation/tasks/$TASK_ID/create-course/" -H "$AUTH")
COURSE_ID=$(echo "$RESP" | jq -r .course_id)
COURSE_XBLOCK=$(echo "$RESP" | jq -r .xblock_id)

# 3. Generate outline (synchronous)
curl -s -X POST "$BASE/course-creation/tasks/$TASK_ID/create-course-outline/" -H "$AUTH"

# 4. Review outline
curl -s "$BASE/course-creation/course/$COURSE_ID/full-structure/" -H "$AUTH" | jq

# 5. Edit: add a bonus section
curl -s -X POST "$BASE/course-creation/course/$COURSE_ID/create-xblock/" \
  -H "$AUTH" -H "Content-Type: application/json" \
  -d "{\"parent_xblock_id\":\"$COURSE_XBLOCK\",\"xblock_type\":\"section\",\"display_name\":\"Bonus: Working with APIs\"}"

# 6. Draft all unit content (async)
curl -s "$BASE/course-creation/course/$COURSE_ID/draft-content-for-all-units/" -H "$AUTH"

# 7. Poll until content appears
until [ "$(curl -s "$BASE/course-creation/course/$COURSE_ID/full-structure/?include_content=true" \
       -H "$AUTH" | jq '[.. | .components? // empty | length] | min')" != "0" ]; do
  sleep 30
done

# 8. Publish
curl -s -X POST "$BASE/course-creation/course/$COURSE_ID/sync-to-edx/" -H "$AUTH"

Error Handling

Symptom	Likely cause	Remedy
create-course returns 200 (not 201)	Course already exists for this task	Re-use returned course_id; a settings re-apply has been scheduled.
create-course-outline returns 400	Outline LLM call failed (rate limit, bad brief)	Check detail; refine description and retry, or switch to bigger model.
create-xblock returns 400 "invalid parent"	Hierarchy violation	Confirm the parent type@ segment matches required parent type.
sync-to-edx returns 400 "no task associated"	Course record lost its task link	Recreate via the task's create-course endpoint.
Task stuck in In Progress for > 15 min	Celery worker stalled or LLM timeout	GET .../cancel/, then restart with GET .../start/.
draft-content-for-unit succeeds but content looks empty	Component created without body	Re-draft; if persistent, update component manually via update-xblock.

Guardrails

• Never publish without review. Always fetch /full-structure/?include_content=true and confirm with the user before calling sync-to-edx — published EdX courses are visible to enrolled students and costly to retract.
• Don't mass-delete. delete-xblock on a section is irreversible and cascades. Confirm target before calling.
• Respect file limits. Max 4 files per task, 20 MB each. Over-limit uploads are rejected with 400.
• Tenant isolation. org and user_id must match the authenticated user's platform and username — cross-tenant requests return 403. Do not attempt to create courses in a tenant different from the caller's.
• Do not leak the API key. Treat api_key as a bearer secret; never echo it in responses, logs, or committed files.
• Background tasks can fail silently to the caller. After /start/ or /draft-content-for-all-units/, always verify downstream (status polling, full-structure fetch) rather than trusting the 202/200 response.
• Ask before picking a larger/more expensive model. gpt-4o/gpt-4.1 cost materially more than gpt-4o-mini. Confirm with the user when complexity warrants it; otherwise default to gpt-4o-mini.