| name | curriculum-writer |
| description | Write KubeDojo curriculum modules. For ANY agent acting as the T0 author (cursor composer-2.5, codex, deepseek-v4-pro, claude headless). Use when creating modules, writing theory, exercises, quizzes. Triggers on "write module", "create module", "new module", "rewrite module". |
| last_calibrated | "2026-05-24T00:00:00.000Z" |
Curriculum Writer Skill
Author skill for new KubeDojo curriculum modules. Ensures consistent structure, tone, and quality across all educational content. Agent-agnostic — applies whether you are cursor composer-2.5, codex, deepseek-v4-pro, claude headless, or agy.
When to Use
- Creating new curriculum modules
- Expanding existing module content
- Writing theory sections, exercises, or quizzes
- Rewriting modules failing the verifier (
median_wpp < 28 etc.) or the 7-dimension rubric
Author lanes (who writes what — 2026-05-24 snapshot)
T0 primary author is CODEX-OR-CURSOR depending on codex weekly-cap state. Quality-best lane wins ([[feedback_quality_over_budget_in_role_allocation]]). When codex cap is healthy → codex gpt-5.5 is the primary writer; cursor composer-2.5 takes over during throttle / thin-cap windows. The reviewer side (Decision Card C symmetric routing) is unchanged regardless of who authored.
| Lane | Agent | When | Notes |
|---|
| T0 primary author (codex cap healthy) | codex (.venv/bin/python scripts/dispatch_smart.py draft --agent codex --mode danger --worktree X) | Default when codex weekly cap is not thin | Default model is gpt-5.3-codex-spark (per TASK_CLASSES["draft"]); override with --model gpt-5.5 for top-tier first-pass quality at higher per-call cost. Codex --search auto-enables via task-class codex_search=True (env export KUBEDOJO_CODEX_SEARCH=1 to the codex CLI); do NOT pass --search to dispatch_smart.py. Quality-best lane: stronger on factual / version / runnability accuracy than composer-2.5 (session 52 cursor-authored tooling/api/docs cohort measured 4/7 = 57% first-pass NEEDS_CHANGES — proxy signal, no curriculum-T0 cohort at scale yet). See [[feedback_codex_writer_needs_search]]. |
| T0 primary author (codex cap thin / throttle) | composer-2.5 (cursor-agent CLI OR cursor IDE) | Codex weekly cap throttled | Pair with codex R1 — composer-2.5 verifier passes ≠ runnability ([[feedback_composer_2_5_viable_for_t0_content]]). |
| Bug fixer (any cap state) | composer-2.5 (cursor-agent CLI OR cursor IDE) | Bug fix PRs — separate lane from T0 author | Proven 3/3 first-commit on session 51 bug PRs per [[feedback_cursor_is_strong_bug_fixer]]. Use regardless of codex cap state. |
| T0 off-load (spread author load) | deepseek-v4-pro | When 3+ codex authors already in-flight (parallel-cap discipline per [[feedback_parallel_rewrite_cap_three]]) | Pair with vigilant code-domain reviewer; hallucinates rule attribution ([[feedback_deepseek_v4_pro_viable_for_t0_content]]). |
| Drafter (needs Claude expansion) | agy (gemini-3.1-pro-high) | When deeper structure scoped but final-form latency cheap | Outputs 350-400 lines, expand to 700-900+ |
| Source-fidelity expansion | claude opus (post-2026-06-15 inline) OR codex (pre, danger mode) | Strict-source rewrites | [[feedback_codex_default_prose_expander]] |
Important: every author lane is bound by the same density gates and 7-dim rubric below. The agent identity changes the dispatch wrapper, not the content contract.
Author contract (every lane)
Every authored/rewritten module MUST satisfy ALL of:
- Density gates (deterministic, enforced by
scripts/quality/verify_module.py):
median_wpp ≥ 28 (median words per paragraph)mean_wpp ≥ 30 (mean words per paragraph)short-para-rate ≤ 20% (paragraphs under 18 words — see verify_module.py:799)
- Frontmatter:
title: + sidebar.order: (mandatory per [[.claude/rules/new-content-checklist]]).
- Slug: if filename has dots (e.g.
module-1.1-foo.md), explicit slug: to preserve them.
- Parent index.md: add module to section's index table.
- Internal links: slug format (
module-foo/), never .md extension.
- Build green:
npm run build with 0 warnings.
- Health check:
.venv/bin/python scripts/check_site_health.py returns 0 errors.
- Citation discipline: every external fact verified, unverified removed ([[feedback_citation_verify_or_remove]]).
- No personal framing: no interview/job/role narrative ([[feedback_no_personal_framing]]).
- Pedagogy over listicles: modules must TEACH, not dump facts ([[feedback_teaching_not_listicles]]).
The 7-dimension rubric (Learning Outcomes, Scaffolding, Active Learning, Real-World Connection, Assessment Alignment, Cognitive Load, Engagement) is the reviewer's contract — see [[module-quality-reviewer]]. Author for ≥3.5 average with no dimension at 1.
Track-Specific Guidelines
KubeDojo has three tracks with different focuses:
Kubernetes Certifications (docs/k8s/)
- Exam-focused content
- Aligned with official CNCF curriculum
- Time-boxed complexity (exam speed matters)
- kubectl commands emphasized
Prerequisites (docs/prerequisites/)
- Beginner-friendly fundamentals
- No assumed knowledge
- Build foundation for certifications
Platform Engineering (docs/platform/)
- Post-certification, practitioner content
- Theory-first approach (principles over tools)
- Three layers: Foundations → Disciplines → Toolkits
Platform Track Structure
Platform modules have three tiers:
Foundations (docs/platform/foundations/)
Timeless theory that doesn't change:
- Systems Thinking
- Reliability Engineering
- Observability Theory
- Security Principles
- Distributed Systems
Disciplines (docs/platform/disciplines/)
Applied practices and mental models:
- SRE
- Platform Engineering
- GitOps
- DevSecOps
- MLOps
Toolkits (docs/platform/toolkits/)
Current tools (will evolve over time):
- Observability (Prometheus, OTel, Grafana)
- GitOps Tools (ArgoCD, Flux)
- Security Tools (Vault, OPA, Falco)
- Platforms (Backstage, Crossplane)
- ML Platforms (Kubeflow, MLflow)
Module Template (Certification Track)
# Module X.Y: [Topic Name]
> **Complexity**: `[QUICK]` | `[MEDIUM]` | `[COMPLEX]`
>
> **Time to Complete**: X-Y minutes
>
> **Prerequisites**: [List required modules or knowledge]
---
## Why This Module Matters
[2-3 paragraphs explaining WHY this topic matters]
> **The [Topic] Analogy**
>
> [Memorable analogy that makes the concept stick]
---
## What You'll Learn
[Clear learning objectives]
---
## Part 1: [Theory/Concepts]
### 1.1 [Subsection]
[Content with diagrams/examples]
> **Did You Know?**
>
> [Interesting fact]
---
## Part 2: [Practical Application]
[Hands-on content]
> **War Story: [Catchy Title]**
>
> [Real incident that illustrates the concept]
---
## Did You Know?
- **[Fact 1]**: [Detail]
- **[Fact 2]**: [Detail]
- **[Fact 3]**: [Detail]
---
## Common Mistakes
| Mistake | Problem | Solution |
|---------|---------|----------|
| [Mistake 1] | [What goes wrong] | [How to fix] |
| [Mistake 2] | [What goes wrong] | [How to fix] |
---
## Quiz
1. **[Question]**
<details>
<summary>Answer</summary>
[Detailed answer explaining why]
</details>
[4 questions total]
---
## Hands-On Exercise
**Task**: [What to do]
**Steps**:
1. [Step 1]
2. [Step 2]
**Success Criteria**:
- [ ] [Verifiable outcome]
**Verification**:
```bash
[Commands to verify]
Next Module
[Link to next module]
---
## Module Template (Platform Track)
Platform modules include additional sections:
```markdown
# Module X.Y: [Topic Name]
> **Complexity**: `[QUICK]` | `[MEDIUM]` | `[COMPLEX]`
>
> **Time to Complete**: X-Y minutes
>
> **Prerequisites**: [List required modules]
>
> **Track**: Foundations | Disciplines | Toolkits
---
## Why This Module Matters
[Real-world motivation - not exam-focused]
> **The [Topic] Analogy**
>
> [Analogy connecting to familiar concepts]
---
## What You'll Learn
[Learning objectives]
---
## Key Concepts
### [Concept 1]
[Theory explanation with diagrams]
### [Concept 2]
[More theory]
---
## Current Landscape
How this concept is implemented in practice:
| Tool/Approach | Description | When to Use |
|---------------|-------------|-------------|
| [Tool 1] | [What it does] | [Use case] |
| [Tool 2] | [What it does] | [Use case] |
---
## Best Practices
What good looks like:
1. **[Practice 1]** - [Explanation]
2. **[Practice 2]** - [Explanation]
3. **[Practice 3]** - [Explanation]
---
## Anti-Patterns
What to avoid:
| Anti-Pattern | Why It's Bad | Better Approach |
|--------------|--------------|-----------------|
| [Pattern 1] | [Problem] | [Solution] |
| [Pattern 2] | [Problem] | [Solution] |
---
## Did You Know?
- **[Fact 1]**: [Detail]
- **[Fact 2]**: [Detail]
---
## Common Mistakes
| Mistake | Problem | Solution |
|---------|---------|----------|
| [Mistake 1] | [Impact] | [Fix] |
---
## Quiz
[4 questions with hidden answers]
---
## Hands-On Exercise
[Practical exercise with verification]
---
## Further Reading
Books, talks, and papers for deeper understanding:
- **[Book/Resource]** - [Why it's valuable]
- **[Talk/Video]** - [Key takeaway]
- **[Paper/Article]** - [What you'll learn]
---
## Next Module
[Link to next module]
Writing Guidelines
Tone
- Conversational, not academic
- Empathetic to learner struggles
- Confident but not arrogant
- Use "you" and "we" freely
Analogies
- One memorable analogy per module minimum
- Connect concepts to familiar real-world things
- Analogies should illuminate, not oversimplify
War Stories
- At least one per module
- Must have real consequences (time lost, money lost, outage)
- End with a lesson learned
- Can be anonymized but should feel authentic
Code Examples
- All code must be complete and runnable
- Use realistic names (not foo/bar)
- Show expected output where helpful
- Include verification steps
Diagrams
- Use ASCII art for architecture diagrams
- Keep diagrams simple and focused
- Every complex concept should have a visual
Technical Accuracy
- Use current versions of tools
- Note when something is deprecated
- Link to official docs for deep dives
- For Platform track: cover principles before tools
Quiz Questions
- Test understanding, not memorization
- Answers should explain "why"
- Mix of conceptual and practical
- 4 questions per module
Complexity Tags
[QUICK]: Simple concept, fast read[MEDIUM]: Moderate complexity[COMPLEX]: Deep topic, requires focus
Quality Checklist
Before considering a module complete:
All Tracks
Platform Track Additional
Naming Conventions
File Names
module-X.Y-topic-name.md
Examples:
module-1.1-what-is-sre.mdmodule-2.3-error-budgets.md
Directory Structure
docs/platform/disciplines/sre/
├── README.md # Part overview
├── module-1.1-xxx.md
├── module-1.2-xxx.md
└── ...
Cross-References
When referencing other modules:
- Use relative links:
../foundations/systems-thinking/module-1.1-xxx.md
- For prerequisites: Link to specific module, not just track
- For further learning: "See also [Module X.Y: Topic]"
Dispatch Recipes (orchestrator perspective)
When the orchestrator delegates authoring to an agent, the recipe is:
Codex (T0 primary when codex cap healthy)
.venv/bin/python scripts/dispatch_smart.py draft --agent codex \
--mode danger --worktree .worktrees/<slug> --new-branch feat/codex-<slug> \
- < /tmp/<slug>-brief.md
The brief MUST include: module spec, density gates, citation discipline rule, target word count, frontmatter requirements. No --search flag on dispatch_smart.py — draft task class auto-sets codex_search=True which exports KUBEDOJO_CODEX_SEARCH=1 to the codex CLI ([[feedback_codex_writer_needs_search]] still applies at the codex layer). Default model is gpt-5.3-codex-spark; override with --model gpt-5.5 when first-pass quality matters more than per-call cost. Cross-family reviewer = cursor composer-2.5 per Decision Card C.
Cursor composer-2.5 (T0 primary when codex cap thin / throttle)
- File a GH issue with the module spec (use [[curriculum-writer]] template + density gates).
- Either dispatch headless via
.venv/bin/python scripts/dispatch_smart.py review --agent cursor --model composer-2.5 (note: draft task class also supports --agent cursor) OR comment "cursor please claim" so cursor IDE picks it from the queue.
- Cursor opens a PR from a worktree.
- Orchestrator picks PR up → dispatches codex R1 ([[cross-family-reviewer]]).
- On NEEDS_CHANGES (session-52 cursor-authored tooling/api/docs cohort measured 4/7 = 57% first-pass NEEDS_CHANGES — proxy signal pending a true curriculum-T0 cohort), comment with R1 findings; cursor fix-passes.
- On APPROVE, orchestrator merges (cursor does NOT merge per session-51 directive).
Deepseek-v4-pro (off-load when 3+ codex authors in-flight)
Same shape as codex via .venv/bin/python scripts/dispatch_smart.py draft --agent deepseek. Pair with a vigilant code-domain reviewer; deepseek hallucinates rule attribution ([[feedback_deepseek_v4_pro_viable_for_t0_content]]).
Post-author orchestrator checklist
- Run
.venv/bin/python scripts/quality/verify_module.py <path> → must pass density gates.
- Run
npm run build → 0 warnings.
- Run
.venv/bin/python scripts/check_site_health.py → 0 errors.
- Dispatch cross-family reviewer per [[cross-family-reviewer]] routing table.
- On R1 APPROVE, merge through PR (rebase). On NEEDS_CHANGES, fix-pass + R2.
References
- [[module-quality-reviewer]] — the 7-dimension rubric your authored module is graded against.
- [[cross-family-reviewer]] — post-author review protocol.
- [[dispatch-router]] — agent routing decisions.
- [[k8s-cert-expert]] — domain expertise for k8s/cert content.
- [[platform-expert]] — domain expertise for platform-engineering content.
docs/quality-rubric.md — full rubric definition.docs/pedagogical-framework.md — research + guidelines.scripts/prompts/module-writer.md — standard module-writer prompt.
