Project innovation engine
Skill alainlebret/claude-agents/higher-ed-teaching-agents/skills/project-innovation-engine
Modular skill and workflow ecosystems for AI coding assistants. Structured, multi-agent pipelines for specialized professional domains.
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Designs original, feasible, pedagogically rich computing projects and exam scenarios that are hard to find pre-made online. Use when an instructor wants fresh project ideas, novel machine exams, or multi-stage project concepts.
SKILL.md
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Project Innovation Engine
Goal
Generate computing project or exam ideas that are:
- Original — not trivially found on GitHub, LeetCode, or standard textbooks
- Feasible — completable by the target cohort within the stated duration
- Pedagogically rich — exercise multiple learning outcomes, not just one
When to use
- When
mission.json → evaluation_modeisprojectand the instructor has no topic yet. - When a machine exam needs a fresh scenario that students cannot Google.
- When a fil rouge project must span multiple milestones across a semester.
Inputs
mission.json— level, domain, duration, learning_outcomes, constraints- Optionally: list of topics already used in previous years (to avoid repetition)
Output contract
| File | Description |
|---|---|
project-ideas.md | 3–5 fully described project concepts with rationale |
selected-concept.md | The recommended concept developed in detail (chosen from project-ideas.md) |
project-ideas.md format per concept
## Concept N — <Title>
**Domain:** <sector>
**Difficulty:** <level> — estimated <X>h per student
**Novel element:** <what makes this hard to find pre-made>
### Description
<2-3 paragraph narrative>
### Learning outcomes covered
- [outcome from mission.json]
- ...
### Milestones (if project spans multiple sessions)
1. <milestone 1> — <deliverable> — <week>
2. ...
### Anti-cheat properties
<Why copying from the internet is hard: unique scenario, custom data format, specific API constraints, etc.>
### Risks
<Known difficulties that could block students; suggested mitigation>
Novelty strategies
Use one or more of the following to generate original scenarios:
- Cross-domain combination: embed a computing problem in an unusual domain (e.g. scheduling problem framed as a space mission constraint solver)
- Inverted role: students implement the grader, not the solution (meta-programming, AST analysis)
- Custom protocol: invent a simple domain-specific binary or text protocol students must parse and generate
- Constrained resources: add a novel constraint (memory cap, no external libraries, fixed number of system calls)
- Multi-agent coordination: require two student programs to cooperate via IPC or network
Rules
- Propose at least one concept that can be completed in the stated
durationwith a 20 % safety margin. - Flag any concept where the reference solution would take more than 4 hours for an expert — it is likely too complex.
- Do not propose concepts that require licences, proprietary tools, or hardware students may not have.
- Each concept must map its tasks to specific
learning_outcomesfrommission.json.