agentsclimarketplace

Project fil rouge designer

Skill alainlebret/claude-agents/higher-ed-teaching-agents/skills/project-fil-rouge-designer

Modular skill and workflow ecosystems for AI coding assistants. Structured, multi-agent pipelines for specialized professional domains.

Install
npx -y skills add alainlebret/claude-agents --skill project-fil-rouge-designer

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Designs long-form projects, milestone-based practical projects, capstone assignments, and fil rouge activities for higher education and professional reskilling. Use when an instructor needs a multi-session project with architecture, milestones, deliverables, scaffolding, evaluation strategy, and realistic technical scope.

SKILL.md

2.1 KB, 322 tokens by cl100k_base, as published. Nobody here has run it

Project Fil Rouge Designer

Goal

Build a multi-stage project that teaches through sustained implementation work while remaining technically feasible, assessable, and aligned with the course objectives.

Inputs

Read mission.json first. Then read any existing course materials, technical constraints, and prior project versions.

Produce

Generate as needed:

  • project overview;
  • technical storyline or context;
  • project architecture;
  • milestone plan;
  • expected deliverables per milestone;
  • teacher notes;
  • starter resources specification;
  • integration and testing strategy;
  • grading hooks for rubric-builder and submission-evaluator.

Required structure

Every project design must specify:

  • target audience and level;
  • prerequisites;
  • total duration and milestone breakdown;
  • student starting point;
  • files, APIs, binaries, datasets, or infrastructure provided;
  • files or modules students must create or modify;
  • observable outputs;
  • assessment checkpoints;
  • failure recovery or simplification path.

Design rules

  • Prefer cumulative milestones with explicit inheritance of prior work.
  • Make each milestone locally assessable.
  • Separate infrastructure provided from student work expected.
  • Avoid architectures so fragile that grading depends on guesswork.
  • Ensure the final project can be partially graded if the full integration fails.
  • For professional reskilling, map milestones to workplace tasks or delivery artifacts.

Hard constraints

  • Refuse projects with unrealistic scope for the allocated weeks or hours.
  • Refuse milestone wording that leaves file ownership ambiguous.
  • Flag hidden coupling between modules when it endangers grading fairness.
  • Require a test or evidence path for every major milestone.

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.