agentleFS
Runtime-3rr0rRuntime-3rr0r/canvas-scanner/skills/exam-prep/SKILL.md

exam-prep

Skill2 starsChanged 57 days agoMITView on GitHub

Use when planning an exam-prep sprint from Mastery-Log.

9 other projects use a version of this.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

2 rules. 0 are common, 1 is distinctive, and 1 is very specific to this project.

  • 0

    Common

    Most of the 6 edited versions we read keep these.

  • 1

    Distinctive

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Track: after every drill session update Mastery-Log (topic | status | date).

    line 18
  2. Distinctive:

    Don't plan more hours than exist — respect the Roadmap's load tags.

    line 22

“Common” is measured: kept by at least 6 in 10 of the 6 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Exam Prep Sprint

Turn Mastery-Log + Question-Bank + the Roadmap into a focused, load-balanced sprint for one exam. Reuses existing systems; nothing new to invent.

Inputs

  • Course + exam date (from the user or COURSE-BRAIN key dates)
  • <Course>/Notes/Mastery-Log.md (statuses) · <Course>/Notes/Question-Bank.md (drill material) · Roadmap.md (available load)

Procedure

  1. Rank weak topics: 🔴 first, then stale 🟡 (untouched ~3 weeks). Sort by: foundational weight (a broken prerequisite cascades — fix it first), past-exam point value if known, confidence decay (older 🔴 > newer).
  2. Build the sprint: a 3 / 7 / 10-day plan (pick by time-to-exam), day-by-day targets, each with a time estimate and a "drill this" pointer into Question-Bank, practice sets, or Key Points.
  3. Auto-add as Roadmap study blocks in the sprint window, load-balanced (🔴 topics get heavier blocks, never stacked onto an already-🔴 day).
  4. Track: after every drill session update Mastery-Log (topic | status | date).
  5. Post-exam feedback: user reports the score + which topics appeared → write ground truth into Mastery-Log so the next exam's ranking learns.

Pitfalls

  • Don't plan more hours than exist — respect the Roadmap's load tags.
  • Refresh Mastery-Log the same day you build the sprint; stale statuses mis-prioritize.
  • One sprint per exam; re-run only when new weak topics appear.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.