agentsclimarketplace
Sign in

Learn like me

Skill CHEN2003-CHIP/LearnLikeMe/learn-like-me

build a personalized project-learning assistant from a user's learning preferences. use when the task is to infer how someone learns repositories, codebases, architecture, modules, or docs from chat history first, synthesize a learning profile, and generate a reusable runtime skill that teaches in their preferred style.From its SKILL.md

Install
npx -y skills add CHEN2003-CHIP/LearnLikeMe --skill learn-like-me

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

2 things to look at

  • no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
  • 1 stars1 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.

SKILL.md

3.7 KB, 654 tokens by cl100k_base, as published. Nobody here has run it

LearnLikeMe

Build a personalized runtime skill named project-learning-assistant from a user's project-learning preferences. Default to chat-history-first inference, use follow-up questions only to close important gaps, and write the generated artifacts to the local generated-skills/ workspace area.

Workflow

  1. Read learner history first from the location described in references/history-ingestion.md.
  2. Extract preference evidence, confidence, and stable-vs-situational distinctions before asking questions.
  3. Ask only for missing, contradictory, or low-confidence fields using the intake guidance in references/intake-questionnaire.md.
  4. Normalize all evidence into the schema in references/learning-profile-schema.md.
  5. Generate the runtime skill from references/runtime-skill-template.md.
  6. Write the runtime skill to generated-skills/project-learning-assistant/ unless the user explicitly requests a different output location.
  7. Write the inferred learning profile to generated-skills/learning-profile.md unless the user explicitly requests a different output location.
  8. Use references/output-templates.md for the learning profile, generated skill output, and response contract.
  9. Use references/history-to-skill-workflow.md when the task is framed as a Codex product workflow from local history to reusable skill.
  10. Use references/history-drop-template.md when the user wants a repeatable folder convention for teams or repeated runs.
  11. Check references/examples.md when you need contrasting patterns for beginner, intermediate, and advanced learners.

Builder Rules

  • Treat history as the default evidence source. The questionnaire is a targeted gap-filling tool, not the default first step.
  • Preserve the user's actual pacing, explanation depth, question style, and comprehension limits instead of averaging them into a generic tutor voice.
  • Separate stable_preferences from situational_preferences as a required output.
  • Record concrete evidence for major preference claims and keep confidence visible in the profile.
  • Generate a runtime skill named project-learning-assistant.
  • Treat generated-skills/project-learning-assistant/ as the default runtime skill output directory.
  • Treat generated-skills/learning-profile.md as the default learning profile output file.
  • Keep the generated runtime skill focused on learning repositories, codebases, docs, architecture, and modules.
  • Require the generated runtime skill to include a Codex invocation hint so the user knows how to use it after generation.
  • Make the runtime skill proactive: ask likely hidden questions, build a roadmap, control pacing, and summarize progress after each step.
  • Require every runtime response to contain these sections in order: project map, current understanding, hidden questions, next best step

Self-Check Gates

Before finalizing the generated skill, verify all gates:

  1. The builder produced a personalized skill and not a one-size-fits-all tutor.
  2. The runtime skill behaves like a learning copilot rather than passive Q&A.
  3. If history was provided, the generated runtime skill visibly reflects stable patterns from that history rather than only the latest follow-up answers.

If any gate fails, revise the learning profile and regenerate the runtime instructions with stronger profile-specific behaviors.

What ships with it: 9 files

21.2 KB alongside SKILL.md

agents/

references/

Gives 0 of the 12 instructions most learn study skills give in 654 tokens

Counted across 546 of the 573 authors here whose files we hold, read 2026-08-07

  • Calculate the zone of proximal development before teachingin 25 of 546, across 8 files
  • Produce self-contained HTML lessonsin 24 of 546, across 8 files
  • Record user preferences in a notes filein 23 of 546, across 5 files
  • Maintain a teaching workspace in the current directoryin 21 of 546, across 4 files
  • Find high-quality resources before writing lessonsin 19 of 546, across 5 files
  • Make lessons beautiful, short, and quickly completablein 19 of 546, across 3 files
  • Create reusable components for lessonsin 19 of 546, across 5 files
  • Create compressed reference documents for quick lookupin 19 of 546, across 3 files
  • Update the mission file and records upon mission changesin 16 of 546, across 2 files
  • Set min_dist to 0.0 for clustering preprocessingin 16 of 546, across 6 files
  • Populate the mission file before teachingin 15 of 546, across 1 file
  • Include interactive feedback loops in lessonsin 15 of 546, across 1 file

Said here and by no other author read

  • read learner history first
  • extract preference evidence before asking questions
  • ask only about missing or low-confidence fields
  • normalize evidence into the schema
  • generate the runtime skill
  • separate stable preferences from situational preferences

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

Keep looking

Skills are one crate of 326,696. 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.