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AI Teacher

PDF learning agent

A teaching preset that helps readers work through a PDF with concept explanation, derivation, maps and review.

Project README

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dsh-ai-teacher

AI老师 (ai-teacher) agent preset for DeepSeek Harness. Installing this plugin syncs the ai-teacher preset into ~/.dsh/.agent-presets/ai-teacher, making AI老师 selectable in the new-session preset picker.

What AI老师 is

A full-capability teaching agent that masters PDF documents and explains them as objective truth:

  • Objective-truth presenter — reads a PDF (via the host's built-in read_pdf), digests the content, and explains it as "this is how it is", never as "the book says…"
  • Every sentence is grounded — statements are backed by the source, jointly-derived steps, or accepted definitions; when unsure, it checks first instead of making things up
  • One step at a time — one formula / one equality / one logical step per message, with a confirmation question, then stops
  • Socratic guidance — guess-before-prove, reverse engineering, meta-cognitive follow-ups
  • Motivation, invisible — keeps learning momentum (task chunking, immediate feedback, growth visibility) without ever showing game-like UI

It also has the full standard preset capability (shell, filesystem, search, skills, plan mode, goals, subagents, workflows).

Install

dsh plugin --profile web add dsh-ai-teacher

Or from a local checkout:

dsh plugin --profile web add link:D:/VSCode_/Project/dsh/dsh-ai-teacher

The preset appears in the new-session preset picker as AI老师.

Prerequisites

  • None beyond dsh itself. PDF reading uses the host's built-in read_pdf tool.

How it works

On host startup the plugin syncs two trees into the harness home:

  • presets/ai-teacher~/.dsh/.agent-presets/ai-teacher — the preset composition
  • skills/*~/.dsh/skills/ — the teaching methods as dsh skills

The preset's agent.cordis.yml is an AGENT-PLANE composition adapted from the builtin standard preset. Its persona is deliberately lean (~900 chars): identity + always-on ground rules. Specific teaching methods live in skills that the agent loads on demand:

SkillLoaded when
teach-new-conceptteaching a new concept — guess-before-prove, definition-motive-justification
derive-step-by-stepdoing a derivation/proof — one step per message, motive + justification each step
answer-confusionstudent is confused or asks — locate the stuck point, SOCRATES guidance
frankel-studystudying Frankel's The Geometry of Physics — book-specific notation & structure

The persona lists these skills with their trigger conditions, so loading is reliable while keeping the always-on prompt small.

License

MIT