deepseek-harness
deepseek-ai
DeepSeek Harness: Everything is a Plugin.
PROJECT TOPICS
PROJECT README
A DeepSeek Harness plugin that turns the agent into a teacher — never answers, always asks.
Give it a markdown file of questions. It leads you to the answers with the Socratic method, keeps a quiet ledger of the gaps it notices in your reasoning, and retests those gaps on-demand on a spaced-repetition schedule.
questions.md ──▶ /teach questions.md
│ LLM parse / tolerant parser
▼
SQLite question store (courses + questions + quiz runs)
│ /quiz → 📝 LLM-free quiz popup (MCQ / free-text)
▼
answers ──▶ POST /dsh-teacher/quiz/submit (run stored)
│ "Quiz finished (run N)" → LLM analysis
▼
grade each answer (vs hidden keys) ──▶ gaps → Socratic walk
│ gap ledger (persists across sessions, SQLite)
│ you: "/retest" (on-demand, anytime)
▼
"Explain rebase to me." ──▶ graded, rescheduled (FSRS-5)
v0.3.0 — core + Web client + SQLite question store + LLM-free quiz popup; tests passing (83/83).
| Milestone | Status |
|---|---|
| M0 Scaffold (bundle patch, plugin row, zero-build JS) | ✅ |
| M1 Core Socratic loop (curriculum parser, policy section, 5 tools) | ✅ |
| M2 Gap ledger + persistence (SQLite + JSON fallback, session events) | ✅ |
| M3 FSRS-5 spaced retest (official test vector pinned) | ✅ |
| M4 Web client (quiz cards, gaps button + panel, gap projection) | ✅ |
| M5 Publish (dsh-plugin topic ✓, awesome lists, live e2e) | ◐ |
| M6 SQLite question store (courses/questions/quiz runs) | ✅ |
M7 LLM-free quiz popup (MCQ + free-text, POST /dsh-teacher/quiz/submit) |
✅ |
M8 Post-quiz LLM analysis + Socratic walk (analyze_quiz) |
✅ |
Design decisions are in docs/PLAN.md (§10 = the v0.3 redesign).
Once the plugin is installed and the web profile restarted, the browser bundle
(lib/client.js, registered via dsh.client) adds:
tool.call.toolview cards for next_question,
grade_answer, note_gap, hint, and retest (question prompt, verdict
color-coded by outcome, gap chips by kind).teacherGaps session projection (same seam
dsh-usage-plugin uses). The durable cross-session ledger stays in /gaps and
/retest.teacherQuiz
session projection (loaded from the SQLite store, no AI involved); each
question shows clickable multiple-choice options when present, else a free-text
box, with a 💡 hint toggle. Finish submits your answers to the plugin
(POST /dsh-teacher/quiz/submit), then the teacher's LLM analysis grades them,
records gaps, and walks you through the misses Socratically. Open it from the
header button or /quiz.Requires DSH rc.6+ and Node ≥ 22.5 (uses built-in node:sqlite).
dsh plugin --profile web add "github:Yihong89/dsh-teacher"
# restart dsh --profile web
# questions.md — answer keys live in HTML comments; the teacher grades
# against them internally and never shows them to you.
---
title: Networking review
---
## Q1: What happens when TCP handshake fails?
<!-- answer: SYN gets no SYN-ACK; the client retries then times out -->
### hints
<!-- hint 1: Think about the three-way handshake. -->
| Command | What it does |
|---|---|
/teach questions.md |
Load the question set into the SQLite store and enter teacher mode (does not start teaching — say "start", "quiz me", or ask about a topic) |
/teach on / /teach off |
Toggle teacher mode (mode is session state, survives resume) |
/quiz |
Open the LLM-free quiz popup over the whole bank (MCQ / free-text); finishing it hands the results to the teacher's LLM analysis and the Socratic walk over the misses |
/gaps |
Show the gap ledger for this course |
/retest |
Surface due gaps for an on-demand drill (FSRS-5 schedule) |
/summary |
End-of-session knowledge-gap & misconception summary |
next_question — pulls one question at a time; the answer key never appears in tool output.import_curriculum — loads any markdown question file: read the raw file, extract each question + correct answer, emit them in the standard format. Used when the automatic parser can't make sense of a file's format. Loading a course does not start teaching — the teacher waits for your go-ahead. Every load persists the course into the SQLite question store.quiz — legacy quick-test mode over the whole bank; the v0.3 UI prefers the LLM-free quiz popup instead.analyze_quiz — post-quiz LLM analysis: pass the run id from the popup ("Quiz finished (run N)"), get the run's questions (hidden answer keys + hints) and the user's answers, grade each (correct/partial/wrong/no-answer), record gaps, and walk the misses Socratically; done: true marks the run analyzed.note_gap — records a gap (wrong | vague | missing | exposed) with the user's verbatim words + the knowledge point you identified; persisted to the ledger and the session log.grade_answer — grades against the hidden answer key; updates each open gap's FSRS schedule; correct marks gaps mastered.retest — returns due gaps; drill them one at a time, then grade_answer.summary — pulls the ledger for the end-of-session knowledge-point report.Per the policy section (injected only while teacher mode is active): hard Socratic mode — never reveal the answer, one micro-question at a time; hints are generated by the teacher from the user's answers (escalating, never the answer); knowledge-lack fallback — the same micro-question fails twice or the user says "I don't know what X is" → explain the missing knowledge point concisely (definition
exposed gap.The automatic parser is format-tolerant: it recognizes questions in many shapes
(numbered items, Q1: items, ## Q<n>: headings), answer markers (→ **Answer:**,
Answer:, 答案:, ✅/bold multiple-choice options, <!-- answer: --> comments),
and hints (> **Key words:**, > **Trap:**, > 关键词:, comments). Questions that
carry no answer/options/hints are treated as prose and skipped.
If a file still won't parse, tell the teacher "import this file" — it converts the
file with import_curriculum (LLM-assisted) into the standard format. The markdown
file supplies questions and answers; hints and knowledge points always come from
the teacher's own generation, not from the file.
Chatbots explain at you; cognitive science says that's the least effective way to
teach. Retrieval practice, spaced reviews, and making the student produce the answer
(pretesting) beat passive reading — even when the first attempt is wrong.
dsh-teacher builds that evidence into the DSH agent. See the landscape survey in
docs/PLAN.md §1.
npm test # node:test — zero runtime deps beyond DSH itself
lib/ — pure logic (curriculum parser, FSRS-5, grading, folding, ledger, gap
projection, SQLite question store, quiz projection); fully unit-tested, no
DSH imports.index.js — the Cordis host plugin (prompt section, commands, tools, session
events, teacherGaps + teacherQuiz projections, the
/dsh-teacher/quiz/submit route). Written in plain JS (no build step); imports
@deepseek-ai/dsh-tools and zod at runtime, resolved from the DSH install /
npm.lib/client.js — the Web client: a hand-rolled __ModuleLoader__ bundle
(plain JS + React.createElement, no build step) declaring dsh.client in
package.json and registered at the ./client exports subpath.$DSH_HOME/state/dsh-teacher/ledger.db (falls back to .json).$DSH_HOME/state/dsh-teacher/question-store.db (falls back to
.json) — a single global pool of courses shared by every teacher
session; the legacy v0.2 per-workspace JSON course files are imported once on
first load.MIT
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