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dsh-research-lab

Research lab toolkit for DeepSeek Harness: AutoSci wiki, ASI-Bench eval ledger, self-building FTS5 retrieval, arXiv digest/review, writing rewrite

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Language
TypeScript
Created
Aug 31, 2026
Updated
Aug 31, 2026
GitHub repo

Introduction

dsh-research-lab

Research-lab toolkit for DeepSeek Harness: a wiki-centric, eval-rigor-driven research workflow distilled from AutoSci (wiki as the center of a research project), ASI-Bench (evaluation rigor), daily-arXiv-ai-enhanced (digest), ChatPaper (paper review), nature-skills / research-writing-skill (academic writing), Supervisor-Skills (senior-review instinct), and hard lessons from real distillation experiments (hypothesis → verdict loops, eval 口径 discipline).

Tools

toolpurposeinspired by
rlab_wikiwrite wiki pages (experiment/literature/decision/todo) under <project>/.rlab/wiki/ + rebuild indexAutoSci
rlab_experimenthypothesis → prediction → evidence → verdict → conclusion pagesAutoSci + falsifiability discipline
rlab_benchper-project eval ledger (bench.jsonl); flags 口径 (protocol) mismatches, e.g. mixing test/dev splitsASI-Bench
rlab_paper_reviewarXiv id → adversarial 8-point review checklist (also applies to your own papers)ChatPaper + ASI-Bench
rlab_arxiv_digestkeyword digest of recent arXiv papers → <project>/.rlab/digests/<date>.mddaily-arXiv-ai-enhanced
rlab_writingline-anchored academic-expression check (passive/weak verbs/vague quantifiers/filler/term consistency)nature-skills, research-writing-skill
rlab_relatedself-building keyword retrieval: NLP tokenization + SQLite FTS5, no preset lexicon — terms are TF-IDF-mined from your docs (EN words + ZH n-grams) and expanded iteratively (search → mine → merge → repeat)hamuleite-style corpus, Rocchio relevance feedback
rlab_statusone-screen project state: wiki counts, bench report, open TODOs

On-disk layout

<project>/.rlab/
  wiki/
    index.md            # auto-rebuilt
    experiment/<id>.md  # hypothesis → verdict
    literature/<id>.md  # paper notes (link from digests)
    decision/<id>.md    # ADRs: why you chose X
    todo/<id>.md        # open items
  related.db           # FTS5 corpus + auto-built lexicon (rlab_related)
  bench.jsonl           # one eval row per line (JSON)
  digests/<date>.md     # arXiv digests

Eval 口径 discipline (rlab_bench)

Every row must state metric, split, and for MTEB tasks the hf_subset. The report warns whenever a task's history mixes splits — comparing test-vs-dev scores is the classic way papers fool themselves (and reviewers).

Install

# from ~/.dsh/profiles/web
pnpm add @snow-the/dsh-research-lab@file:../.dsh-starter/plugins/dsh-research-lab
# add "dsh-research-lab" to dsh.profile.bundles, then restart dsh web

Build

pnpm install && pnpm build   # tsc declarations + esbuild bundle → dist/