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Honest Scholar honest-scholar gives your whole scientific research workflow one uniform, git-native home inside Claude Code — idea → literature → hypothesis → test → publish-decision → paper → thesis — so you and your collaborators work the same way and never re-derive the workflow, or its rigor, per project. Concretely, it:
  • scouts and positions the literature over the citation graph (OpenAlex + Semantic Scholar) — forward/backward citations, co-citation and bibliographic-coupling neighbours, a CSL-JSON bib, and a decision triage sidecar;
  • runs the hypothesis → test → publish-decision lifecycle with every result traced to a run-ref, refuted ideas retired (not deleted), and named human sign-off on each material decision;
  • keeps datasets reproducible — a tiered registry with SHA-256 fixity, a private rclone mirror, datasheets, and Croissant import/export;
  • builds and checks your understanding with a Socratic tutor-examiner (defend) so you can actually defend the work in review;
  • delegates the engineering (design, plans, code) to a bound engineering backend, staying domain- and tool-neutral.
Because AI is in the loop, it is also built so the science stays honestly yours and defensible (see The mechanics of honesty).
New here? Read the docs at honest-scholar.science — start with the User Guide. Status: pre-release, actively developed. The design is complete and recorded (see Design & reasoning); the skills and their supporting honest-scholar CLI are implemented. See STATUS.md for the current ledger.

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The mechanics of honesty

Honesty here is mechanical, not a slogan. Two principles do the load-bearing work (both grounded in the literature — see resources/references/):
  1. Assistants, not researchers (agency). The skills keep the accounts, advise as a mentor, and discuss as a colleague — they do not perform independent research or make material scientific decisions. Every material decision (is a hypothesis confirmed/refuted, is a result real, is a paper worth publishing, what the thesis claims, is it defensible) is yours, recorded with a named sign-off. You author; the skill drafts. You cannot “run” the workflow to produce a paper or thesis — you drive it. So the science is honestly yours.
  2. You must understand it (understanding). Every material claim, decision, and method must be understood to the standard a good mentor or reviewer expects. honest-scholar verifies and builds that understanding through Socratic questioning and teaching (the defend skill), and will not let work advance past a gap silently — including examining the why behind the methodology, to prevent cargo-cult rigor. So “I understand my own paper” is true, not assumed.
Around them sits the rest of the honesty kit: the rigor kit guards against cargo-cult method (anti-cargo-cult), provenance makes evidence honest (every number traces to a run-ref), anti-Goodhart keeps metrics honest (no productivity scores to game), and file-drawer + disclosure discipline retires dropped ideas rather than deleting them and discloses AI use truthfully (see DISCLOSURE.md).

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The skills

A single object×action shape at three nested levels: Each resolve skill drives one candidate through science-before-engineering staged documents, delegating the engineering (design, plans, code) to the bound engineering backend via the engineering-delegation contract. Shared capabilities: literature (scout/position over the citation graph; CSL-JSON bib + a decision triage sidecar), dataset (tiered registry + private rclone mirror + SHA-256 fixity + datasheets), and a pluggable experiment-backend contract. Cross-cutting: progress (frontmatter status + a generated, semantic dashboard; refuted = done) and defend (the Socratic tutor-examiner, with author-selectable mentor personas — never inferred from personality). Onboarding: research-init scaffolds a fresh repo (init) or backfills an existing one (adopt).

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Honest AI use — disclose & cite

When a paper is done you can add a truthful AI-use disclosure — a short, evidence-based statement of what you and the AI each did — plus a citation to honest-scholar. See DISCLOSURE.md for the template, how-to-cite, and an optional badge. paper-synthesis proactively proposes both at finalize (after the publish decision is signed), drafted from your provenance record — who signed off what, which run-refs back which results. It is opt-in and author-owned: you review, edit, adopt, or decline. The growth angle, plainly: every published paper that carries the disclosure points other researchers to the tool. But Honest Scholar only supports honest disclosure — it does not certify that your research is honest, and there is no seal of honesty. The statement says what was done and links the record; readers judge.

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Install

honest-scholar is a Claude Code plugin; the repo is its own marketplace.
Enable it for a whole project (so collaborators get it on trust) — add to the consuming repo’s .claude/settings.json:
To pin a fixed release, add "ref": "v0.1.0" inside the marketplace source; omit it to track the plugin’s main. See STATUS.md for the current state.

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Design & reasoning

The design is captured in three complementary layers:
  • Specs — the what: docs/design/ (meta-spec + four sub-specs).
  • Decision log — the why: decisions/ — MADR-style ADRs, each with the options considered and the rejected alternatives and why.
  • Reference digests — the evidence: resources/references/ — verified primary-source digests behind each skill and principle.
  • Proposals — docs/design/proposals/: the design specs for the honest-scholar CLI modules (now implemented) and for cross-repo thesis aggregation.
Also: the User Guide, the commit-attribution / discovery convention (resources/commit-attribution.md), and the visual identity (docs/design/visual-identity.md). This record is intended to seed a blog post / paper explaining the skills and their rationale — ideally written using honest-scholar itself.

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Contributing

See CONTRIBUTING.md. honest-scholar’s own development uses superpowers; using honest-scholar does not require it — engineering is delegated via the engineering contract.

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License

Apache-2.0.