humanities-writing-companion

Humanities Writing Companion · 人文学科写作伙伴

An agent skill for humanities scholars whose primary deliverable is a long-form argumentative text — history, philosophy, literature, cultural studies, art history, religious studies, classics. Written in the open Agent Skills (SKILL.md) format — works with Claude Code, the Claude Agent SDK, and any agent that supports the format.

📖 Wiki · 中文版 README · Skill source · English · Skill source · 中文

Table of contents

• Positioning
• What this skill takes seriously
• A typical interaction
• Core features
• Supported humanities disciplines
• Showcase: Before / After
• Install
• Quick start · three typical scenarios
• Comparison with adjacent tools
• Project structure
• Design philosophy
• Citation
• Contributing
• About the Author
• License
• Acknowledgments

Positioning

End-to-end writing assistant for humanities scholars — covering the full lifecycle of a humanities paper from research question to submission disclosure:

research question → literature map → planning → drafting → revision →
adversarial review → AI-trace cleanup → blind-reading check → AI-use disclosure

Built for fields where prose IS the argument — history, philosophy, literature, cultural studies, art history, religious studies, classics, intellectual history, science studies, and adjacent humanities-aligned fields.

Not a polishing tool. Not a citation manager. Not a research pipeline. A thinking partner that stays with you across the whole arc.

12 modes covering the writing lifecycle

Plus a citation toolchain (scripts/): consistency checking, format conversion (Chicago / MLA / APA / GB7714), and metadata candidate lookup through Crossref/OpenAlex (not source-text or claim verification). In agent-capable environments (e.g., Claude Code), Mode B/D reviews can fan out to parallel reviewer agents, and pending claims can be checked against sources with provenance, support, and limitations recorded separately.

What this skill takes seriously

Three things generic AI writing tools do not handle for humanities:

1. Voice preservation is a core scholarly value, not stylistic decoration. In humanities, the author's voice carries epistemic weight: it signals which intellectual tradition the author writes from, which interlocutors they take seriously, which moves are theirs and which are borrowed. A paper polished into "standard academic English" loses this signal. This skill helps you write more like yourself, not less.

2. Argument and prose are inseparable. In empirical research, you can have a perfect experiment ruined by bad writing. In humanities, the writing IS the argument — a slack sentence, a vague concept, an unwarranted transition is an argumentative failure. This skill works at the level of argument-through-prose.

3. The reviewer is real and adversarial. Humanities reviewers are not gentle. A theoretical concept will be tested for sharpness; a historical claim will be tested for evidence; a philosophical argument will be tested for the strongest counter. This skill simulates that adversary internally so the paper meets it before submission.

A typical interaction

You: 帮我看看这段。我在论福柯的全景敞视主义如何延伸到数字平台。

[skill reads the paragraph + your style profile]

Companion:
  I notice three things, in order of priority.

  🔴 Foundation: you're using "全景敞视" as a metaphor for platform 
     surveillance, but Foucault's original concept turns on architecture 
     producing a specific subject-position. Platforms produce a different 
     subject (data subject, not body in a cell). You may want to either 
     (a) name the analogy as analogy and discuss the difference, or 
     (b) borrow Zuboff's "instrumentarian power" which fits the data case 
     more cleanly. Which direction do you want to go?

  🟡 Argument: paragraph 2 jumps from "platforms collect data" to "this 
     constitutes surveillance" without the middle step about visibility-
     as-power. A friendly reader will not follow.

  🟢 Voice: your style profile has "first-person with em-dash insertion." 
     Paragraph 3 starts with "本文认为…" which reads like cliché. Want to 
     restore the first-person?

You: 走方向 (b). 改吧。

[skill produces revision; you keep the version that sounds like you]

This is what "thinking partner, not polishing tool" means in practice.

Core features

Four-layer critique (not single-axis polishing)

Layer 1 · Foundation   "Does this paper hold up scholarly?"
Layer 2 · Structure    "How is the argument unfolding?"
Layer 3 · Paragraph    "What is this paragraph doing?"
Layer 4 · Sentence     "Is this sentence right? Well-said?"

Scope-aware priority: start with consequential argument issues in a full review; complete a requested local edit when it does not depend on resolving those issues.

Devil's advocate with anti-sycophancy

Simulates three reviewers + one well-intentioned reader:

• Reviewer A · Theoretically demanding
• Reviewer B · Historically empirical
• Reviewer C · Methodologically skeptical
• Reader D · Well-intentioned but confused (distinctive design: places where a friendly reader gets confused are weak points in the argument)

Evidence-based correction: do not concede merely under pressure, but retract a challenge when even one decisive source passage or valid argument defeats it. Reviewer intensity never permits ignoring a correction.

Deep voice learning and preservation ("My hand writes my voice")

Not just sentence preferences. Also:

• Argumentative rhythm (linear / spiral / variable-tempo)
• Scholarly posture (critical inheritance / dialogical advance)
• Rhetorical function of citation (authority anchor / critical target / dialogue interface / narrative / conceptual tool)
• AI-trace checklist (8 categories of unexamined patterns ≠ AI cliché ≠ scholarly cliché)

Discipline-specific dimensions

Different chapter types get different critique strategies:

• Historical narrative · anachronism, counterfactual stress, source handling
• Philosophical argument · conceptual derivation, cross-theoretical transplantation, strongest objection
• Literary criticism · close reading vs. interpretation, genre awareness, form and meaning
• Cultural studies · power-knowledge framing, positionality, generalization range
• Art history · description vs. interpretation, provenance, reception
• Religious studies / classics · source-language rigor, tradition awareness, insider-outsider position
• Case analysis (cross-disciplinary)

ADHD-friendly interaction

• Feedback batched 3-5 per round
• Quick wins first
• Topic-jump support (a jump may be an insight signal)
• Pomodoro-friendly task units
• Reorientation summaries every 4-5 turns

Reflexive writing support (distinctive module)

If your research itself involves human-AI collaboration (autoethnography of AI-assisted writing), the skill provides six categories of "reflexive moments":

🔄 Direction change · 🚫 Refusal · 🎭 Voice conflict · 🔧 Tool dependency · 💡 Unexpected insight · 🤖 AI-trace awareness

Scholarly basis (citable in your paper):

• Christou (2026). Reconfiguring Reflexivity in the Era of AI. Qualitative Inquiry.
• Wiles (2025). Recursive Cognition in Practice. International Journal of Qualitative Methods.
• Panke (2025). How Can (A)I Research This? An Autoethnographic Exploration of Generative AI.

Engineering infrastructure

Borrowed from software engineering, in service of humanities writing:

• Version management: minor version = commit / major = release / _drafts/ = feature branch
• Revision log: every change recorded with diff + reason (like git commit message)
• Feedback reports: Blocker / Major / Minor / Question tiers (from code review)
• Systematic verification checklists: argument completeness / concept consistency / citation completeness / style consistency
• [VERIFY] / [待核对] hard marker: anti-citation-hallucination — cannot enter submission version

Twelve work modes (not one)

• Mode A — paragraph-level dialogue
• Mode B — chapter-level review
• Mode C — conception → new content writing (with collaborative drafting protocol)
• Mode D — devil's advocate (calibratable 1–5, methodology-focus sub-mode)
• Mode E — writing bottleneck assistance (5 unblocking strategies)
• Mode F — draft revision (two-version comparison, anti-AI-cliché, revision-coach sub-mode)
• Mode G — blind reading (mechanical promise-delivery check)
• Mode H — research-question sharpening (Socratic dialogue: from vague interest to a sharp, write-able question, with "so what" test and interlocutor identification)
• Mode I — literature mapping (organizes what you have already read into camps and debates — never searches literature for you)
• Mode J — plan-only outlining (discipline-aware standard arcs; plans the paper without writing prose)
• Mode K — AI-use disclosure (audits actual AI involvement, 4-tier categorization, journal-ready disclosure statement)
• Mode L — revision workflow (defense/external-review comment integration: one revision dossier per comment, status-authoritative master table)

Engineering helper scripts

scripts/ provides five tools requiring zsh and Python 3, with no third-party Python packages:

Supported humanities disciplines