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Jf Writing Style Guide

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Ryyyyyyy233
jf-writing-style-guide

JF writing style reference for polishing academic prose and Chinese-to-English translation in Journal of Finance style. Triggers on: JF风格, JF润色, jf-skill, JF写作, JF polish, JF translate. v3.1 adds three-tier AI-fingerprint defense.

Overview

PublisherRyyyyyyy233
RepositoryJF-Writing-Skill
Skill namejf-writing-style-guide
Stars
91
Forks
11
Bundled files
8
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 8 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Ryyyyyyy233 on GitHub. Read the source before you install it.

Installation

Install the Jf Writing Style Guide AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Ryyyyyyy233/JF-Writing-Skill/tree/main/jf-skill-1.0.0
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Ryyyyyyy233/JF-Writing-Skill.git /tmp/JF-Writing-Skill
mkdir -p .claude/skills
cp -r /tmp/JF-Writing-Skill/jf-skill-1.0.0 .claude/skills/jf-writing-style-guide
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Jf Writing Style Guide in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Jf Writing Style Guide on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Jf Writing Style Guide is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

JF Writing Style Guide v3.1

Reference for writing and polishing academic finance prose in the Journal of Finance style. Extracted from 144 JF papers (2022--2025). Use for: drafting new sections, polishing drafts, Chinese-to-English translation.

v3.1 adds a three-tier AI-fingerprint defense (phrase scan, structural audit, quantitative verification), expanded anti-patterns covering structural symmetry, and a comprehensive detection-defense reference (Caveats M-R). Principles adapted from the structure-randomizer and deep-humanize skills.


Quick Start

Minimal invocation:

jf-skill 请帮我润色这段引言
JF风格 把这段中文摘要翻译成英文
jf-skill polish my results section

Execution flow:

  1. Diagnose — Which section of the paper? (Introduction, Results, Conclusion, etc.)
  2. Load references — Read the relevant reference file(s) for that section
  3. Apply patterns — Match templates, calibrate hedging, check collocations
  4. Polish — Iterate, avoiding mechanical substitution
  5. Verify — Run AI-fingerprint check

Trigger Conditions

Trigger Keywords

中文: JF风格, JF润色, jf-skill, JF写作, JF翻译, JF句型, JF句式, JF用语

English: jf-skill, JF polish, JF translate, JF style, JF writing

When NOT to Use

ScenarioUse Instead
General academic writing (not finance)General style guide
Writing for other journals (JPE, QJE, RFS)Check target journal conventions
Pure grammar/typo fixingDirect editing
Content/structure feedbackPeer review workflow

Mode Selection

User NeedModeWhat to Load
"润色引言" / "polish introduction"Introduction polishreferences/sentence-templates.md §1-5
"润色结果" / "polish results"Results polishreferences/sentence-templates.md §9-11; references/paragraph-patterns.md §29-33
"翻译摘要" / "translate abstract"Translation + polishreferences/phrase-bank.md; references/usage-guide.md §Non-Academic table
"检查全文写作风格" / "full paper audit"Full auditAll reference files; focus on AI-fingerprint markers
"不知道怎么开头" / "help me open"Opening brainstormreferences/sentence-templates.md §1
"润色结论" / "polish conclusion"Conclusion polishreferences/sentence-templates.md §15
"改写段落" / "rewrite paragraph"Paragraph restructurereferences/paragraph-patterns.md

Reference Files Index

FileContentWhen to Load
references/sentence-templates.mdPart A: Opening patterns, research questions, puzzles, gaps, contributions, findings, data, hypotheses, results reporting, economic magnitude, robustness, mechanisms, alternative explanations, limitations, conclusions, roadmaps, identification framing, null results, policy implicationsAny section-level writing
references/meta-discourse.mdClarifying notes, boundary-setting, interpretive control, layered analysis, narrative scaffoldingWhen results need careful framing; when preempting misinterpretations
references/phrase-bank.mdVerb collocations, adjective pairs, adverb combinations, hedging phrases (by certainty level), certainty phrases, logical connectors, model description patterns, non-academic-to-JF alternativesAll polishing and translation
references/paragraph-patterns.md30+ paragraph-level patterns: Claim-Evidence-Interpretation, Gap-Fill, Broad-to-Narrow, Alternative-Explanation, Puzzle-Resolution, Competing-Mechanisms, and moreWhen structuring or restructuring paragraphs
references/usage-guide.md8-step polishing workflow, appendix quick-reference templates, hedging calibration table, critical caveats A-R (ALL CAPS trap, contribution inflation, AI-fingerprint warnings, em dash overuse, "potentially" dilution, symmetry problem, high-trigger AI patterns, quantitative audit metrics, imperfect writing as defense, section-specific risks, 2-minute pre-submission check)Before finalizing any polished output

Core Workflow

Step 1: Diagnose the Context

Identify what section of the paper the text belongs to, what rhetorical function it serves, and what the user is asking for (polish existing, translate, draft new, restructure).

Step 2: Load Relevant References

Based on the diagnosis, read the appropriate reference files. Most tasks need only 1--2 references. A full paper audit needs all 5.

Step 3: Apply Patterns, Not Templates

The patterns in the references are descriptive (what JF authors actually do), not prescriptive (what you must do). Adapt them to the user's specific argument and evidence. Never copy-paste template sentences verbatim.

Step 4: Calibrate Hedging

Match claim strength to evidence strength. See the hedging calibration table in references/usage-guide.md.

  • Speculative: "It is possible that..." / "We cannot rule out..."
  • Suggestive: "These results suggest that..." / "This finding is consistent with..."
  • Strong: "We find that..." / "Our results show that..."
  • Definitive: "We conclude that..." / "Taken together, the evidence indicates that..."

Step 5: Three-Tier AI-Fingerprint Defense

AI text is statistically detectable: low structural variance, seamless transitions, zero admissions of uncertainty, flat information density. The correct defense is not lexical substitution but structural randomization — break symmetries, inject imperfection, vary every pattern that AI defaults to a mean. The three tiers below escalate from surface markers through structural diagnostics to quantitative thresholds.


Tier 1: Phrase-Level Scan (12 Markers)

Scan the full text. If any marker hits its threshold, fix mechanically.

#MarkerThresholdFix
1"notably" / "importantly" as sentence starters≤1 per sectionDelete or move mid-sentence
2Double quotation marks around technical terms for emphasis0 (JF: quotes ONLY for direct quotations or coined terms)Remove quotes
3Em dashes as sentence connectors0 (JF: only for parenthetical insertions)Replace with period or semicolon
4"This is because"≤1 per paper"The reason is that..." or state explanation directly
5Triple-dot ellipses (...) in body text0 (only in direct quotations)Remove
6"potentially" / "arguably"≤1 per sectionDelete; if claim is uncertain, restate as conditional
7Em dashes≤1 per paragraphReduce; JF average is 1 per 2-3 paragraphs in intro, near-zero in results
8Furthermore / Moreover / Additionally / It is worth noting / In conclusion / To summarize0 per paperDelete entirely; these are AI-default connectors that never appear in JF
9"shed new light on" / "bridge different strands" / "demystify" / "paint a more complete picture"≤2 DISTINCTIVE collocations per paperIf 3+ appear, keep the best 2, rephrase others
10"Not X, but Y" or "Not X; it is Y" binary contrast≤1 per paperBreak into 2-3 sentences with concrete context
11"We hope this work will..." / "We leave this for future work" / "Further investigation is warranted" / "It remains an open question whether..." / "We should be candid about..."0 per paperThese 5 hedging phrases are learned by GPTZero; use a specific forward pointer instead ("The next step is to test whether...")
12ALL CAPS applied to the paper's own findings0 (ALL CAPS only for stylized facts/puzzles)Move findings to abstract; keep ALL CAPS for genuine broad facts

Tier 2: Structural Self-Audit (5 Diagnostic Questions)

Read the full text and answer honestly. "Yes" to 3+ questions means structural rewrite is needed before submission.

  1. How many distinct paragraph templates appear? Does any single template appear more than twice? If most paragraphs follow claim→evidence→implication, the paper has a structural AI fingerprint. At least 30% of paragraphs should deviate from the dominant template.

  2. Is the Introduction the standard territory→gap→answer→roadmap four-segment structure? If yes: delete or compress the roadmap to one sentence. Consider merging territory and gap into one paragraph. Let the answer segment be 3-4 sentences, not a full preview of all findings.

  3. Do parallel subsections (e.g., three Results subsections) use the same reporting template? If yes: vary their structures deliberately. One opens with the result; another opens with the intuition; a third opens with an unexpected finding. Make their lengths unequal — allow one subsection to be 1 paragraph while another is 3.

  4. Are there at least 2 explicit admissions of uncertainty, surprise, or limitation? If no: add them. Human authors express doubt. Use "We do not yet have a clean explanation for..." or "This was not what we expected" or "Whether this holds beyond [boundary] we have not tested." Distribute uncertainty across different rhetorical types: data-level, mechanism-level, scope-level, and implicit (not using the word "uncertain").

  5. Does the Conclusion recapitulate contributions one by one (numbered re-listing of what the Introduction already promised)? If yes: rewrite as 1 paragraph stating the core insight (1 sentence) + implication (1 sentence) + open question or concrete prediction (1 sentence). Never restate the contribution checklist.


Tier 3: Quantitative Verification (5 Metrics)

Compute these metrics on the final text. If any fails, return to the draft.

MetricThresholdWhy
Sentence length standard deviation>25 (target >40)AI produces 15-25 word sentences in a tight bell curve; human writing is multi-modal with ≤8 and ≥35 word sentences coexisting
Connector density (however/therefore/thus/hence)<4 per 1000 wordsAI over-uses logical glue; human academic writing relies on implicit transitions
Extreme-length paragraph fraction≥20% of paragraphs ≤3 sentences or ≥8 sentencesAI defaults to uniform 4-6 sentence paragraphs; humans write 1-sentence transition paragraphs and 10-sentence technical deep-dives
Uncertainty expression count≥3 distinct admissions of uncertainty per paperDistributions across at least 2 different rhetorical subtypes (data-level, mechanism-level, scope-level)
Dominant template shareNo single paragraph template used in >70% of paragraphsIf claim-evidence-implication appears in >70% of paragraphs, the AI default structure dominates

Metric calculation reference:

  • Sentence length std dev: Extract all sentences, count words, compute std. If <25, deliberately inject 2-3 extremely short (≤8 word) sentences and 2-3 long (≥30 word) sentences into the densest section.
  • Connector density: Grep for "however|therefore|thus|hence|moreover|furthermore|additionally|consequently|accordingly". Divide count by total word count × 1000.
  • Extreme paragraph fraction: Count paragraphs with ≤3 sentences; count paragraphs with ≥8 sentences; sum and divide by total paragraph count.

Quality Standards

  1. ⚠️ IRON RULE: Do not produce sentences a JF editor would flag as AI-generated. Run the fingerprint check every time.
  2. ⚠️ IRON RULE: Match hedging to evidence strength. Overclaiming damages credibility more than underclaiming.
  3. One paper, not all patterns. Overusing distinctive collocations ("shed new light on," "bridge different strands," "demystify") in the same paper creates artificiality. Each paper typically uses only 2--4 distinctive patterns.
  4. Journal specificity. ALL CAPS openings, extensive enumeration, and roadmap paragraphs are JF conventions. Check target journal if not submitting to JF.
  5. Contribution restraint. "To the best of our knowledge" requires a genuine literature search. "First direct evidence" is used by <5% of published papers.

Anti-Patterns

#Anti-PatternWhy It FailsCorrect Behavior
1Mechanical template substitutionProduces formulaic, voice-less proseAdapt patterns to the specific argument; vary sentence structure
2Over-hedging every claimMakes the paper sound uncertain about everythingReserve hedging for genuinely uncertain claims; use confident phrasing for well-supported findings
3ALL CAPS for narrow/obvious claimsWeakens the impact of the opening; looks pretentiousALL CAPS only for broad, genuinely striking stylized facts
4"Battery of robustness checks" without specificsBoilerplate that signals lazinessName specific test types (falsification, placebo, external validity); explain WHY each matters
5Using 5+ distinctive collocations in one paperReads as AI-generated or trying too hardLimit to 2--4 distinctive patterns per paper
6"We contribute to three strands..." without differentiationFormulaic and forgettableExplain what is NEW about each contribution, not just that it exists
7ALL CAPS for your own findingsFindings go in the abstract, not as ALL CAPS openingALL CAPS openings are for stylized facts/puzzles, not your results
8Mechanical translation word-by-wordProduces unnatural English that no JF author would writeTranslate the meaning and rhetorical function, not the words; then apply JF patterns
9"Not X, but Y" binary contrastsAI's favorite clean structure: precise opposition with zero hesitation. GPTZero recognizes this as a high-confidence pattern.Break into 2-3 sentences with concrete context. For example: "BM's problem isn't numerical imprecision — factor-of-2 errors are acceptable experimentally. The deeper issue is that it claims something doesn't exist, when it does. In device design, this existence-blindness outweighs any numerical error."
10Rhetorical question immediately self-answered"What distinguishes these three regimes? The answer lies in..." — AI's default explanatory opener. Question too precise, answer too immediate.Either (a) open with assertion instead of question, or (b) delay the answer by 2-3 sentences with a caveat or historical context first.
11Symmetric parallel lists (equal-length items)"Star: [description+conclusion]. Cyclic: [description+conclusion]. Chain: [description+conclusion]." Detection algorithms measure cosine similarity across list items; equal structure = >0.9 similarity → flagged.Make list items deliberately unequal: one item 1 sentence, another 3 sentences; one opens with a citation, another opens with a result. Allow the paper's focal case to be detailed while others are briefly noted.
12"Below X, Y happens. Above X, Z happens" symmetric thresholdAI's default template for describing phase transitions or regime changes. Two perfectly symmetric sentences.Break symmetry — detail only the empirically rich side. Mention the other side in a half-sentence or parenthetical.
13Evaluation → Exhaustive list → Significance (three-beat summary)"The result is strikingly general. It holds for all N, all temperatures, all spacings, and all spectral densities. Any nonzero ergotropy is necessarily a beyond-BM effect." AI's perfect three-part cadence: evaluate, enumerate, declare significance.(a) Replace "all X, all Y, all Z" with "regardless of parameters" or the single most surprising member of the set. (b) Merge evaluation and significance into one sentence. (c) Or reverse order: significance first, then scope.
14Every paragraph follows the same structural templateIf all paragraphs are claim→evidence→implication, detection algorithms measure low structural entropy → flagged. Humans vary paragraph structure unconsciously.Target: no single template appears in >70% of paragraphs. At least 30% of paragraphs should deviate from the dominant template. Deliberately use abrupt-end, evidence-first, or causal-chain structures for some paragraphs.
15Perfect transitions between every section and paragraphAI writes seamless logical flow. Humans skip transitions, jump topics, leave gaps. Zero logical breaks = AI fingerprint.Deliberately insert 2-4 "logic breaks" in the paper where the next paragraph opens without any transitional sentence from the previous one. Distribute across section boundaries.

Caveats

Before every use, keep these in mind:

  1. This is a reference, not a recipe. The patterns are what JF authors actually do; your paper has its own voice and argument. Use the skill to diagnose what's missing, not to replace your judgment.

  2. Variation matters. A paper that uses every pattern from this skill will read as mechanical. Each real JF paper deploys only a subset.

  3. Check your target journal. ALL CAPS openings, extensive enumeration, and "The remainder of the paper is organized as follows" are common in JF but may be inappropriate for JPE, QJE, or Econometrica.

  4. The best JF writing is invisible. The goal is not to show off how many templates you've mastered — it's to make your argument clear, precise, and persuasive.


Version History

VersionDateChanges
3.1.02026-06-05Three-tier AI-fingerprint defense: upgraded Step 5 from 7-item scan to 12-marker phrase scan + 5-question structural audit + 5-metric quantitative verification. Expanded anti-patterns from 8 to 15 entries (symmetry patterns 9-15 from structure-randomizer/deep-humanize). Added Caveats M-R to usage-guide.md covering: symmetry problem, high-trigger patterns with fixes, quantitative audit metrics, imperfect writing as defense, section-specific risks, 2-minute pre-submission check.
3.0.02026-06-05Ground-up modular rewrite. Split ~2000-line monolithic SKILL.md into orchestrator + 5 reference files. Added trigger conditions table, mode selection guide, anti-patterns table, AI-fingerprint quality standards.
2.x2026-06-04Round 4 additions (Sections 62-90), AI-fingerprint warnings, caveats J-L
2.x2026-06-03Round 3 quality audit, caveats A-I
1.x2026-05Initial creation from 124 JF papers

Synthesized from 144 Journal of Finance papers (2022--2025). All examples verbatim from published JF articles.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Jf Writing Style Guide AI skill do?

JF writing style reference for polishing academic prose and Chinese-to-English translation in Journal of Finance style. Triggers on: JF风格, JF润色, jf-skill, JF写作, JF polish, JF translate. v3.1 adds three-tier AI-fingerprint defense.

Why use Jf Writing Style Guide on TypingMind?

Because you install it once and use it with any model. Jf Writing Style Guide is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Jf Writing Style Guide in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Ryyyyyyy233/JF-Writing-Skill/tree/main/jf-skill-1.0.0. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Jf Writing Style Guide?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Jf Writing Style Guide?

As many as you like. As long as a model supports skills, you can use Jf Writing Style Guide with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Jf Writing Style Guide AI skill free?

Yes. It is published on GitHub by Ryyyyyyy233 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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