Typst academic paper assistant for existing `.typ` paper projects in English or Chinese. Use this skill whenever the user wants to compile, audit, or improve a Typst paper, including format checks, bibliography validation for BibTeX or Hayagriva, grammar/sentence/logic review, expression polishing, translation, title optimization, pseudocode review, algorithm block cleanup, de-AI editing, experiment-section review, table structure validation, three-line table generation, abstract structure diagnosis, or journal adaptation. Trigger even when the user only mentions one Typst file, one bibliography issue, one pseudocode block, one section rewrite, "three-line table", "check abstract", or "reformat for another journal". Also trigger when the user mentions ".typ files", "typst compile error", "typst export", "typst bibliography", `algorithm-figure`, `lovelace`, or `algorithmic` even without saying the word "Typst" explicitly.
---
name: typst-paper
description: Typst academic paper assistant for existing `.typ` paper projects in English or Chinese. Use this skill whenever the user wants to compile, audit, or improve a Typst paper, including format checks, bibliography validation for BibTeX or Hayagriva, grammar/sentence/logic review, expression polishing, translation, title optimization, pseudocode review, algorithm block cleanup, de-AI editing, experiment-section review, table structure validation, three-line table generation, abstract structure diagnosis, or journal adaptation. Trigger even when the user only mentions one Typst file, one bibliography issue, one pseudocode block, one section rewrite, "three-line table", "check abstract", or "reformat for another journal". Also trigger when the user mentions ".typ files", "typst compile error", "typst export", "typst bibliography", `algorithm-figure`, `lovelace`, or `algorithmic` even without saying the word "Typst" explicitly.
metadata:
category: academic-writing
tags: [typst, paper, chinese, english, ieee, acm, springer, neurips, compilation, grammar, bibliography, hayagriva, pseudocode, algorithmic, lovelace]
argument-hint: "[main.typ] [--section SECTION] [--module MODULE]"
allowed-tools: Read, Glob, Grep, Bash(uv *), Bash(typst *)
---
# Typst Academic Paper Assistant
Use this skill for targeted work on an existing Typst paper project. Route requests to the smallest useful module and keep outputs compatible with Typst source review.
## Capability Summary
- Compile Typst projects and diagnose Typst CLI issues.
- Audit format, bibliography, grammar, sentence length, argument logic, expression quality, and AI traces.
- Review IEEE-like pseudocode blocks for `algorithmic`, `algorithm-figure`, `lovelace`, caption handling, and comment length.
- Support both BibTeX and Hayagriva bibliography files.
- Improve titles, translation, and experiment-section clarity for Typst papers.
## Triggering
Use this skill when the user has an existing `.typ` paper project and wants help with:
- Typst compilation or export issues
- format or venue compliance
- bibliography validation for BibTeX or Hayagriva
- grammar, sentence, logic, or expression review
- translation or bilingual polishing
- title optimization
- pseudocode and algorithm-block review
- de-AI editing
- experiment-section review
## Do Not Use
Do not use this skill for:
- LaTeX-first conference or thesis projects
- DOCX/PDF-only editing without Typst source
- thesis template detection or GB/T 7714 thesis workflows
- from-scratch paper planning or literature research
- multi-perspective review, scoring, or submission gate decisions (use `paper-audit`)
- standalone pseudocode drafting without a paper context
## Module Router
| Module | Use when | Primary command | Read next |
| --- | --- | --- | --- |
| `compile` | Typst build, export, font, or watch issues | `uv run python $SKILL_DIR/scripts/compile.py main.typ` | `references/modules/COMPILE.md` |
| `format` | Venue/layout review for a Typst paper | `uv run python $SKILL_DIR/scripts/check_format.py main.typ` | `references/modules/FORMAT.md` |
| `bibliography` | BibTeX or Hayagriva validation | `uv run python $SKILL_DIR/scripts/verify_bib.py references.bib --typ main.typ` | `references/modules/BIBLIOGRAPHY.md` |
| `grammar` | Grammar cleanup on Typst prose | `uv run python $SKILL_DIR/scripts/analyze_grammar.py main.typ --section introduction` | `references/modules/GRAMMAR.md` |
| `sentences` | Long or dense sentence diagnostics | `uv run python $SKILL_DIR/scripts/analyze_sentences.py main.typ --section introduction` | `references/modules/SENTENCES.md` |
| `logic` | Argument flow, introduction funnel, cross-section closure, and abstract/conclusion alignment review | `uv run python $SKILL_DIR/scripts/analyze_logic.py main.typ --section methods` | `references/modules/LOGIC.md` |
| `expression` | Tone and expression polishing | `uv run python $SKILL_DIR/scripts/improve_expression.py main.typ --section methods` | `references/modules/EXPRESSION.md` |
| `translation` | Chinese/English academic translation in Typst context | `uv run python $SKILL_DIR/scripts/translate_academic.py input_zh.txt --domain deep-learning` | `references/modules/TRANSLATION.md` |
| `title` | Generate, compare, or optimize Typst paper titles | `uv run python $SKILL_DIR/scripts/optimize_title.py main.typ --check` | `references/modules/TITLE.md` |
| `pseudocode` | Review `algorithmic` / `algorithm-figure` / `lovelace` output for IEEE-like safety, captions, and comment length | `uv run python $SKILL_DIR/scripts/check_pseudocode.py main.typ --venue ieee` | `references/modules/PSEUDOCODE.md` |
| `deai` | Reduce English or Chinese AI-writing traces while preserving Typst syntax | `uv run python $SKILL_DIR/scripts/deai_check.py main.typ --section introduction` | `references/modules/DEAI.md` |
| `experiment` | Inspect experiment-section clarity, discussion layering, and reporting quality | `uv run python $SKILL_DIR/scripts/analyze_experiment.py main.typ --section experiment` | `references/modules/EXPERIMENT.md` |
| `tables` | Table structure validation, three-line table generation | `uv run python $SKILL_DIR/scripts/check_tables.py main.typ` | `references/modules/TABLES.md` |
| `abstract` | Abstract five-element structure diagnosis and word count validation | `uv run python $SKILL_DIR/scripts/analyze_abstract.py main.typ` | `references/modules/ABSTRACT.md` |
| `adapt` | Journal adaptation: reformat paper for a different venue | (LLM-driven workflow) | references/modules/ADAPT.md |
## Required Inputs
- `main.typ` or the Typst entry file.
- Optional `--section SECTION` for targeted analysis.
- Optional bibliography path when the request targets references.
- Optional venue context when the user cares about IEEE, ACM, Springer, or similar expectations.
If arguments are missing, ask only for the Typst entry file and the target module.
## Output Contract
- Return findings in Typst diff-comment style whenever possible: `// MODULE (Line N) [Severity] [Priority]: Issue ...`
- Report the exact command used and the exit code when a script fails.
- Preserve `@cite`, `<label>`, math blocks, and Typst macros unless the user explicitly asks for source edits.
## Workflow
1. Parse `$ARGUMENTS` and select the active module.
2. Read only the reference file needed for that module.
3. Run the module script with `uv run python ...`.
4. Return Typst-ready comments and next actions.
5. For bibliography requests, decide BibTeX vs Hayagriva first, then run `bibliography`.
## Safety Boundaries
- Never invent citations, labels, or experimental claims.
- Never rewrite Typst references, labels, or math content by default.
- Keep compile diagnostics separate from prose rewrites so the user can validate each step.
## Reference Map
- `references/TYPST_SYNTAX.md`: Typst syntax reminders and pitfalls.
- `references/STYLE_GUIDE.md`: paper-writing style baseline.
- `references/CITATION_VERIFICATION.md`: citation verification workflow.
- `references/modules/`: module-specific Typst commands and choices.
- `references/modules/PSEUDOCODE.md`: IEEE-like defaults for Typst pseudocode.
Read only the file that matches the active module.
## Example Requests
- “Compile this Typst paper and tell me why the export works locally but fails in CI.”
- “Check bibliography, title, and abstract wording in my `main.typ` submission.”
- “Review this `algorithm-figure` block for IEEE-like caption, line-number, and comment issues.”
- “Review the methods section for sentence length and logic, but keep Typst labels intact.”
See `examples/` for full request-to-command walkthroughs.
When Claude will use it
Use this skill whenever the user wants to compile, audit, or improve a Typst paper, including format checks, bibliography validation for BibTeX or Hayagriva, grammar/sentence/logic review, expression polishing, translation, title optimization, pseudocode review, algorithm block cleanup, de-AI editing, experiment-section review, table structure validation, three-line table generation, abstract structure diagnosis, or journal adaptation.
---
name: auto-empirical-research-skills
description: Route empirical-research requests through the Auto-Empirical Research Skills catalog when this whole repository is installed as one skill in Codex, CodeBuddy, Claude Code, or another IDE. Use to choose and load the right vendored AERS skill for causal inference, econometrics, replication, data acquisition, manuscript writing, peer review and referee responses, citation checking, de-AIGC editing, or full empirical-paper workflows, or an end-to-end run from raw data all the way to a finished Word (.docx) manuscript, without reading the entire repository at once.
---
---
name: Full-empirical-analysis-skill
description: Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy + statsmodels + linearmodels + pyfixest + rdrobust + econml + causalml + matplotlib/seaborn. **Defaults to economics empirical-paper style** (AER / QJE / AEJ) — every run produces a publication-ready output set with a multi-column regression table (M1→M6 progressive controls/FE) as the centerpiece, plus Table 1 (descriptives), mechanism / heterogeneity / robustness tables, and event-study + coefficient + trend figures. Covers the full 8-step pipeline an applied economist or quantitative social scientist runs on every paper — (1) data cleaning, (2) variable construction & transformation, (3) descriptive statistics & Table 1, (4) statistical diagnostic tests, (5) baseline empirical modeling, (6) robustness battery, (7) further analysis (mechanism, heterogeneity, mediation, moderation), (8) publication-ready tables & figures. **Also covers two parallel domain modes that share the same 8-step scaffolding** — **Mode A — Epidemiology / public health** (target-trial emulation via `zepid` / hand-rolled `pandas`, IPTW + g-formula + TMLE doubly-robust triplet via `zepid` / `econml` / `lifelines`, Mendelian randomization via `pymr` / `mrtool` (or `rpy2` → `MendelianRandomization`/`TwoSampleMR`), KM / AFT / Cox survival via `lifelines`, E-value sensitivity, principal stratification — STROBE / TRIPOD reporting), and **Mode B — ML causal inference** (DML via `econml.dml` / `doubleml`, S/T/X/R/DR meta-learners via `econml.metalearners` / `causalml`, causal forest via `econml.grf` / `causalml`, Dragonnet / TARNet / CEVAE neural causal via `causalml`, BCF via `pymc-bart` / `bcf-py`, matrix completion, CATE distribution + policy tree via `econml.policy` / `policytree-py`, off-policy evaluation, conformal causal via `mapie`, fairness audit via `fairlearn`, DAG learning via `causal-learn` / `cdt` / LLM-assisted). Prescribes which library to reach for at each step, shows the canonical code, and links to deeper `references/` files for variant-specific patterns. Use when the user asks for a **complete empirical analysis** in Python, wants to replicate an applied-economics paper from scratch, needs a reproducible workflow that is NOT opinionated on any single vertical package (contrast with StatsPAI), wants explicit control over every estimator and diagnostic, or asks "how do I write a full empirical pipeline in Python?". Also triggers when the user names a specific classical step in isolation — "winsorize at 1/99%", "run Breusch-Pagan", "build a Table 1 balance table", "do a placebo test", "event study plot", "mediation analysis" — and wants it wired into the broader pipeline. Mode A triggers on "target trial emulation", "IPTW", "TMLE", "Mendelian randomization", "STROBE", "公共健康", "流行病学". Mode B triggers on "DML", "double machine learning", "causal forest", "meta-learner", "Dragonnet", "BCF", "policy tree", "conformal causal", "fairness audit", "因果机器学习".
---
