Thesis Skills v3.5.0

Deterministic thesis workflow tools for citation sync, format checks, review handoff, and pre-submission readiness.

Spend your time thinking, not fixing formatting.

中文文档 · English · Showcase

What's New · Quickstart · Outputs · Scenarios · Updating · Rule Packs · Creating Your Own · Boundaries

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What is this?

Thesis Skills is not an AI writing assistant, not a thesis template, and not a tool that writes thesis content for you.

It is a CLI workflow system that connects the tools many graduate students and researchers already use: Word, Zotero, EndNote, LaTeX, structured check reports, safe fix patches, review handoff artifacts, and pre-submission readiness checks.

                  ┌───────────────────────────────────────────┐
Zotero / EndNote ─┤                                           ├─→ LaTeX thesis
Word .docx ───────┤              Thesis Skills                ├─→ Review Word export
LaTeX project ────┤                                           ├─→ Defense pack
                  └───────────────────────────────────────────┘
                                      │
                                      ▼
                 check reports → dry-run fixes → readiness gate

The goal is simple: turn scattered, manual, error-prone thesis finishing work into a workflow that is checkable, repeatable, and auditable.

For repetitive finishing work, the expected time savings are concrete:

Workflow	Manual baseline	With Thesis Skills	Speedup
Bibliography intake	30-60 min	2-5 min	~10× faster
Word ↔ LaTeX review handoff	1-3 hrs	5-10 min	~15× faster
Deterministic format checks	1-3 hrs	2-5 min	~20× faster
Safe report-driven fixes	1-2 hrs	5-10 min	~10× faster
Pre-submission readiness review	30-60 min	1-2 min	~30× faster
Defense prep inventory	2-4 hrs	10-15 min	~15× faster

Time savings are conservative estimates for repetitive formatting and handoff work. Thesis Skills does not replace writing, thinking, advisor judgment, or institutional confirmation.

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What's new in v3.5.0

• Bounded auto-fix for final-audit findings: new fixers for final cleanup residue, statistical notation normalization, manual anchor insertion, and unused bibliography entry removal. All fixers are preview-first and only apply low-risk, reversible edits.
• Final delivery workflow: 33-final-delivery/run_final_delivery.py orchestrates evidence generation, final-audit checks, optional bounded fixes, and bundle rebuild in one command.
• Fix cycle integration: run_fix_cycle.py now supports final-audit and all modes for running the new fixers.
• Report navigation polish (from v3.4.1): local HTML report surfaces now cross-link index, final-audit, reference-ledger, claim-citation, readiness, and raw JSON / CSV artifacts more consistently.
• Support-risk heuristic calibration (from v3.4.1): possible_overclaim no longer duplicates possible_topic_mismatch when a PASS reference is already flagged by the more specific topic-mismatch signal.
• V3.4 final-audit surfaces remain in place: deterministic final cleanup, statistical consistency, manual-anchor checks, final-audit JSON aggregation, reference-audit ledger, and static HTML report surfaces.
• Static local report UX: reports/index.html, reports/final-audit-report.html, reports/reference-audit-ledger.html, and reports/claim-citation-triage.html make JSON / CSV artifacts easier to review without replacing them as source of truth.
• V3.3 reference verification hardening remains in place: final reference set parsing, DOI candidates, URL verification, scoped/resumable external verification, and the unified evidence pipeline runner run_evidence_pipeline.py.

v3.3 highlights recap

• Readiness Gate Integration with the four-layer citation evidence pipeline remains in place; V3.4 built on top of that with final-audit and local HTML report surfaces.

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Quickstart

Run the built-in sample project through the check pipeline:

git clone https://github.com/quzhiii/thesis-skills.git
cd thesis-skills

test -d examples/minimal-latex-project

python run_check_once.py \
  --project-root examples/minimal-latex-project \
  --ruleset university-generic \
  --skip-compile

Expected result: JSON reports are written to examples/minimal-latex-project/reports/, including run-summary.json and readiness-report.json, without requiring a local LaTeX installation.

If you already have a LaTeX thesis project:

python run_check_once.py \
  --project-root thesis \
  --ruleset university-generic \
  --skip-compile

More details: docs/quickstart.md.

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Outputs

Hero workflow

1. Intake        2. Check           3. Fix safely        4. Gate          5. Handoff
──────────       ───────────        ─────────────        ─────────        ─────────────
Zotero           references         dry-run patches      PASS             advisor Word
EndNote     →    language      →    preview first   →    WARN       →     review TODOs
Word/LaTeX       format            apply explicitly      BLOCK            defense pack

Readiness gate preview

┌──────────────────────────────────────────────────────────────┐
│ Readiness verdict: WARN                                      │
├───────────────────────┬────────┬─────────────────────────────┤
│ Dimension             │ Status │ Why it matters              │
├───────────────────────┼────────┼─────────────────────────────┤
│ References            │ PASS   │ all cite keys resolve       │
│ Language              │ WARN   │ 2 style warnings remain     │
│ Format                │ PASS   │ labels and refs are stable  │
│ Compile evidence      │ WARN   │ skipped in demo mode        │
│ Export evidence       │ WARN   │ not produced by smoke test  │
│ Review-loop evidence  │ WARN   │ not produced by smoke test  │
└───────────────────────┴────────┴─────────────────────────────┘

Next actions:
1. Review reports/check_language-report.json
2. Generate Word export / review-loop artifacts when those handoffs are needed
3. Re-run without --skip-compile before final submission

The baseline run_check_once.py command writes machine-readable artifacts such as:

• reports/check_bib_quality-report.json
• reports/check_references-report.json
• reports/citation-integrity-report.json
• reports/citation-integrity-report.md
• reports/citation-issues.csv
• reports/check_language-report.json
• reports/check_language_deep-report.json
• reports/check_format-report.json
• reports/check_content-report.json
• reports/readiness-report.json
• reports/run-summary.json

Optional final-audit foundation artifact:

• reports/final-cleanup-report.json from 23-check-final-cleanup/check_final_cleanup.py
• reports/statistical-consistency-report.json from 25-check-statistical-consistency/check_statistical_consistency.py
• reports/manual-anchor-report.json from 26-check-manual-anchor/check_manual_anchor.py
• reports/final-audit-report.json from 27-final-audit-report/build_final_audit_report.py
• reports/reference-audit-ledger.csv from 28-reference-audit-ledger/build_reference_audit_ledger.py
• reports/index.html from 29-report-index/build_report_index.py
• reports/final-audit-report.html from 30-final-audit-html/build_final_audit_html.py
• reports/reference-audit-ledger.html from 31-reference-ledger-html/build_reference_audit_ledger_html.py
• reports/claim-citation-triage.html from 32-claim-citation-html/build_claim_citation_html.py

Bounded auto-fix modules (preview-first, low-risk only):

• 23-fix-final-cleanup/fix_final_cleanup.py — remove exact process markers (TODO, FIXME, ???, draft, debug, \textcolor{blue})
• 25-fix-statistical-consistency/fix_statistical_consistency.py — normalize non-dominant statistical notation when dominant style is unambiguous
• 26-fix-manual-anchor/fix_manual_anchor.py — insert \phantomsection before flagged \addcontentsline entries
• 28-fix-reference-audit-ledger/fix_reference_audit_ledger.py — remove truly unused bibliography entries (is_unused_bib_entry=true, is_cited_in_tex=false, is_final_reference=false)

Final delivery workflow:

• 33-final-delivery/run_final_delivery.py — orchestrate evidence generation, final-audit checks, optional bounded fixes, and bundle rebuild

The optional v3.3 evidence pipeline writes the citation evidence artifacts:

• reports/final-reference-set-report.json
• reports/final-reference-set-report.csv
• reports/external-verification-report.json when external verification is not skipped
• reports/missing-doi-candidates.json when external verification is not skipped
• reports/missing-doi-candidates.csv when external verification is not skipped
• reports/url-verification-report.json when external verification is not skipped
• reports/url-verification-flagged.csv when external verification is not skipped
• reports/hallucination-risk-report.json
• reports/high-risk-references.csv
• reports/claim-citation-triage-report.json
• reports/claim-citation-triage.md
• reports/claim-citation-triage.csv

Example JSON snippets and demo walkthroughs: docs/examples.md.

Citation Integrity preview

The current v3.5.0 release line keeps local Citation Integrity as the first layer of pre-submission reference checking:

References: BLOCK
- cited keys missing from bibliography files
- duplicate citation keys with conflicting metadata
- DOI/year field warnings
- LaTeX undefined-citation warnings from local compile logs

Boundary: the current Citation Integrity workflow only checks local citation integrity. It does not query external databases and never auto-inserts or rewrites citations. Use the external verification and hallucination risk layers for evidence-based screening.

External Verification (v2.0.0)

An optional external metadata verification layer queries CrossRef, OpenAlex, and Semantic Scholar for each bibliography entry and writes reports/external-verification-report.json.

Use this when you want a fast authenticity screen for AI-drafted or suspicious-looking references before a manual final check.

python 18-verify-references/verify_external_references.py \
  --project-root thesis \
  --ruleset university-generic

Or via the existing reference checker with an explicit flag:

python 10-check-references/check_references.py \
  --project-root thesis \
  --ruleset university-generic \
  --with-external-verification

V2.0 boundaries:

• Providers: CrossRef, OpenAlex, and Semantic Scholar.
• No readiness gate blocking from the local References dimension.
• external_verification is advisory only.
• No automatic citation rewriting.
• Network failures degrade to UNAVAILABLE, never crash.

Final Reference Set + DOI / URL checks (v3.3.0)

V3.3 hardens citation evidence by separating three scopes:

• final: references that actually entered the compiled bibliography via .aux / .bbl
• cited: citation keys extracted from TeX source \cite{} commands
• all: every entry in active .bib files

The final reference set builder writes:

• reports/final-reference-set-report.json
• reports/final-reference-set-report.csv

The external verification layer can now resume long runs and write partial results safely:

python 18-verify-references/verify_external_references.py \
  --project-root thesis \
  --ruleset university-generic \
  --scope final \
  --resume

V3.3 also adds advisory follow-up reports:

• reports/missing-doi-candidates.json and .csv for likely DOI additions
• reports/url-verification-report.json and reports/url-verification-flagged.csv for URL resolution checks

Boundaries:

• No LLM usage.
• No automatic DOI write-back to .bib files.
• No automatic URL replacement.
• URL verification checks whether a URL resolves; it does not judge document authenticity.

Hallucination Risk (v3.0.0)

Score each bibliography entry for hallucination risk using local metadata and optional external verification evidence. The hallucination risk scorer reads reports/external-verification-report.json if present and writes reports/hallucination-risk-report.json plus reports/high-risk-references.csv.

python 19-check-hallucination-risk/check_hallucination_risk.py \
  --project-root thesis \
  --ruleset university-generic

Risk labels:

Label	Meaning
PASS	Multi-source match with consistent metadata
WARN	Entry exists but fields differ noticeably
REVIEW	Possible match but evidence is weak
HIGH_RISK	No credible match found in enabled databases
UNSUPPORTED	Chinese-language or non-standard entry that cannot be auto-verified

V3.0 boundaries:

• No LLM usage. Scoring is deterministic based on local metadata and external verification evidence.
• No automatic citation or bibliography rewriting.
• No live network calls. Reads external-verification-report.json if present.
• UNSUPPORTED means "cannot be automatically judged by enabled evidence," not "safe."
• HIGH_RISK means "manual verification strongly recommended," not "fake."

Claim-Citation Support Triage (v3.1.0)

Extract the sentence surrounding each \cite{} command from .tex files and pair it with cited bibliography metadata and V3.0 hallucination risk data. Produce deterministic triage labels that help identify claim-citation pairs that may lack credible structural support — without LLM.

python 20-check-claim-citation/check_claim_citation.py \
  --project-root thesis \
  --ruleset university-generic

Triage labels:

Label	Meaning
WELL_SUPPORTED	Cited reference PASS in V3.0, complete metadata, substantive context
SUPPORTED	Reference PASS/WARN in V3.0, minor risk signals
WEAK	Reference REVIEW in V3.0, or vague context, or incomplete metadata
ORPHANED	Citation key not found in bibliography files
UNVERIFIABLE	Cited reference UNSUPPORTED in V3.0 (CJK, thesis type)

The report also includes a backward-compatible support-review layer: claim_type, support_review_label, support_review_reason, support_signals, risk_signals, cluster_keys, cluster_risk_summary, and next_actions. These fields explain why a pair or grouped citation cluster deserves manual review; they do not replace the original triage_label or make final truth claims. Local lexical evidence can use title, abstract, and keyword token overlap when those .bib fields are present. Conservative risk signals such as possible_topic_mismatch, possible_outdated_support, and possible_overclaim are advisory prompts for human review, not automatic judgments. The JSON/Markdown reports may also include advisory citation_needed_candidates for uncited high-assertion sentences; these are manual review prompts, not blocking findings.

V3.1 boundaries:

• No LLM usage. Scoring is deterministic based on V3.0 risk labels, metadata, context quality, grouping, and citation frequency.
• No semantic similarity between claim text and reference content.
• No automatic citation rewrite or suggestion.
• Reads reports/hallucination-risk-report.json if present; treats missing it conservatively.
• Exit code 1 when any pair is ORPHANED.

Final Cleanup Checker

Before final PDF or submission handoff, scan LaTeX sources for process residue such as TODO, FIXME, ???, \textcolor{blue}, \color{blue}, draft, debug, and Chinese review notes like 待修改 or 待核查.

python 23-check-final-cleanup/check_final_cleanup.py \
  --project-root thesis \
  --ruleset university-generic

Output: reports/final-cleanup-report.json. This checker is report-only: it does not delete markers, rewrite prose, or change source files. The final-audit workflow can aggregate this JSON into reports/final-audit-report.json and render it through reports/final-audit-report.html and the local report index.

Statistical Consistency Checker

Before final submission, scan for mixed statistical notation such as p值/P值, p=/P=, 95%CI/95\%CI/95%置信区间, Bootstrap/自助法, and SMD/标准化均数差.

python 25-check-statistical-consistency/check_statistical_consistency.py \
  --project-root thesis \
  --ruleset university-generic

Output: reports/statistical-consistency-report.json. The checker reports the dominant style in the current project and flags deviations; it does not force a universal notation preference or rewrite source files.

Manual Anchor Checker

If the project uses manual contents entries, scan for \addcontentsline commands that may be missing a nearby preceding \phantomsection anchor.

python 26-check-manual-anchor/check_manual_anchor.py \
  --project-root thesis \
  --ruleset university-generic

Output: reports/manual-anchor-report.json. The checker reports likely TOC / LOF / LOT hyperlink-jump risks, but it does not repair labels, captions, numbering, figures, tables, or references.

Final Audit Report

After generating the source-of-truth JSON reports, aggregate them into a single final-audit handoff artifact:

python 27-final-audit-report/build_final_audit_report.py \
  --project-root thesis \
  --ruleset university-generic

Output: reports/final-audit-report.json. This report imports existing JSON evidence and groups dimensions, blockers, warnings, next actions, and source links. It does not rerun checks, call external services, modify thesis sources, or replace the raw JSON reports.

Reference Audit Ledger

For spreadsheet review and advisor/service handoff, aggregate existing reference evidence into one CSV ledger:

python 28-reference-audit-ledger/build_reference_audit_ledger.py \
  --project-root thesis \
  --ruleset university-generic

Output: reports/reference-audit-ledger.csv. The ledger preserves source-specific statuses from local citation integrity, final reference set, external verification, DOI candidates, URL verification, and hallucination-risk reports. It does not edit .bib, insert DOI values, replace URLs, call external services, or treat NO_CANDIDATE as fake.

Static Report Index

Generate a local HTML landing page for the reports directory:

python 29-report-index/build_report_index.py \
  --project-root thesis

Output: reports/index.html. This page links available JSON / CSV artifacts and shows present / missing / unreadable counts. It is a local reading surface only; JSON and CSV remain the source of truth.

Final Audit HTML

Generate a readable local detail page for the aggregated final-audit JSON:

python 30-final-audit-html/build_final_audit_html.py \
  --project-root thesis

Output: reports/final-audit-report.html. This static page is generated from final-audit-report.json and shows the overall verdict, KPI row, dimension matrix, issues, next actions, and source links. JSON remains authoritative.

Reference Audit Ledger HTML

Generate a readable local detail page for the reference-audit CSV ledger:

python 31-reference-ledger-html/build_reference_audit_ledger_html.py \
  --project-root thesis

Output: reports/reference-audit-ledger.html. This static page is generated from reference-audit-ledger.csv and shows summary stats, scope slices, citation-key groupings, and the full ledger table. CSV remains authoritative.

Claim-Citation HTML

Generate a readable local detail page for claim-citation support review:

python 32-claim-citation-html/build_claim_citation_html.py \
  --project-root thesis

Output: reports/claim-citation-triage.html. This static page is generated from claim-citation-triage-report.json and shows P0 / P1 / P2 / P3 review groups, issue-card style summaries, citation-needed candidates, uncited references, cluster review details, support/risk signals, and next actions. It also deep-links the local readiness / references / claim-citation / final-audit report surfaces so reviewers can move between summary, reference ledger, and detail pages. The HTML is a mobile-readable local HTML bundle for manual review. JSON / CSV remain the source of truth; CSV / Markdown outputs remain unchanged.

Scenarios

1. I just switched from Word to LaTeX

python 00-bib-zotero/sync_from_word.py \
  --project-root thesis \
  --word-file thesis.docx \
  --apply

python run_check_once.py \
  --project-root thesis \
  --ruleset university-generic \
  --skip-compile

2. I already use LaTeX and want to check my thesis

python run_check_once.py \
  --project-root thesis \
  --ruleset university-generic

python 16-check-readiness/check_readiness.py \
  --project-root thesis \
  --ruleset university-generic \
  --mode advisor-handoff

3. My advisor needs a Word version for review

python 02-latex-to-word/migrate_project.py \
  --project-root thesis \
  --output-file thesis-review.docx \
  --profile review-friendly \
  --apply true

4. I received Word feedback and need to update LaTeX

python 04-word-review-ingest/feedback_ingest.py \
  --project-root thesis \
  --input review-feedback.json

python 03-latex-review-diff/review_diff.py \
  --project-root thesis

5. I am preparing for defense

python 17-defense-pack/generate_outline.py \
  --project-root thesis \
  --ruleset university-generic

python 17-defense-pack/generate_figure_inventory.py \
  --project-root thesis \
  --ruleset university-generic

6. I want to screen AI-generated or suspicious references

python 18-verify-references/verify_external_references.py \
  --project-root thesis \
  --ruleset university-generic

python 19-check-hallucination-risk/check_hallucination_risk.py \
  --project-root thesis \
  --ruleset university-generic

Use this when you want a fast authenticity screen for references drafted by AI or copied from sources you do not fully trust. It produces a hallucination_risk_score per entry and a high-risk-references.csv for manual review, without rewriting the bibliography. Chinese-language references are marked UNSUPPORTED since external databases do not cover them.

More scenarios: docs/examples.md.

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Rule Packs

Rule packs are the most important concept in Thesis Skills: they encode your institution's formatting requirements as structured YAML so the checkers know what counts as "correct" and what counts as an issue.

Built-in Packs

90-rules/packs/
 ├── university-generic/        # Generic university thesis starter (default, permissive)
 ├── journal-generic/           # Generic journal article starter (English, minimal)
 ├── tsinghua-thesis/           # Tsinghua University Master's/PhD thesis pack
 │                              #   First-pass calibrated against 《研究生学位论文写作指南（202503）》
 │                              #   CJK/English rules, figure numbering, and reference defaults tuned to the guide
 └── demo-university-thesis/    # Concrete non-Tsinghua example pack

• university-generic is suitable for most Chinese universities — broad coverage, moderate thresholds.
• tsinghua-thesis is specifically calibrated for Tsinghua students: GB/T 7714 reference style, mixed CJK/English rules per the university writing guide, and Chinese chapter naming conventions. For many Tsinghua thesis projects this works as a direct starting point, but you should still verify against your department template and local requirements.
• journal-generic targets English journal submissions, with CJK-specific rules disabled.

Inside a Rule Pack

Each pack is a folder with three files:

90-rules/packs/your-school/
 ├── pack.yaml      # Metadata: name, kind, version
 ├── rules.yaml     # Rules: what to check, severity, thresholds
 └── mappings.yaml  # File/path mappings (main tex candidates, bib paths)

rules.yaml is organized by dimension:

Section	Controls	Examples
project	Project structure: main tex file names, chapter globs, bib paths	main_tex_candidates, chapter_globs
reference	Citation integrity: missing keys, orphans, duplicates, bib quality	missing_key: error
language	Surface language: CJK/Latin spacing, brackets, punctuation, weak phrases	cjk_latin_spacing, bracket_mismatch
language_deep	Deep language: connectors, collocations, inference strength, boundary signposts	inference_overclaim, boundary_signpost
consistency	Terminology: variant detection for the same concept	terminology_consistency
format	Format structure: figure/table lists, numbering, cross-references	require_list_of_figures
content	Content completeness: required sections, keyword count	required_sections
compile	Compile diagnostics: engine, error categories, severity mapping	engine_hint: xelatex

Creating Your Own School Rule Pack

If you are not a Tsinghua student, or your department/journal has specific requirements, create a custom pack from one of the built-in starters.

Step 1: Scaffold the pack

python 90-rules/create_pack.py \
  --pack-id my-university \
  --display-name "My University Master's Thesis" \
  --starter university-generic \
  --kind university-thesis

This generates three files under 90-rules/packs/my-university/, copied from university-generic as a starting point.

Step 2: Adjust project structure

Edit rules.yaml → project to match your thesis directory layout:

project:
  main_tex_candidates:       # Possible names for your main tex file, in priority order
    - thesis.tex
    - main.tex
  chapter_globs:             # Where chapter files live and their naming pattern
    - chapters/*.tex
  bibliography_files:        # Paths to .bib files
    - ref/refs.bib

Step 3: Tune rules to your school's guide

Check your institutional thesis writing guide and decide rule by rule:

• Keep enabled: Rules that your guide explicitly requires and the checker can reliably detect (e.g., missing citation keys, figure/table numbering)
• Demote: Rules your guide does not mandate — change severity from warning to info (e.g., CJK/Latin spacing if not required)
• Disable: Rules clearly irrelevant to your institution or discipline — set enabled: false (e.g., CJK rules for English-only theses)

Example — demoting CJK spacing when your guide doesn't require it:

# Before
cjk_latin_spacing:
  enabled: true
  severity: warning

# After (school guide does not mandate CJK-Latin spacing)
cjk_latin_spacing:
  enabled: true
  severity: info

Step 4: Update required section names

If your thesis uses Chinese section naming (not English IMRaD), sync the content rules:

content:
  required_sections:
    - Introduction (or 绪论)
    - Literature Review (or 文献综述)
    - Methods (or 研究方法)
    - Conclusion (or 结论)

Step 5: Run checks with your custom pack

python run_check_once.py \
  --project-root thesis \
  --ruleset my-university \
  --skip-compile

Step 6: Validate and iterate

After running, inspect the JSON reports under reports/. If you notice:

• Too many false positives in a category → demote or disable that rule
• Real issues not detected → check if the rule is enabled and severity is set high enough
• Project discovery failed → adjust main_tex_candidates or chapter_globs

Tweak → re-run → review reports. Most packs converge in 1–2 calibration rounds.

Step 7: Export a handoff bundle

After the pack passes lint, export a minimal versioned export bundle for handoff:

python 90-rules/export_pack.py \
  --pack-path 90-rules/packs/my-university \
  --output dist/my-university.zip

This creates a linted ZIP bundle for sharing the pack outside a local checkout. The ZIP includes manifest.json with pack metadata and the lint scorecard summary. There is still no formal registry or publish command.

For non-Tsinghua users: If your calibrated rule pack is stable and you'd like it featured, PRs adding new packs to 90-rules/packs/ are welcome. Future students from your school won't have to start from scratch.

---

Tested on

• Python 3.10+
• Windows / macOS / Linux
• LaTeX optional for the --skip-compile demo; run without --skip-compile when you want compile-log diagnostics

---

Boundaries

Thesis Skills is	Thesis Skills is not
A bridge between Word, Zotero, EndNote, and LaTeX	A thesis template or document class
A deterministic checker for formatting and structural rules	An AI writing assistant that generates thesis content
A report-driven workflow with dry-run previews	A replacement for Grammarly or other prose editors
A pre-submission readiness gate	An automatic final defense PPT generator
Extensible through institution-specific rule packs	A guarantee that every school or journal rule is already supported
CLI-based with auditable artifacts	A GUI or web-based editor

---

Documentation

Document	Purpose
docs/quickstart.md	Minimal install and first check run
docs/examples.md	Output previews and scenario examples
docs/modules.md	Full module reference moved out of the README
docs/architecture.md	Workflow and module architecture
docs/getting-started-zh.md	Step-by-step beginner guide in Chinese
CHANGELOG.md	Release history

---

Release history

• v3.4.1: polished cross-report HTML navigation and calibrated duplicate support-risk signals.
• v3.4.0: added final-audit report surfaces, reference-audit ledger HTML, and conservative claim-citation support-risk signals.
• v3.3.0: hardened reference verification with final reference set parsing, resumeable external verification, DOI candidate suggestions, and URL verification.
• v3.2.0: integrated hallucination risk and claim-citation triage into readiness gate, added unified evidence pipeline runner, run_evidence_pipeline.py.
• v3.1.0: added claim-citation support triage, claim-citation-triage-report.json, deterministic triage scoring, and three demo projects.
• v3.0.0: added hallucination risk scoring, hallucination-risk-report.json, high-risk-references.csv, Chinese-language UNSUPPORTED handling, and three demo projects.
• v2.0.0: added CrossRef / OpenAlex / Semantic Scholar external verification, consensus candidates, and an external_verification readiness advisory.
• v1.0.0: stabilized the public workflow story across README, roadmap, site, examples, and code paths.
• v1.1.0: added the local-first Citation Integrity engine and readiness integration.
• v1.2.0: added Markdown/CSV Citation Integrity outputs, clean/broken demos, and public version-line alignment.
• See CHANGELOG.md for the full changelog.

---

Updating your local copy

Downloading or cloning the repository once does not make future updates appear automatically on your machine.

Choose the update path that matches how you got Thesis Skills:

If you cloned with Git

Run:

git pull origin main

This fetches the newest committed changes from GitHub into your local checkout.

If you want to see what changed before pulling:

git fetch origin
git log --oneline HEAD..origin/main

If you downloaded a ZIP

A ZIP download is just a snapshot. It will not sync by itself.

To get updates, either:

1. download a fresh ZIP from GitHub and replace your local copy manually, or
2. switch to a Git clone so future updates only need git pull

If you edited the repository locally

Pulling new changes is easiest when your local copy has no uncommitted edits.

Before updating, check:

git status

If you have local modifications, commit or back them up first so git pull does not create conflicts unexpectedly.

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Module reference

The long module table lives in docs/modules.md so this README stays focused on the product workflow.

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Template recommendations

Thesis Skills is designed to work alongside mature templates and institution-specific document classes.

Institution	Template repository
Tsinghua University	tuna/thuthesis
Shanghai Jiao Tong University	sjtug/SJTUThesis
University of Science and Technology of China	ustctug/ustcthesis
Peking University	CasperVector/pkuthss
Stanford University	dcroote/stanford-thesis-example
University of Cambridge	cambridge/thesis

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Acknowledgments

Special thanks to tuna/thuthesis and other open-source thesis template projects. These projects make high-quality LaTeX thesis writing more accessible and inspired the workflow design of Thesis Skills.

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License

MIT License