Borrowing it
Nothing to install: this file belongs to AlessandroCaforio/Academic-Writing. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/AlessandroCaforio/Academic-Writing/main/.claude/skills/thesis-excellence/SKILL.mdgit clone --depth 1 https://github.com/AlessandroCaforio/Academic-WritingWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/alessandrocaforio/academic-writing/thesis-excellence)<a href="https://agentmods.dev/skills/alessandrocaforio/academic-writing/thesis-excellence"><img src="https://agentmods.dev/badge/skills/alessandrocaforio/academic-writing/thesis-excellence/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/alessandrocaforio/academic-writing/thesis-excellence"><img src="https://agentmods.dev/badge/skills/alessandrocaforio/academic-writing/thesis-excellence.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00022 | $0.00748 |
| Opus 5 | $0.00011 | $0.00374 |
| Sonnet 5 | $0.00004 | $0.00150 |
| Haiku 4.5 | $0.00002 | $0.00075 |
Grade A, and why
thesis-excellence scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Thesis Excellence Review
Orchestrate a comprehensive multi-agent review of the thesis. The argument specifies the scope.
Scope Options
all— Review the entire thesis (all written chapters)04_methodology/05_results/ etc. — Review a specific chaptercode— Review all analysis notebookspre-submit— Full pre-submission check (everything)
Orchestration Procedure
Step 1: Compile
Run tectonic to verify compilation:
cd /Users/alessandro/Projects/Tesi/thesis && tectonic main.tex 2>&1
If compilation fails, report the error and stop.
Step 2: Determine Agents to Dispatch
Based on the scope, select which agents to run:
| Scope | Agents |
|---|---|
Any .tex chapter |
proofreader, latex-auditor, notation-checker |
| Ch. 4 or Ch. 5 | + causal-inference |
Chapters with \citet/\citep |
+ literature-rag |
code scope |
code-reviewer |
all or pre-submit |
All agents |
Step 3: Run Agents
Use the Task tool to fork to each agent in parallel where possible. For each agent:
- proofreader: Read the chapter(s) and produce a proofreading report
- latex-auditor: Compile and check cross-references, citations, formatting
- notation-checker: Check all math against the notation registry
- causal-inference: (only for Ch. 4/5) Deep methodology review
- literature-rag: (only for chapters with citations) Verify top claims via RAG
- code-reviewer: (only for code scope) Review notebooks
Step 4: Synthesize Report
Combine all agent reports into a unified assessment:
THESIS EXCELLENCE REPORT
=========================
Scope: [what was reviewed]
Date: [date]
COMPILATION: [PASS/FAIL]
AGENT REPORTS:
├── Proofreader: [summary + issue count]
├── LaTeX Auditor: [summary + issue count]
├── Notation Checker: [summary + issue count]
├── Causal Inference: [summary + top recommendation]
├── Literature RAG: [summary + verification rate]
└── Code Reviewer: [summary + issue count]
QUALITY ASSESSMENT:
├── Clarity: X/10
├── Rigor: X/10
├── Consistency: X/10
├── Completeness: X/10
└── Presentation: X/10
Overall: X.X/10
TOP 5 PRIORITY ACTIONS:
1. [highest priority improvement]
2. [second priority]
3. [third priority]
4. [fourth priority]
5. [fifth priority]
DETAILED FINDINGS:
[Include full agent reports below, organized by agent]
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 93 lines · 22 tokens per session scan A dac929f9466e
thesis-excellence is a skill published in the GitHub repository AlessandroCaforio/Academic-Writing (17 stars, last pushed 5mo ago), licensed MIT. It adds 22 tokens to every session and 748 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
fin-paper-convert
Compile LaTeX to PDF and convert to target journal format.
fin-paper-plan
Generate structured paper outline adapted to target journal.
proofcheck
A systematic checker for mathematical proofs in statistics and machine-learning research papers.
theory-simulation
Bridge between theoretical results and Monte Carlo simulation, built to top-stat-journal standards (AoS, JASA, JRSS-B, Biometrika, Bernoulli). Two modes: (1) DESIGN mode — for each theoretical claim, design new simulations that verify rates, coverage, stress-test assumptions, and reveal theory-improvement…
proof-repair
Generate self-consistent repair plans for mathematical proof issues found by /proofcheck, with literature-backed support. For each problematic assumption, model, proposition, or theorem, proposes fixes that preserve the full dependency chain and searches arXiv, Semantic Scholar, and Google Scholar for new references…
theory-sharpen
A framework for testing whether a research paper’s theoretical results can be made stronger, such as by using fewer assumptions or proving faster rates. It also checks whether the theory matches the model, experiments, and existing research.