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/rewrite-literature/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/rewrite-literature)<a href="https://agentmods.dev/skills/alessandrocaforio/academic-writing/rewrite-literature"><img src="https://agentmods.dev/badge/skills/alessandrocaforio/academic-writing/rewrite-literature/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/rewrite-literature"><img src="https://agentmods.dev/badge/skills/alessandrocaforio/academic-writing/rewrite-literature.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 | $0.00028 | $0.07414 |
| Opus 5 | $0.00014 | $0.03707 |
| Sonnet 5 | $0.00006 | $0.01483 |
| Haiku 4.5 | $0.00003 | $0.00741 |
Grade C, and why
rewrite-literature scanned grade C with 1 finding 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 11d 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.
Recursive force deletehighDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
rm -rf /Users/alessandro/Projects/Tesi/thesis/chapters/.litreview_workspace How it starts
The opening of the file, as written. The whole thing — 612 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Literature Review Rewrite Orchestrator
Rewrite Chapter 2 from scratch with deep extraction from ~20 cited papers. Uses a 5-phase pipeline with 7 specialized agents.
Arguments
all— Run the full 5-phase pipeline (default)phase1throughphase5— Run a specific phase (assumes prior phases completed)section N— Draft only section 2.N (for iterating on one section)
Workspace
All intermediate artifacts go in thesis/chapters/.litreview_workspace/. Create it at the start:
mkdir -p /Users/alessandro/Projects/Tesi/thesis/chapters/.litreview_workspace/drafts
Target Structure (~400-500 lines)
Chapter 2: Literature Review
├── Intro paragraph (preview sections using \Cref)
├── 2.1 Automation and the Labor Market
│ ├── 2.1.1 The Task-Based Framework
│ ├── 2.1.2 Labor Market Polarization
│ ├── 2.1.3 Task Displacement and Rent Dissipation
│ └── 2.1.4 Bridge paragraph → politics
├── 2.2 Economic Shocks and Political Attitudes
│ ├── 2.2.1 The China Shock and Political Consequences
│ ├── 2.2.2 Automation and Voting Behavior
│ └── 2.2.3 Methodological Parallels (NEW)
├── 2.3 Mechanisms: From Economic Disruption to Political Change
│ ├── 2.3.1 The Economic Insecurity Thesis
│ ├── 2.3.2 The Cultural Backlash Thesis
│ ├── 2.3.3 Status Threat and the 2016 Election
│ ├── 2.3.4 Psychological Foundations (DEEPENED)
│ └── 2.3.5 Identity Threat as Integrating Framework (NEW)
└── 2.4 Contribution of This Thesis
PHASE 1: RESEARCH (parallel)
Launch TWO agents in parallel using the Task tool:
Agent 1: Extraction Assembler (sonnet, read-only)
subagent_type: general-purpose
model: sonnet
Prompt:
You are assembling a structured evidence dossier for a thesis literature review rewrite.
Read ALL files in
thesis/references/literature_analysis/paper_extractions/*.json(12 files), the concept graph atthesis/references/literature_analysis/concept_graph.json, and the mechanism extraction atthesis/references/literature_analysis/mechanism_extraction.json.Produce TWO JSON files in
thesis/chapters/.litreview_workspace/:dossier.json — Organize ALL extracted evidence by the target section structure:
{ "2.1.1_task_framework": { "papers": ["autor_levy_murnane_2003"], "key_quotes": [{"text": "...", "page": N, "bibkey": "autor2003skill"}], "claims": [{"claim": "...", "evidence": "...", "strength": "strong"}], "concept_edges": ["task_model → routine_manual", "task_model → substitution"], "constructs": [{"name": "...", "definition": "..."}] }, "2.1.2_polarization": { ... }, ... }gaps.json — Identify areas where extractions are thin and RAG queries would help:
{ "gaps": [ { "section": "2.1.3", "topic": "rent dissipation mechanism details", "query": "rent dissipation automation wage premium loss", "reason": "Extraction has claim but no detailed mechanism quote" }, ... ] }Map evidence to these sections:
- 2.1.1: autor_levy_murnane_2003 (task framework, DOT, routine/nonroutine)
- 2.1.2: autor_dorn_2013 (polarization, U-shape, commuting zones, shift-share)
- 2.1.3: acemoglu_restrepo_2025 (task displacement, labor share, rent dissipation)
- 2.2.1: autor_dorn_hanson_majlesi_2020, colantone_stanig_2018 (China shock politics)
- 2.2.2: frey_berger_chen_2018, anelli_colantone_stanig_2019 (robots and voting)
- 2.2.3: methodological parallels (shift-share across studies, pseudo-panel)
- 2.3.1: economic insecurity evidence (Colantone & Stanig, Norris/Inglehart)
- 2.3.2: inglehart_norris_2016, norris_inglehart_2018 (cultural backlash)
- 2.3.3: mutz_2018, morgan_2018 (status threat debate)
- 2.3.4: osborne_2023, stenner_2005 (psychological mechanisms, dual-process)
- 2.3.5: manunta_2025, gidron_hall_2017 (identity threat integration)
- 2.4: contribution positioning
Include ALL verbatim quotes with page numbers. Be exhaustive — this is the evidence base for the entire chapter.
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.
- 11d ago First seen · 612 lines · 28 tokens per session scan C a5da790e7eda
rewrite-literature is a skill published in the GitHub repository AlessandroCaforio/Academic-Writing (17 stars, last pushed 5mo ago), licensed MIT. It adds 28 tokens to every session and 7,414 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it C with 1 finding (recursive force delete). 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.