Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Zaoqu-Liu/ScienceClaw --skill review-writinggit clone --depth 1 https://github.com/Zaoqu-Liu/ScienceClawWrote 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/zaoqu-liu/scienceclaw/review-writing)<a href="https://agentmods.dev/skills/zaoqu-liu/scienceclaw/review-writing"><img src="https://agentmods.dev/badge/skills/zaoqu-liu/scienceclaw/review-writing/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/zaoqu-liu/scienceclaw/review-writing"><img src="https://agentmods.dev/badge/skills/zaoqu-liu/scienceclaw/review-writing.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 | $0.00000 | $0.04847 |
| Opus 5 | $0.00000 | $0.02423 |
| Sonnet 5 | $0.00000 | $0.00969 |
| Haiku 4.5 | $0.00000 | $0.00485 |
Grade A, and why
review-writing 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 8d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- review-writing — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 503 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review Writing — 学术综述逐节写作方法论
Use this skill when the user asks to write a literature review, review article, or 综述 based on an outline. Trigger keywords: "写综述", "write review", "综述写作", "按大纲写", "逐节写", "review section", "写第N节". This skill orchestrates the ENTIRE review writing process from outline to finished manuscript.
This skill calls academic-literature-search skill for all search and citation operations. Read that skill first if not already loaded.
Tool routing: PubMed operations → MCP tools (pubmed_search_articles, pubmed_fetch_contents, pubmed_article_connections). arXiv search, GB/T 7714 formatting, citation processing → Python code. See academic-literature-search for the complete routing table and code templates.
Architecture: Why Section-by-Section
A full review (12,000–15,000 words, 100–130 references) CANNOT be written in one pass due to context window limits. The correct approach:
Outline
→ [Phase 0: Validate & Revise outline]
→ [Phase 1..N: Per-section pipeline]
→ [Final: Assemble full review]
Each section is an independent unit of work:
Search → Filter → Group → Write → Cite → Save to file
↓
section_N.md (persisted immediately)
Final assembly reads all section files → cross-section dedup → unified numbering
Phase 0: Outline Validation & Revision (大纲验证与修订)
DO NOT skip this phase. No outline is perfect before reading the literature.
Step 0.1: Read the outline
Read the user's outline file. Parse each section's title, sub-topics, and any pre-identified references.
Step 0.2: Scout search (侦察检索)
For each section, run ONE quick search using the section title/topic as query:
- MCP
pubmed_search_articles(maxResults=10, fetchBriefSummaries=5) - For CS/AI-heavy sections: also Python
search_arxiv()(max_results=5) - For sections with known seed papers: MCP
pubmed_article_connections(similar, maxRelatedResults=5)
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.
- 8d ago First seen · 503 lines · 0 tokens per session scan A 316b20610fde
review-writing is a skill published in the GitHub repository Zaoqu-Liu/ScienceClaw (60 stars, last pushed 5mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 4,847 tokens. 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 2026-09-03.
Other skills, from other repositories
instrument-data-to-allotrope
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full…
matlab
Build, review, migrate, and safely plan MATLAB or GNU Octave numerical workflows, including arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.
exploratory-data-analysis
Perform bounded, local exploratory analysis of explicitly supported scientific files. Use for redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation sensitivity; and rigorous EDA report scaffolds. Other domain…
phylogenetics
Build and analyze phylogenetic trees using MAFFT (multiple alignment), IQ-TREE 2 (maximum likelihood), and FastTree (fast NJ/ML). Visualize with ETE3 or FigTree. For evolutionary analysis, microbial genomics, viral phylodynamics, protein family analysis, and molecular clock studies.
research-engineer
An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.
mapping-to-snomed
Maps clinical concept spans extracted by OpenMed to SNOMED CT concepts through a USER-SUPPLIED terminology server (the user's own Ontoserver, Snowstorm, or UMLS/UTS), never a bundled vocabulary. Use when the user wants to code findings, disorders, procedures, body structures, or substances to SNOMED CT, run an ECL…