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 h4vzz/awesome-ai-agent-skills --skill literature-reviewgit clone --depth 1 https://github.com/h4vzz/awesome-ai-agent-skillsWrote 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/h4vzz/awesome-ai-agent-skills/literature-review)<a href="https://agentmods.dev/skills/h4vzz/awesome-ai-agent-skills/literature-review"><img src="https://agentmods.dev/badge/skills/h4vzz/awesome-ai-agent-skills/literature-review/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/h4vzz/awesome-ai-agent-skills/literature-review"><img src="https://agentmods.dev/badge/skills/h4vzz/awesome-ai-agent-skills/literature-review.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 | $0.00039 | $0.02162 |
| Opus 5.5 | $0.00016 | $0.00865 |
| Sonnet 5.5 | $0.00008 | $0.00432 |
| Haiku 4.5 | $0.00004 | $0.00216 |
Grade A, and why
literature-review 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 1mo 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.
This is a copy
95% identical to literature-review — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 159 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Literature Review
This skill enables an AI agent to conduct a rigorous, structured literature review following established academic methodology. The agent defines a search strategy with targeted keywords, applies explicit inclusion and exclusion criteria to filter results, extracts key data from selected papers, and synthesizes the findings into a thematic narrative with a summary table and reference list. The workflow is inspired by systematic review practices (including PRISMA-style reporting) and is suitable for academic research, technology landscape analysis, and evidence-based decision making.
Workflow
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Define the Research Question and Scope: Work with the user to formulate a precise research question using a framework such as PICO (Population, Intervention, Comparison, Outcome) or a domain-appropriate equivalent. Establish the review's scope: time range, languages, source types (journal articles, conference papers, preprints), and any domain constraints.
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Develop the Search Strategy: Generate a set of search queries using combinations of primary keywords, synonyms, and Boolean operators. Identify the databases and sources to search (e.g., Google Scholar, Semantic Scholar, arXiv, PubMed, ACM Digital Library, IEEE Xplore). Document the complete search strategy for reproducibility.
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Screen and Filter Results: Apply predefined inclusion and exclusion criteria to the search results. Inclusion criteria typically cover topic relevance, publication date range, study type, and language. Exclusion criteria filter out duplicates, non-peer-reviewed opinion pieces, retracted papers, and off-topic results. Record the number of papers at each stage for a PRISMA-style flow.
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Extract Key Data: For each included paper, extract structured information: title, authors, year, venue, research question, methodology, key findings, limitations, and relevance to the review question. Store this data in a consistent format (table or structured notes) for cross-paper comparison.
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.
- 1mo ago First seen · 159 lines · 39 tokens per session scan A a9022b90104d
literature-review is a skill published in the GitHub repository h4vzz/awesome-ai-agent-skills (36 stars, last pushed 22d ago), licensed MIT. It adds 39 tokens to every session and 2,162 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 95% identical to literature-review, differing in 2 lines, and is treated as a copy.
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