Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/Agentic-Assets/corbis-literature-starter-kitnpx agentmods add skills/agentic-assets/corbis-literature-starter-kit/literature-landscapeWrote 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/agentic-assets/corbis-literature-starter-kit/literature-landscape)<a href="https://agentmods.dev/skills/agentic-assets/corbis-literature-starter-kit/literature-landscape"><img src="https://agentmods.dev/badge/skills/agentic-assets/corbis-literature-starter-kit/literature-landscape/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/agentic-assets/corbis-literature-starter-kit/literature-landscape"><img src="https://agentmods.dev/badge/skills/agentic-assets/corbis-literature-starter-kit/literature-landscape.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 | $0.00046 | $0.02326 |
| Opus 5.5 | $0.00018 | $0.00930 |
| Sonnet 5.5 | $0.00009 | $0.00465 |
| Haiku 4.5 | $0.00005 | $0.00233 |
Grade A, and why
literature-landscape 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.
How it starts
The opening of the file, as written. The whole thing — 230 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Literature Landscape
Generate publication-ready figures that visualize the structure and evolution of an academic literature. These figures appear in survey papers, dissertation chapters, grant proposals, and paper introductions to motivate research gaps.
When to use
- The user wants to visualize trends in a field (publication volume, citation patterns, thematic shifts)
- The user has completed a literature search (via
/lit-reviewor/lit-search) and wants figures - The user wants to identify research gaps visually (the coverage gap map)
- The user is writing a survey or lit review chapter and needs landscape figures
Prerequisites
Requires Python with matplotlib, pandas, and numpy. If not installed:
pip install matplotlib pandas numpy
Available figure types
| Key | Figure | What it shows |
|---|---|---|
timeline |
Publication timeline | Papers per year, optionally stacked by journal |
citations |
Citation landmark chart | Scatter of year vs. citations, top papers labeled |
journals |
Journal distribution | Horizontal bars: which outlets publish most on the topic |
themes |
Thematic evolution heatmap | Keyword frequency across year bins, showing focus shifts |
methods |
Methods timeline | Stacked bar of identification strategies over time |
gapmap |
Coverage gap map | Matrix: settings x methods, empty cells = research gaps |
Inputs to collect
| Input | Required? | Default |
|---|---|---|
| Topic or research question | Yes (if no existing data) | — |
| Existing paper data (JSON file, bib file, or reading list) | No | Run fresh Corbis searches |
| Which figures to generate | Yes (propose, user selects) | All 6 |
| Gap map row dimension | No | Geographic setting (auto-detected) |
| Gap map column dimension | No | Research method (auto-detected) |
If the user provides a topic but no existing data, run searches to build the dataset. If they point to an existing reading list or bib file from a prior /lit-review, use that as the starting point and enrich with get_paper_details to fill in missing citation counts and abstracts.
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 Changed · +2 lines 964297e2dcfd
- 1mo ago First seen · 228 lines · 46 tokens per session scan A 109109e5abca
literature-landscape is a skill published in the GitHub repository Agentic-Assets/corbis-literature-starter-kit (11 stars, last pushed 1mo ago), licensed MIT. It adds 46 tokens to every session and 2,326 once invoked, about $0.0002 per session on Opus 5.5. 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.
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