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.
git clone --depth 1 https://github.com/Agentic-Assets/corbis-literature-starter-kitWrote 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/commands/agentic-assets/corbis-literature-starter-kit/lit-landscape)<a href="https://agentmods.dev/commands/agentic-assets/corbis-literature-starter-kit/lit-landscape"><img src="https://agentmods.dev/badge/commands/agentic-assets/corbis-literature-starter-kit/lit-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/commands/agentic-assets/corbis-literature-starter-kit/lit-landscape"><img src="https://agentmods.dev/badge/commands/agentic-assets/corbis-literature-starter-kit/lit-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 | $0.00013 | $0.00177 |
| Opus 5 | $0.00006 | $0.00088 |
| Sonnet 5 | $0.00003 | $0.00035 |
| Haiku 4.5 | $0.00001 | $0.00018 |
Grade A, and why
lit-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 9d 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.
What it actually says
Run the literature-landscape skill for the following topic or data:
$ARGUMENTS
Steps:
- Build a paper dataset: use existing data if provided, or run 4-5 Corbis searches to collect ~80-100 papers with metadata (title, authors, year, journal, citedByCount, abstract).
- Save the dataset to
output/lit_landscape_data.json. - Analyze the data and propose which figures to generate (timeline, citations, journals, themes, methods, gapmap). Propose gap map dimensions. Wait for user approval.
- Run
python utils/lit_landscape.pyto generate the approved figures. - Present each figure with a 2-3 sentence interpretation.
- Log to lab notebook.
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.
- 9d ago First seen · 16 lines · 13 tokens per session scan A beb2147cd3ac
lit-landscape is a command published in the GitHub repository Agentic-Assets/corbis-literature-starter-kit (11 stars, last pushed 4mo ago), licensed MIT. It adds 13 tokens to every session and 177 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 commands, from other repositories
xray
Multi-dimensional paper audit — spawns 5 parallel sub-agents to check numerical accuracy, terminology consistency, code-paper alignment, citation accuracy, and evaluation integrity.
paper-trail-ingest
Ingest the citations of one SOTA into the registry. Extracts bibliographic sections, parses citations via the citation-parser sub-agent, resolves DOI via Crossref/S2, deduplicates against the registry, creates new candidate refs, and substitutes free-text citations with [[wikilinks]]. Required first step to make a…
paper-trail-new-paper
Start writing an academic paper (IMRaD structure) on a topic, with anti-hallucination citation verification at every step. Builds on existing audited SOTAs.
paper-trail-new-sota
Create a new State-of-the-Art review on a topic, guaranteed without hallucinated citations. Inverted workflow (research → acquire PDFs → read → write), refuses to write from memory.
paper-trail-receipts
Local-only per-citation audit (no remote API). Reads the cited PDFs from the local registry, generates RECEIPTS.md. Faster than audit-article (no Crossref / paper-search calls).
lit-review
A command for carrying out a systematic literature review, meaning a structured search and summary of research papers on a chosen topic. It also builds a citation graph showing connections between papers.