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 Agentic-Assets/corbis-literature-starter-kit --skill literature-positioning-mapgit 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/skills/agentic-assets/corbis-literature-starter-kit/literature-positioning-map)<a href="https://agentmods.dev/skills/agentic-assets/corbis-literature-starter-kit/literature-positioning-map"><img src="https://agentmods.dev/badge/skills/agentic-assets/corbis-literature-starter-kit/literature-positioning-map/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-positioning-map"><img src="https://agentmods.dev/badge/skills/agentic-assets/corbis-literature-starter-kit/literature-positioning-map.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 | $0.00037 | $0.01856 |
| Opus 5 | $0.00018 | $0.00928 |
| Sonnet 5 | $0.00007 | $0.00371 |
| Haiku 4.5 | $0.00004 | $0.00186 |
Grade A, and why
literature-positioning-map 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 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.
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 — 149 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Literature Positioning Map
Your job is not to summarize everything ever written. Your job is to help the paper occupy a precise place in the literature.
Workflow
- Identify the paper's exact question, mechanism, setting, and design.
- Search for truly close papers using available tools before expanding outward.
- Build concentric rings: closest papers (direct competitors), near papers (shared mechanism or method), and contextual papers (broader field).
- Group papers by the dimension of comparison: mechanism, identification strategy, setting, data, or prediction.
- Identify the comparison set that a referee would naturally invoke.
- Write a positioning argument that is specific enough to survive scrutiny.
Tool integration (Corbis MCP) — this is critical
Always search before writing. Do not rely on parametric knowledge alone. Corbis searches 250,000+ papers via hybrid semantic+keyword search.
Mandatory search sequence (execute in order)
Step 0 — Check existing data and run architecture + frontier searches:
- If
output/paper_set.jsonexists, read it first. Papers already collected for this topic can inform the positioning without redundant searches. search_papers(query: the core topic,sortBy: "citedByCount",matchCount: 15) → immediately see the field's citation hierarchy. The most-cited papers are what referees will compare you to.search_papers(query: core topic,minYear: 2020,matchCount: 15) → the recent frontier and scooping risks.- These two searches frame everything that follows. Save results to
output/paper_set.json(merge if exists) and append queries tooutput/search_log.md.
Step 1 — Inner ring (direct competitors):
search_papers(query: the exact question + method,matchCount: 15) → find papers doing the closest thing.get_paper_details_batch(paper IDs from top 5 results) → read abstracts to confirm true overlap.
Step 2 — Middle ring (same question, different methods OR same method, different question):
search_papers(query: the same question with alternative methods,matchCount: 10)search_papers(query: the same method applied to related questions,matchCount: 10)
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 149 lines · 37 tokens per session scan A df1608cce599
literature-positioning-map is a skill published in the GitHub repository Agentic-Assets/corbis-literature-starter-kit (11 stars, last pushed 4mo ago), licensed MIT. It adds 37 tokens to every session and 1,856 once invoked, about $0.0002 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.
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