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 AndyZhuang/Opentest --skill tooluniverse-literature-deep-researchgit clone --depth 1 https://github.com/AndyZhuang/OpentestWrote 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/andyzhuang/opentest/tooluniverse-literature-deep-research)<a href="https://agentmods.dev/skills/andyzhuang/opentest/tooluniverse-literature-deep-research"><img src="https://agentmods.dev/badge/skills/andyzhuang/opentest/tooluniverse-literature-deep-research/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/andyzhuang/opentest/tooluniverse-literature-deep-research"><img src="https://agentmods.dev/badge/skills/andyzhuang/opentest/tooluniverse-literature-deep-research.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 | $0.00121 | $0.08357 |
| Opus 5 | $0.00060 | $0.04179 |
| Sonnet 5 | $0.00024 | $0.01671 |
| Haiku 4.5 | $0.00012 | $0.00836 |
Grade A, and why
tooluniverse-literature-deep-research 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.
How it starts
The opening of the file, as written. The whole thing — 1,051 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Literature Deep Research Strategy (Enhanced)
A systematic approach to comprehensive literature research that starts with target disambiguation to prevent missing details, uses evidence grading to separate signal from noise, and produces a content-focused report with mandatory completeness sections.
KEY PRINCIPLES:
- Target disambiguation FIRST - Resolve IDs, synonyms, naming collisions before literature search
- Right-size the deliverable - Use Factoid / Verification Mode for single, answerable questions; use full report mode for “deep research”
- Report-first output - Default deliverable is a report file; an inline answer is allowed (and recommended) for Factoid / Verification Mode
- Evidence grading - Grade every claim by evidence strength (mechanistic paper vs screen hit vs review vs text-mined)
- Mandatory completeness - All checklist sections must exist, even if "unknown/limited evidence"
- Source attribution - Every piece of information traceable to database/tool
- English-first queries - Always use English terms for literature searches and tool calls, even if the user writes in another language. Only try original-language terms as a fallback if English returns no results. Respond in the user's language
Workflow Overview
User Query
↓
Phase 0: CLARIFY + MODE SELECT (factoid vs deep report)
↓
Phase 1: TARGET DISAMBIGUATION + PROFILE (default ON for biological targets)
├─ Resolve official IDs (Ensembl, UniProt, HGNC)
├─ Gather synonyms/aliases + known naming collisions
├─ Get protein length, isoforms, domain architecture
├─ Get subcellular location, expression, GO terms, pathways
└─ Output: Target Profile section + Collision-aware search plan
↓
Phase 2: LITERATURE SEARCH (internal methodology, not shown)
├─ High-precision seed queries (build mechanistic core)
├─ Citation network expansion from seeds
├─ Collision-filtered broader queries
└─ Theme clustering + evidence grading
↓
Phase 3: REPORT SYNTHESIS
├─ Progressive writing to [topic]_report.md
├─ Mandatory completeness checklist validation
└─ Biological model + testable hypotheses
↓
Optional: methods_appendix.md (only if user requests)
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 · 1,051 lines · 121 tokens per session scan A 172a370091ac
tooluniverse-literature-deep-research is a skill published in the GitHub repository AndyZhuang/Opentest (22 stars, last pushed 6mo ago), licensed MIT. It adds 121 tokens to every session and 8,357 once invoked, about $0.0006 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 2026-09-03.
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