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 onceMisery/seahorse-agent --skill systematic-literature-reviewgit clone --depth 1 https://github.com/onceMisery/seahorse-agentWrote 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/oncemisery/seahorse-agent/systematic-literature-review)<a href="https://agentmods.dev/skills/oncemisery/seahorse-agent/systematic-literature-review"><img src="https://agentmods.dev/badge/skills/oncemisery/seahorse-agent/systematic-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/oncemisery/seahorse-agent/systematic-literature-review"><img src="https://agentmods.dev/badge/skills/oncemisery/seahorse-agent/systematic-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 | $0.00073 | $0.04146 |
| Opus 5 | $0.00036 | $0.02073 |
| Sonnet 5 | $0.00015 | $0.00829 |
| Haiku 4.5 | $0.00007 | $0.00415 |
Grade A, and why
systematic-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 12d 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
100% identical to systematic-literature-review — 0 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 — 236 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Systematic Literature Review Skill
Overview
This skill produces a structured systematic literature review (SLR) across multiple academic papers on a research topic. Given a topic query, it searches arXiv, extracts structured metadata (research question, methodology, key findings, limitations) from each paper in parallel, synthesizes themes across the full set, and emits a final report with consistent citations.
Distinct from academic-paper-review: that skill does deep peer review of a single paper. This skill does breadth-first synthesis across many papers. If the user hands you one paper URL and asks "review this paper", route to academic-paper-review instead.
When to Use This Skill
Use this skill when the user wants any of the following:
- A literature survey on a topic ("survey transformer attention variants", "review the literature on diffusion models")
- A synthesis across multiple papers ("what do recent papers say about X", "compare methodologies across papers on Y")
- A systematic review with consistent citation format ("do an SLR on Z in APA format")
- An annotated bibliography on a topic
- An overview of research trends in a field over a time window
Do not use this skill when:
- The user provides exactly one paper and asks to review it (use
academic-paper-review) - The user asks a factual question that does not require synthesizing multiple sources (answer directly)
- The user wants general web research without academic rigor (use standard web search)
Workflow
The workflow has five phases. Follow them in order.
Phase 1: Plan
Before doing any retrieval, confirm the following with the user. If any of these are unclear, ask one clarifying question that covers the missing pieces. Do not ask one question at a time.
- Topic: the research area in plain English (e.g. "transformer attention variants").
- Scope: how many papers (default 20, hard upper bound 50), optional time window (e.g. "last 2 years"), optional arXiv category (e.g.
cs.CL,cs.CV). - Citation format: APA, IEEE, or BibTeX (default APA if the user does not specify and does not seem to be writing for a specific venue).
- Output location: where to save the final report (default
/mnt/user-data/outputs/).
What ships with it
6 files 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.
- 12d ago First seen · 236 lines · 73 tokens per session scan A 9fe4891e86b2
systematic-literature-review is a skill published in the GitHub repository onceMisery/seahorse-agent (18 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 73 tokens to every session and 4,146 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to systematic-literature-review, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
instrument-data-to-allotrope
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full…
matlab
Build, review, migrate, and safely plan MATLAB or GNU Octave numerical workflows, including arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.
exploratory-data-analysis
Perform bounded, local exploratory analysis of explicitly supported scientific files. Use for redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation sensitivity; and rigorous EDA report scaffolds. Other domain…
phylogenetics
Build and analyze phylogenetic trees using MAFFT (multiple alignment), IQ-TREE 2 (maximum likelihood), and FastTree (fast NJ/ML). Visualize with ETE3 or FigTree. For evolutionary analysis, microbial genomics, viral phylodynamics, protein family analysis, and molecular clock studies.
research-engineer
An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.
mapping-to-snomed
Maps clinical concept spans extracted by OpenMed to SNOMED CT concepts through a USER-SUPPLIED terminology server (the user's own Ontoserver, Snowstorm, or UMLS/UTS), never a bundled vocabulary. Use when the user wants to code findings, disorders, procedures, body structures, or substances to SNOMED CT, run an ECL…