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 Raidriar7170/hermes-skilleval --skill literature-reviewgit clone --depth 1 https://github.com/Raidriar7170/hermes-skillevalWrote 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/raidriar7170/hermes-skilleval/literature-review)<a href="https://agentmods.dev/skills/raidriar7170/hermes-skilleval/literature-review"><img src="https://agentmods.dev/badge/skills/raidriar7170/hermes-skilleval/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/raidriar7170/hermes-skilleval/literature-review"><img src="https://agentmods.dev/badge/skills/raidriar7170/hermes-skilleval/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 | $0.00017 | $0.00062 |
| Opus 5 | $0.00009 | $0.00031 |
| Sonnet 5 | $0.00003 | $0.00012 |
| Haiku 4.5 | $0.00002 | $0.00006 |
Grade A, and why
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 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
Literature Review
Compare related papers and organize prior work into a clear research narrative.
Use Cases
- Build related-work sections for agent benchmarks.
- Compare common evaluation weaknesses across papers.
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 · 14 lines · 17 tokens per session scan A bf0f8b19c54a
Literature Review is a skill published in the GitHub repository Raidriar7170/hermes-skilleval (123 stars, last pushed 1mo ago), licensed MIT. It adds 17 tokens to every session and 62 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 2026-09-03.
Other skills, from other repositories
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gsmm-validator
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gsmm-builder
Build or load a genome-scale metabolic model (GSMM) using COBRApy. Covers loading from BIGG, constructing minimal models from scratch, setting medium constraints, and exporting validated .json model files.
stat-result-validator
Validate statistical research outputs for formulation quality, method-to- problem alignment, theory presence, experimental evidence, fair comparison, artifact completeness, and final-claim consistency.
statistical-experimental-evaluation
Design and run statistical experiments that test the formal problem, proposed methods, theoretical predictions, baselines, and ablations.
meta-analysis
Statistical methods for combining results across multiple studies. Use when aggregating cross-study or cross-experiment results.