Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/Mathews-Tom/armorynpx agentmods add skills/mathews-tom/armory/literature-reviewWrote 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/mathews-tom/armory/literature-review)<a href="https://agentmods.dev/skills/mathews-tom/armory/literature-review"><img src="https://agentmods.dev/badge/skills/mathews-tom/armory/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/mathews-tom/armory/literature-review"><img src="https://agentmods.dev/badge/skills/mathews-tom/armory/literature-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 3 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Data Exfiltration · line 66 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Prompt Injection · line 188 Large whitespace padding was detected (a block of blank lines or a long run of spaces). This can push injected instructions below or to the right of the visible area so a human reviewer never sees them while the agent still reads them. Manual review of the hidden content is recommended.Fix: Remove the large whitespace padding (blank-line blocks or long space runs) and review any content hidden below or to the right of it. Keep skill files compact and reviewable so no instructions can be
- medium Prompt Injection · line 192 Large whitespace padding was detected (a block of blank lines or a long run of spaces). This can push injected instructions below or to the right of the visible area so a human reviewer never sees them while the agent still reads them. Manual review of the hidden content is recommended.Fix: Remove the large whitespace padding (blank-line blocks or long space runs) and review any content hidden below or to the right of it. Keep skill files compact and reviewable so no instructions can be
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 | $0.00074 | $0.02139 |
| Opus 5.5 | $0.00030 | $0.00856 |
| Sonnet 5.5 | $0.00015 | $0.00428 |
| Haiku 4.5 | $0.00007 | $0.00214 |
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 1mo 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 — 197 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Literature Review
Systematic discovery, extraction, and synthesis of academic research on a defined topic.
When to use this skill vs. others
| Need | Skill |
|---|---|
| Survey a research area, synthesize multiple papers | literature-review (this skill) |
| Critique a single paper's methodology and claims | research-critique |
| Audit a manuscript's formatting, structure, citations | manuscript-review |
| Verify a manuscript's numbers trace to code | manuscript-provenance |
| Search arXiv for papers matching a query | arxiv-search (utility) |
Workflow
Phase 1: Scope Definition
Before searching, establish the review boundaries:
- Research question — What specific question does the review answer? Vague topics produce vague reviews. "What techniques exist for X" is weaker than "How do methods for X compare on metric Y across domains Z?"
- Inclusion criteria — Define what counts:
- Date range (e.g., 2020–present)
- Publication type (peer-reviewed, preprints, both)
- Domains/categories (e.g., cs.CL, cs.AI)
- Minimum relevance threshold
- Exclusion criteria — Define what does not count:
- Tangentially related work
- Non-primary sources (blog posts, tutorials) unless explicitly included
- Duplicate or superseded versions
- Expected output — What form should the review take? Narrative synthesis, tabular comparison, gap analysis, annotated bibliography, or related-work section?
Present the scope to the user for confirmation before proceeding.
Phase 2: Search & Discovery
Execute searches across available sources. Use multiple queries with varying specificity to avoid single-query blind spots.
Primary source: arXiv (via arxiv-search utility)
uv run --with arxiv python scripts/arxiv_search.py "QUERY" --max-results 30 --sort-by relevance
What ships with it
4 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.
- 1mo ago First seen · 197 lines · 74 tokens per session scan A 441f0e05042f
literature-review is a skill published in the GitHub repository Mathews-Tom/armory (337 stars, last pushed today), licensed MIT. It adds 74 tokens to every session and 2,139 once invoked, about $0.0003 per session on Opus 5.5. 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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