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 SkillMedev/academic-researcher --skill literature-reviewgit clone --depth 1 https://github.com/SkillMedev/academic-researcherWrote 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/skillmedev/academic-researcher/literature-review)<a href="https://agentmods.dev/skills/skillmedev/academic-researcher/literature-review"><img src="https://agentmods.dev/badge/skills/skillmedev/academic-researcher/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/skillmedev/academic-researcher/literature-review"><img src="https://agentmods.dev/badge/skills/skillmedev/academic-researcher/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 | $0.00120 | $0.01207 |
| Opus 5 | $0.00060 | $0.00603 |
| Sonnet 5 | $0.00024 | $0.00241 |
| Haiku 4.5 | $0.00012 | $0.00121 |
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 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.
How it starts
The opening of the file, as written. The whole thing — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Literature Review
A literature review is an argument about the state of knowledge, not a list of summaries. The failure it prevents is the annotated bibliography in disguise - one paragraph per paper, no comparison, no position - which readers skip and reviewers reject because it demonstrates reading, not understanding. Every section must compare works against each other and end somewhere: a consensus, a live dispute, or a gap.
Operating procedure
Step 1: Gather inputs
Collect before writing:
- The guiding question or claim the review serves (a thesis chapter, a paper's related-work section, a research proposal, a decision memo). Default framing if none given: "What is known, what is contested, and what is missing about X?"
- The corpus: papers supplied, or search scope (fields, venues, timeframe) if collection is part of the job.
- Audience and length target (a related-work section runs 800-1,500 words; a standalone review 3,000+).
- Inclusion posture: representative coverage is the default; if the user says "every study, defensibly complete," stop and route to systematic-review - that job needs a protocol.
Label assumed scope as a guess and confirm.
Step 2: Scope and collect
Define the question, rough inclusion criteria, and timeframe. Prioritize two classes of work: seminal papers (highly cited, field-defining - the ones later work argues with) and recent work (the current frontier). A review citing only old classics is stale; only recent preprints, rootless. Snowball: mine reference lists of the 3-5 most central papers backward, and their citing papers forward.
Step 3: Extract claims, not summaries
For each work record: the central claim, the method, sample or setting, the key limitation, and which conversation it joins. One or two sentences per field. This table is what makes Step 4 possible - grouping requires comparable atoms.
Step 4: Organize by theme, never by author
Group works by approach, finding, or dispute - not chronologically and not paper-by-paper. A theme is a sentence, not a topic word. Bad theme: "Remote work studies." Good theme: "Remote work raises measured output but weakens team cohesion, with the effect moderated by tenure." Aim for 3-6 themes; more than that means the question is too broad or the themes are actually sub-points.
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 · 76 lines · 120 tokens per session scan A 709f1fd447df
Literature Review is a skill published in the GitHub repository SkillMedev/academic-researcher (3 stars, last pushed 2mo ago), licensed MIT. It adds 120 tokens to every session and 1,207 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 2026-08-31.
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