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 redhuntlabs/wizard --skill structured-literature-reviewgit clone --depth 1 https://github.com/redhuntlabs/wizardWrote 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/redhuntlabs/wizard/structured-literature-review)<a href="https://agentmods.dev/skills/redhuntlabs/wizard/structured-literature-review"><img src="https://agentmods.dev/badge/skills/redhuntlabs/wizard/structured-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/redhuntlabs/wizard/structured-literature-review"><img src="https://agentmods.dev/badge/skills/redhuntlabs/wizard/structured-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 | $0.00026 | $0.01081 |
| Opus 5 | $0.00013 | $0.00541 |
| Sonnet 5 | $0.00005 | $0.00216 |
| Haiku 4.5 | $0.00003 | $0.00108 |
Grade A, and why
structured-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 10d 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 — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Structured Literature Review
What this does
Produces a publishable or shareable literature review by running a structured pipeline: scope → search → screen → extract → synthesize → write → verify. Each stage has an explicit handoff artifact and a quality gate.
When to use
- Writing a literature review for a paper, thesis, or report
- Producing a "state of the field" memo for stakeholders
- Building a foundation for a new research project
- Updating an existing review with new evidence
What you bring (Inputs)
- Research question (one sentence)
- Audience (academic / practitioner / executive)
- Time and depth budget
- Inclusion criteria you can articulate (date range, source types, languages)
What you get (Output)
A written review (1-20 pages depending on scope) with: scope, methods, themes, evidence map, gaps, and a verified bibliography.
How it works (Steps)
This is a chained workflow with 6 stages and explicit handoffs.
Stages
Stage 1: Scope (handoff: a one-page scope document)
- Research question
- Inclusion criteria (date range, study types, geography, languages)
- Exclusion criteria
- Audience and depth target
Gate: scope fits on one page; inclusion criteria are specific enough that two people would categorize the same source the same way.
Stage 2: Search (handoff: a list of candidate sources, 30-100)
Run literature-scan to identify the canonical sources. Then expand:
- Forward citations from those canonical sources
- Backward citations from recent reviews
- Database searches with explicit queries (record the queries)
Save: source list with metadata (title, author, year, venue, where you found it).
Gate: at least 30 candidates; search queries are recorded.
Stage 3: Screen (handoff: included sources list, 10-40)
Apply inclusion/exclusion criteria from Stage 1 to each candidate. Record reason for each exclusion.
Gate: included list is non-empty and the exclusion reasons are concrete.
Stage 4: Extract (handoff: a structured table)
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.
- 10d ago First seen · 131 lines · 26 tokens per session scan A b17be609cbdb
structured-literature-review is a skill published in the GitHub repository redhuntlabs/wizard (9 stars, last pushed 4mo ago), licensed MIT. It adds 26 tokens to every session and 1,081 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 2026-08-31.
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