literature-survey

literature-survey is a skill for Claude Code from yqi96/warranted. It costs 52 tokens per session (10,914 once invoked), scanned A, original, MIT.

A workflow for reviewing many research papers, from dozens to hundreds, and organizing their findings into themes and claims. It covers search scope, screening, data extraction, classification, and synthesis; TDD-like phase rules are used to keep the evidence structure current.

In plain words
What is it for?
Use it to build a research taxonomy, screen papers, extract findings, identify research gaps, study trends, and synthesize evidence across a large candidate pool.
Why use it?
It helps prevent a large literature set from becoming an untracked collection of papers and unsupported conclusions. It records what was included, what each paper says, and how findings support broader claims.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: mentions subagents.

Part of the warranted plugin — 7 skills, 8 agents, 1 hook, 1 MCP server shipped together

Good fit Use it to build a research taxonomy, screen papers, extract findings, identify research gaps, study trends, and synthesize evidence across a large candidate pool.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/yqi96/warranted/literature-survey
Install

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.

Any agent
npx skills add yqi96/warranted --skill literature-survey
Clone the repo
git clone --depth 1 https://github.com/yqi96/warranted

Made for: Claude Code.

Or install warranted, the plugin that ships this one along with the rest of its 7 skills, 8 agents, 1 hook, 1 MCP server.

Wrote 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.

agentmods badge for literature-survey

README.md
[![agentmods](https://agentmods.dev/badge/skills/yqi96/warranted/literature-survey/github.svg)](https://agentmods.dev/skills/yqi96/warranted/literature-survey)
Your own site
<a href="https://agentmods.dev/skills/yqi96/warranted/literature-survey"><img src="https://agentmods.dev/badge/skills/yqi96/warranted/literature-survey/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.

agentmods 80×15 button for literature-survey

Your own site · 80×15
<a href="https://agentmods.dev/skills/yqi96/warranted/literature-survey"><img src="https://agentmods.dev/badge/skills/yqi96/warranted/literature-survey.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 52 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 10,914 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00052 $0.10914
Opus 5 $0.00026 $0.05457
Sonnet 5 $0.00010 $0.02183
Haiku 4.5 $0.00005 $0.01091

Measured 11d ago against content hash 21570482cf00, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

literature-survey 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 11d 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.

skills/literature-survey/SKILL.md · 364 lines

How it starts

The opening of the file, as written. The whole thing — 364 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Phases

Phase Ends when Graph state produced
1. Scope The protocol Statement exists One meta:protocol Statement holding question, criteria, planned search
2. Screen No row in screening.csv is still PENDING, and every INCLUDED row has a paper: tag Candidate ledger on disk; tag ledger of inclusions; protocol Statement updated with actual counts
3. Extract Every INCLUDED tag has statements or a terminal status Pending Statements carrying only paper: tags
4. Classify No Statement has a paper: tag without a theme: tag Converged theme vocabulary; every Statement classified
5. Synthesize Every Claim has a verdict — or is proposed for a reason written into its Warrant — every theme either carries a Rebuttal or says in its Warrant why its evidence is consistent, and compiles are fresh Multi-root DAG; everything attached to it verified

Build no Warrants before phase 5. Adding a Ground to a Warrant invalidates that Claim and every Claim above it, so any argument structure built during ingestion gets recompiled on every subsequent batch.

Phases 3 and 4 alternate. Run phase 4 every 3–5 extraction batches — never extract everything and classify at the end. A taxonomy built once, at the end, is dominated by whatever you read last.

Phase 1 — Scope

Before any search, create the protocol Statement:

create_tags([
  {name: "meta:protocol", description: "Survey protocol and screening record"},
  {name: "meta:conflict",  description: "Statement conflicts with another finding; resolve in phase 5"},
])
create_statement(
  source="observed", verification="pending", tags=["meta:protocol"],
  content="<research question> | Inclusion: <criteria> | Exclusion: <criteria> | Planned search: <venues, years, queries>"
)

This node is not bookkeeping. It becomes the Backing of the coverage Warrant in phase 5, it is the only place the survey's scope survives a lost session, and it is the text every screening subagent is given verbatim. Write the criteria in it in full — not a summary of them. A criterion you compress here is a criterion the screeners never receive.

Read the full file on GitHub · 364 lines

Changes

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.

  1. 11d ago First seen · 364 lines · 52 tokens per session scan A 21570482cf00

Subscribe to this mod's changes

literature-survey is a skill published in the GitHub repository yqi96/warranted (2 stars, last pushed 9d ago), licensed MIT. It adds 52 tokens to every session and 10,914 once invoked, about $0.0003 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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