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WritingPython

Literature Survey Loop

by gaasher

Literature Survey Loop is a Writing skill for Claude Code, published by gaasher in Agent-Loop-Skills.

166 stars19 forkson gaasher/Agent-Loop-SkillsAdded 2026/09/08+2% in starsRepository updated 2026/06/30
agent-skillsagentic-loopsagentic-workflowsai-agentsanthropicautoresearchclaudeclaude-codedata-analysisliterature-reviewllm-agentsmachine-learningml-autoresearchopen-sourceprompt-engineeringred-teamingscientific-writingskillssubagents
Install in seconds
Install Literature Survey Loop
Copy Literature Survey Loop into your Claude Code skills folder. Run the command in your terminal, or review the source on GitHub before installing.
terminal
npx degit https://github.com/gaasher/Agent-Loop-Skills/tree/main/loops/literature-survey ~/.claude/skills/literature-survey

Requires Node.js. Downloads this skill only — not the rest of the repository — into your Claude Code skills folder.

Without Node.js

git clone https://github.com/gaasher/Agent-Loop-Skills.git

Clones the whole repository, then copy the skill’s own directory into your skills folder yourself.

In this catalog

Source file
loops/literature-survey/SKILL.md in gaasher/Agent-Loop-Skills
Installs to
~/.claude/skills/literature-survey
Collection
One of 25 skills cataloged from this repository
Category
Writing1361 skills

What Literature Survey Loop does

Literature Survey Loop builds an evidence-and-contradiction matrix for a research question by iteratively searching, extracting claims, and mapping source stances. Use it for structured literature surveys that need saturation, dispute tracking, and citation-backed coverage.

Literature Survey Loop is cataloged under Writing on DirSkills. Literature Survey Loop comes from a repository tagged agent-skills, agentic-loops, agentic-workflows, ai-agents and anthropic.

Documentation

README

Literature Survey Loop

A search → extract → map → expand loop that builds an evidence/contradiction matrix and stops at saturation. The artifact is the matrix (claims × sources, with each source's stance); the feedback signal is how many new, matrix-changing sources a round adds — you keep expanding until that falls below <min_new> for <patience> rounds. Unlike a one-shot summary, the loop deliberately hunts contradictions and gaps and keeps pulling threads until the picture stops changing.

The discipline: every cell — a source's stance on a claim — is backed by a verbatim snippet from a real retrieval. The value is not a tidy narrative; it is an honest map of where the literature agrees, disagrees, and is silent.

This is the opening of the README. Read the full README on GitHub.

Frequently asked about Literature Survey Loop

  • What else does gaasher publish alongside Literature Survey Loop?

    Literature Survey Loop is one of 25 skills that DirSkills catalogs from gaasher/Agent-Loop-Skills, the repository it ships in. Its siblings there include Alpha Evolve, Anomaly Investigation and Blue Team. Each one is a separate skill with its own page in this directory, installs the same way Literature Survey Loop does, and is maintained by gaasher in that same repository. The rest of the collection is listed on the gaasher/Agent-Loop-Skills page.

  • How does Literature Survey Loop compare to other Writing skills?

    Literature Survey Loop ranks #1182 by stars among the 1361 Writing skills in this catalog. The most-starred ones next to it are Article Writing, Social Media Content Calendar and Knowledge Comic Creator. DirSkills ranks by the star count of the repository each skill ships in, so that order reflects how popular those repositories are rather than any review of Literature Survey Loop against them. Open each page to compare what they document and how they install.

More from gaasher/Agent-Loop-Skills

Literature Survey Loop is one of 25 skills cataloged on DirSkills from gaasher/Agent-Loop-Skills.

See all 25 skills
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Alpha Evolve

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Anomaly Investigation

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Blue Team

Blue Team patches a target against a concrete set of failing cases, one root-cause class at a time, while checking that previously passing cases still pass. Use it for red-team failure catalogues or CI test failures when you want the fix loop to stop only when regressions are closed.
Quality
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🧪
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Claim Verify Loop

Claim Verify Loop checks each discrete claim in a results draft against the underlying dataset, then stress-tests it for outliers, confounds, and subgroup effects. Use it before publishing when a data-backed draft needs adversarial verification and revision.
AI Engineering
16619
📊
1h ago

Data Analysis Loop

Data Analysis Loop performs iterative exploratory analysis on a dataset, testing one hypothesis at a time and only keeping findings that reproduce with a meaningful effect size. Use it for open-ended discovery when every claim needs a computed number behind it.
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⚔️
1h ago

Dueling Autoresearch

Dueling Autoresearch runs two different approaches against the same metric in parallel and keeps a shared scoreboard. Use it when you want an analysis-first head-to-head between lanes such as classical versus learned methods.
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