makecindy
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cindy
Cindy is an open-source AI agent that runs locally on your machine, integrates multiple AI harnesses and models, and provides memory, skills, and automation to perform real work in your projects and apps.
AI repository intelligence
FR-AI / ANALYSISWhy it is worth attention
It directly addresses the problem of hallucinated and misattributed citations in academic writing, using multiple API sources with automatic fallbacks and honesty mechanisms that clearly label uncertainty.
Who it is for
- Graduate students writing theses or dissertations
- Researchers who need to verify citation accuracy
- LLM agent users seeking rigorous literature support
- Academic editors or reviewers auditing references
Use cases
- Audit all citations in a draft for existence, bibliography accuracy, and claim support
- Search for supporting literature given a paragraph or topic
- Write a literature-grounded article with citations that are self-audited
- Quickly verify a single citation's existence and bibliographic details
Strengths
- Uses three API sources (Semantic Scholar, OpenAlex, arXiv) with automatic fallbacks and rate limiting
- Implements isolation firewalls (evidence, role, injection isolation) to prevent LLM hallucination
- Provides cost tiers (quick/default/thorough) matching verification intensity to risk
- Works with no API keys required and is pure Python 3.8+ standard library
Considerations
- Default judgments are abstract-based; full-text verification depends on open-access availability
- Coverage of Chinese-language literature and pre-2010 books is limited, with honest flags
- Industry proceedings (e.g., IPC/SMTA) are near-impossible to verify; tool flags as 'not found' but never fabricates
README quick start
Quick start
Description
Literature-fetching & citation-auditing skill for LLM agents (Claude/Codex) — 讓 AI 引用經得起追問:找文獻、查核引用真偽與支持度、文獻支撐寫作,防幻覺引用。
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