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by ndcorder · LLM Plugin · ★ 60
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
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outputguard Stop wrestling with broken LLM structured output. Validate, repair, and retry — automatically. The Problem LLMs produce broken structured output constantly. JSON is the common case, but models also return YAML, TOML, Python-style literals when forced JSON is off, markdown fences, comments, trailing commas, , truncated objects, and helpful commentary around the data you asked for. Every AI application ends up writing the same brittle parser + + regex gauntlet. The Solution json {'name': 'Alice', 'age': 30,} ''' result = outputguard.validateandrepair(llmoutput, schema) print(result.valid) # True print(result.data) # {'name': 'Alice', 'age': 30} print(result.strategiesapplied) # ['stripfences', 'fixquotes', 'fixc
| Stars | 60 |
| Forks | 0 |
| Language | Python |
| Category | LLM Plugin |
| License | MIT |
| Quality Score | 68.6140318047801/100 |
| Last Updated | 2026-08-03 |
| Created | 2026-05-09 |
| Platforms | cli, python |
| Est. Tokens | ~18k |
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outputguard is Validate, repair, and retry LLM structured outputs. 13 repair strategies for common JSON malformations, JSON Schema validation, and retry-with-feedback prompts.. It is categorized as a LLM Plugin with 60 GitHub stars.
outputguard is primarily written in Python. It covers topics such as ai, anthropic, cli.
You can find installation instructions and usage details in the outputguard GitHub repository at github.com/ndcorder/outputguard. The project has 60 stars and 0 forks, indicating an active community.
outputguard is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to outputguard on Agent Skills Hub include SimplerLLM, jta, claude-code-open. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.