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Context Engineering Template is a comprehensive template and workflow repository designed to teach and implement context engineering, a structured approach to preparing and organizing the information necessary for AI coding assistants to complete complex tasks reliably. Instead of relying solely on short prompts, this project encourages developers to create rich, structured context files that include project rules, examples, and validation criteria so that AI systems can act more like...

Autocoder is an experimental auto-generation engine that transforms high-level prompts or structured descriptions into functioning source code, models, or systems with minimal manual intervention. Rather than hand-writing boilerplate or repetitive patterns, users supply a specification—such as a description of a feature, a function prototype, or a module outline—and Autocoder fills in complete implementations that compile and run. It is built to support iterative refinement: after generating...

Let Sweep handle your tech debt so you can focus on the exciting problems. Sweep is an AI junior developer that transforms bug reports & feature requests into code changes. Describe bugs, small features, and refactors like you would to a junior developer and Sweep.

...Local-first verification, safety monitoring, and guardrails for any LLM 100% Local | No Telemetry | Privacy-First | 500 free calls/day Links Product Page: subodhkc.com/products/llmverify npm: npmjs.com/package/llmverify GitHub: github.com/subodhkc/llmverify-npm Python Package (PyPI): github.com/subodhkc/llmverify Author: Subodh KC — AI governance, compliance, and security leader Feature One-liner What it catches Verify output await verify(aiResponse) Hallucinations, harmful content, quality issues Block injections isInputSafe(userMessage) Prompt injection, jailbreaks, malicious input Redact PII redactPII(text) Emails, phones, SSNs, credit cards, API keys Fix broken JSON repairJSON(broken) Missing brackets, trailing commas, unquoted keys Monitor health monitorLLM(client) Latency spikes, token drift, behavioral changes Sentinel tests sentinel.quick(client, model) LLM regressions b