Skill UI

bdistill transforms AI interactions into a structured, queryable knowledge base. It extracts, structures, and quality-scores domain knowledge from both closed-source AI models (in-session) and local open-source models (via Ollama). Users can build comprehensive reference datasets, Q&A pairs, or generate structured training data for traditional machine learning, ensuring validated and exportable domain expertise.

This skill generates comprehensive C4 architecture documentation for existing repositories using a bottom-up analysis approach. It follows the C4 model (Context, Container, Component, Code) to create technical documentation suitable for both technical and non-technical stakeholders. The workflow analyzes code directories, synthesizes components, and maps containers to provide a complete architectural view.

This skill mines offline Telegram Desktop chat exports (result.json) to identify unmet market needs and product opportunities. It uses chunked streaming to prevent memory crashes on large files and ensures verbatim quote grounding for accuracy. It applies cross-chat multiplicity laws to filter noise and hallucinations, supporting both English and Russian lexicons for pain point extraction without network requests.

This skill monitors the performance of Claude Code and the local operating system. It diagnoses slowdowns, measures CPU/RAM/Disk usage, checks API latency, and generates detailed system health reports. It helps determine if the bottleneck is local hardware or the remote API connection, providing actionable corrective suggestions to improve the development workflow.

Manage Cloudflare R2 buckets, objects, lifecycle rules and signed URLs. Covers Wrangler CLI, S3-compatible API access with AWS CLI and boto3, plus Worker bindings for zero-egress media delivery and static asset hosting.

Automates Coda document and data workflows through Rube MCP/Composio, covering searches, tables, formulas, exports, permissions, sharing, and publishing so bots can manage docs without manual API fiddling.

This skill simulates a senior engineer's final, deep audit of an entire codebase before committing or pushing to a remote repository. It systematically checks for junk files, dead code, security vulnerabilities (secrets, injection flaws), and performance bottlenecks (N+1 queries). Use this pre-flight check before any major release or public deployment to ensure maximum code quality and production readiness.

A structured playbook for improving the time and space complexity (Big-O) of existing, slow code. This skill guides developers through systematic refactoring, focusing on identifying bottlenecks like nested loops or N+1 queries. It emphasizes applying transformations one at a time, maintaining test coverage, and measuring performance gains to ensure correct and efficient code.

A methodology for managing project context as structured artifacts alongside code to ensure consistent AI interactions and team alignment. It involves defining product vision, tech stack, and workflows before implementation, supporting both new and existing projects.

This skill provides a systematic framework for diagnosing and proposing structural repairs for organizational failures. It moves beyond personal blame by analyzing complex dynamics such as team structures, project bottlenecks, authority chains, and governance gaps. It generates actionable memos and low-risk pilot plans, ensuring solutions address systemic root causes rather than individual shortcomings. Best used when CrossFrame Suite routes explicit organizational tasks.

This comprehensive workflow guides the entire database lifecycle, from initial architecture design (covering both SQL and NoSQL) to advanced implementation, optimization, and maintenance. It covers crucial stages like schema design, implementing migrations, optimizing query performance, developing robust data pipelines (using tools like Airflow and dbt), and managing data quality and operations. Ideal for engineers building complex, production-grade data systems.

This skill provides a structured methodology for tackling complex bugs, performance bottlenecks, and production incidents. It guides users through reproducing issues, forming hypotheses, performing controlled experiments, and narrowing down the scope using proven strategies and tools. Ideal for deep dive troubleshooting and root cause analysis in complex software systems.