chromadb-integration-skills
ChromaDB Integration Skills
Purpose: This skill teaches agents how to integrate ChromaDB for semantic search, persistent storage, and pattern matching across ANY domain - research, code, trading, legal, documentation, and more.
Critical Use Case: When agents need to work with large datasets (1000+ items), perform semantic search, maintain persistent knowledge, or learn from historical patterns, ChromaDB eliminates token limits and enables powerful vector-based retrieval.
Used By: All agent types - researchers, developers, traders, legal analysts, documentation writers, QA testers, etc.
When to Use ChromaDB Integration
Use ChromaDB when:
- Large Datasets: Working with 1000+ items (documents, code files, bugs, trades, contracts, etc.)
- Semantic Search: Finding items by meaning, not just keywords
- Persistent Memory: Knowledge needs to survive across sessions, days, months
- Pattern Matching: Identifying similar historical cases/patterns for decision-making
- Cross-Session Learning: Building institutional knowledge over time
- Token Limits: Data too large to fit in context window (100K+ tokens)
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Sequential reasoning with deep self-reflection and backtracking. Use when problems have step-by-step dependencies, need careful logical reasoning, or require error correction. Each step includes self-reflection, and incorrect steps trigger backtracking. Ideal for debugging, mathematical proofs, sequential planning, or causal analysis where order matters.
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Self-improving agent architecture using ChromaDB for continuous learning, self-evaluation, and improvement storage. Agents maintain separate memory collections for learned patterns, performance metrics, and self-assessments without modifying their static .md configuration.
44integrated-reasoning
Meta-orchestration guide for choosing optimal reasoning patterns. Analyzes problem characteristics and recommends which cognitive methodology to use - tree-of-thoughts (find best), breadth-of-thought (explore all), self-reflecting-chain (sequential logic), or direct analysis. Use when facing complex problems and unsure which reasoning approach fits best.
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Teaches document writing patterns and templates that agents apply when generating documentation, reports, contracts, guides, and technical writing. Use when creating API docs, user guides, reports, changelogs, ADRs, or technical documentation.
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Universal error handling, exception management, and logging best practices for all development agents across JavaScript/TypeScript, Python, Rust, Go, and Java. Use when implementing error handling, exception management, logging, error recovery, or debugging production issues.
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Rigorous A/B/C testing framework for empirically evaluating reasoning patterns. Use when you need data-driven pattern selection, want to quantify trade-offs between patterns, or need to validate claims about which cognitive methodology performs best. Enables scientific measurement of quality, cost, and time trade-offs across ToT, BoT, SRC, HE, AR, DR, AT, RTR, and NDF patterns.
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