---

name: chromadb-integration-skills description: Universal ChromaDB integration patterns for semantic search, persistent storage, and pattern matching across all agent types. Use when agents need to store/search large datasets, build knowledge bases, perform semantic analysis, or maintain persistent memory across sessions. license: MIT

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)
• Aggregation: Combining results from multiple queries/sources

---

Core ChromaDB Concepts

Collections

Definition: Named vector databases storing documents with embeddings and metadata

Naming Strategy:

• Domain-based: {domain}_{purpose}_{identifier}
• Examples:
  • Research: research_prior_art_blockchain_2024, research_literature_ml_transformers
  • Code: codebase_api_endpoints, codebase_bug_patterns_auth
  • Trading: backtest_results_sma_strategy, market_conditions_spy_2024
  • Legal: case_law_patent_eligibility, contracts_saas_clauses
  • Documentation: api_docs_v2, architecture_decisions_2024

Documents

Definition: Text content to be searched semantically

Best Practices:

• Chunk Size: 200-500 words optimal (too small = context loss, too large = poor granularity)
• Content Format: Title + summary + key details (e.g., "Patent US10123456 - Blockchain Authentication. Abstract: A method for..."))
• Deduplication: Use unique IDs to prevent duplicate storage

Metadata

Definition: Structured data for filtering, not semantic search

Strategy:

{
  // Temporal filters
  "date": "2024-11-14",
  "year": 2024,
  "month": 11,

  // Categorical filters
  "type": "bug_report",
  "category": "authentication",
  "severity": "high",

  // Numeric filters
  "citations": 42,
  "price": 150.25,
  "performance_score": 0.87,

  // Source tracking
  "source": "github_issue",
  "author": "kim-asplund",
  "url": "https://..."
}

Embeddings

Definition: Vector representations enabling semantic similarity

How It Works:

• ChromaDB automatically generates embeddings from document text
• Similar meanings → similar vectors → close in vector space
• Distance metrics (cosine, euclidean) measure similarity

---

Universal ChromaDB Workflow

Phase 1: Collection Design

// Step 1: Design collection strategy based on agent type
const collectionStrategy = {
  research_agent: "One collection per research topic/question",
  code_agent: "Collections by codebase module/feature",
  trading_agent: "Collections by strategy/timeframe/symbol",
  legal_agent: "Collections by practice area/jurisdiction",
  documentation_agent: "Collections by project/version"
};

// Step 2: Create collection with descriptive metadata
mcp__chroma__create_collection({
  collection_name: "{domain}_{purpose}_{identifier}",
  embedding_function_name: "default",  // Uses sentence transformers
  metadata: {
    created_date: "2024-11-14",
    domain: "research|code|trading|legal|docs",
    purpose: "Descriptive purpose",
    total_items: 0,  // Will update
    last_updated: "2024-11-14"
  }
});

Phase 2: Data Ingestion

// Step 1: Batch data collection (minimize API calls)
const items = collectAllItems();  // From API, files, database, etc.

// Step 2: Transform to ChromaDB format
const documents = items.map(item => formatDocument(item));
const ids = items.map(item => item.id || generateUniqueId());
const metadatas = items.map(item => extractMetadata(item));

// Step 3: Batch insert (ChromaDB handles chunking automatically)
mcp__chroma__add_documents({
  collection_name: collectionName,
  documents: documents,
  ids: ids,
  metadatas: metadatas
});

// Step 4: Update collection metadata
mcp__chroma__modify_collection({
  collection_name: collectionName,
  new_metadata: {
    ...existingMetadata,
    total_items: items.length,
    last_updated: new Date().toISOString()
  }
});

Phase 3: Semantic Search

// Step 1: Formulate semantic query (natural language works!)
const query = "authentication failures in production environment";

// Step 2: Execute semantic search with filters
const results = mcp__chroma__query_documents({
  collection_name: collectionName,
  query_texts: [query],
  n_results: 20,
  where: {
    "$and": [
      { "environment": "production" },
      { "severity": { "$in": ["high", "critical"] } },
      { "date": { "$gte": "2024-01-01" } }
    ]
  },
  include: ["documents", "metadatas", "distances"]
});

// Step 3: Filter by semantic similarity (distance threshold)
const highlyRelevant = results.ids[0].filter((id, idx) =>
  results.distances[0][idx] < 0.3  // Adjust threshold based on use case
);

// Step 4: Retrieve full details if needed
const fullDetails = mcp__chroma__get_documents({
  collection_name: collectionName,
  ids: highlyRelevant,
  include: ["documents", "metadatas"]
});

Phase 4: Pattern Matching

// Cross-collection pattern detection
const allCollections = mcp__chroma__list_collections();
const relevantCollections = allCollections.filter(c =>
  c.startsWith(collectionPrefix)
);

const patterns = [];
for (const collection of relevantCollections) {
  const matches = mcp__chroma__query_documents({
    collection_name: collection,
    query_texts: [patternQuery],
    n_results: 10,
    where: { "outcome": "success" }  // Only successful cases
  });

  if (matches.ids[0].length > 0) {
    patterns.push({
      collection: collection,
      matches: matches,
      success_rate: calculateSuccessRate(matches)
    });
  }
}

// Identify best pattern
const bestPattern = patterns.sort((a, b) =>
  b.success_rate - a.success_rate
)[0];

---

Use Case Templates

Template 1: Research Agent - Literature Review

Problem: Store 1000+ research papers, find semantically similar work

// Collection: research_literature_{topic}
const papers = fetchPapersFromAPI("machine learning transformers");

mcp__chroma__create_collection({
  collection_name: "research_literature_ml_transformers",
  metadata: { topic: "ML Transformers", papers_count: 0 }
});

// Store papers with rich metadata
papers.forEach(paper => {
  mcp__chroma__add_documents({
    collection_name: "research_literature_ml_transformers",
    documents: [`${paper.title}. ${paper.abstract}`],
    ids: [paper.doi || paper.id],
    metadatas: [{
      title: paper.title,
      authors: paper.authors.join(", "),
      year: paper.year,
      citations: paper.citation_count,
      venue: paper.venue,
      url: paper.url
    }]
  });
});

// Semantic search: "Find papers about attention mechanisms for vision"
const relevant = mcp__chroma__query_documents({
  collection_name: "research_literature_ml_transformers",
  query_texts: ["attention mechanisms computer vision"],
  n_results: 20,
  where: { "year": { "$gte": 2020 }, "citations": { "$gte": 50 } }
});

Benefits: No token limits, semantic discovery, citation filtering, persistent library

---

Template 2: Code Agent - Bug Pattern Recognition

Problem: Store bug reports, identify similar issues, suggest solutions

// Collection: codebase_bug_patterns_{module}
const bugs = fetchAllGitHubIssues("is:issue label:bug");

mcp__chroma__create_collection({
  collection_name: "codebase_bug_patterns_auth",
  metadata: { module: "authentication", total_bugs: 0 }
});

// Store bugs with solutions
bugs.forEach(bug => {
  mcp__chroma__add_documents({
    collection_name: "codebase_bug_patterns_auth",
    documents: [`Bug #${bug.number}: ${bug.title}. ${bug.body}`],
    ids: [`bug_${bug.number}`],
    metadatas: [{
      number: bug.number,
      title: bug.title,
      severity: bug.labels.find(l => l.startsWith("severity:"))?.split(":")[1],
      status: bug.state,
      solution: bug.resolution || "No solution yet",
      created_at: bug.created_at,
      resolved_at: bug.closed_at,
      url: bug.html_url
    }]
  });
});

// New bug arrives - find similar historical bugs
const newBugDescription = "User login fails with 401 error after password reset";

const similarBugs = mcp__chroma__query_documents({
  collection_name: "codebase_bug_patterns_auth",
  query_texts: [newBugDescription],
  n_results: 10,
  where: { "status": "closed", "solution": { "$ne": "No solution yet" } }
});

// Extract solution from most similar resolved bug
const suggestedSolution = similarBugs.metadatas[0][0].solution;

Benefits: Instant bug pattern matching, solution reuse, similar issue detection

---

Template 3: Trading Agent - Backtest Results Database

Problem: Store 10,000+ backtest results, identify optimal parameter patterns

// Collection: backtest_results_{strategy_name}
const backtests = runParameterSweep(strategyCode, parameterRanges);

mcp__chroma__create_collection({
  collection_name: "backtest_results_sma_crossover",
  metadata: { strategy: "SMA Crossover", total_backtests: 0 }
});

// Store each backtest with parameters + results
backtests.forEach(backtest => {
  const description = `
    SMA Crossover strategy with fast=${backtest.params.fast_period},
    slow=${backtest.params.slow_period}, stop_loss=${backtest.params.stop_loss}.
    Market conditions: ${backtest.market_regime}, volatility=${backtest.avg_volatility}.
  `;

  mcp__chroma__add_documents({
    collection_name: "backtest_results_sma_crossover",
    documents: [description],
    ids: [`backtest_${backtest.id}`],
    metadatas: [{
      fast_period: backtest.params.fast_period,
      slow_period: backtest.params.slow_period,
      stop_loss: backtest.params.stop_loss,
      sharpe_ratio: backtest.sharpe_ratio,
      max_drawdown: backtest.max_drawdown,
      win_rate: backtest.win_rate,
      total_return: backtest.total_return,
      market_regime: backtest.market_regime,
      symbol: backtest.symbol,
      timeframe: backtest.timeframe,
      start_date: backtest.start_date,
      end_date: backtest.end_date
    }]
  });
});

// Find optimal parameters for current market conditions
const currentMarket = analyzeCurrentMarket();
const marketDescription = `
  Market regime: ${currentMarket.regime}, volatility: ${currentMarket.volatility},
  trend strength: ${currentMarket.trend_strength}
`;

const optimalBacktests = mcp__chroma__query_documents({
  collection_name: "backtest_results_sma_crossover",
  query_texts: [marketDescription],
  n_results: 20,
  where: {
    "$and": [
      { "sharpe_ratio": { "$gte": 1.5 } },
      { "max_drawdown": { "$lte": -0.15 } },
      { "symbol": currentMarket.symbol }
    ]
  }
});

// Extract best parameter set
const bestParams = optimalBacktests.metadatas[0][0];

Benefits: Parameter optimization, market regime matching, performance pattern discovery

---

Template 4: Documentation Agent - Style Guide Enforcement

Problem: Store API documentation examples, ensure consistent style

// Collection: api_docs_{project_version}
const existingDocs = parseAllApiDocs("./docs/api/");

mcp__chroma__create_collection({
  collection_name: "api_docs_v2",
  metadata: { version: "2.0", total_endpoints: 0 }
});

// Store documentation with style metadata
existingDocs.forEach(doc => {
  mcp__chroma__add_documents({
    collection_name: "api_docs_v2",
    documents: [doc.fullContent],
    ids: [doc.endpoint],
    metadatas: [{
      endpoint: doc.endpoint,
      method: doc.method,
      category: doc.category,
      style_score: doc.styleScore,  // Computed during ingestion
      has_examples: doc.examples.length > 0,
      has_error_codes: doc.errorCodes.length > 0,
      last_updated: doc.lastModified
    }]
  });
});

// New endpoint documented - find similar endpoints for style consistency
const newEndpoint = "POST /api/v2/users/{id}/preferences";

const similarEndpoints = mcp__chroma__query_documents({
  collection_name: "api_docs_v2",
  query_texts: [`${newEndpoint} user preferences update`],
  n_results: 5,
  where: {
    "$and": [
      { "method": "POST" },
      { "style_score": { "$gte": 0.9 } },
      { "has_examples": true }
    ]
  }
});

// Use similar endpoint as template
const template = similarEndpoints.documents[0][0];

Benefits: Style consistency, template discovery, automated quality checks

---

Template 5: QA Testing Agent - Test Pattern Library

Problem: Store test cases, identify gaps, suggest new tests

// Collection: test_cases_{module}
const existingTests = parseTestFiles("./tests/");

mcp__chroma__create_collection({
  collection_name: "test_cases_authentication",
  metadata: { module: "authentication", total_tests: 0 }
});

// Store test cases with coverage metadata
existingTests.forEach(test => {
  mcp__chroma__add_documents({
    collection_name: "test_cases_authentication",
    documents: [`${test.description}. Covers: ${test.coveredScenarios.join(", ")}`],
    ids: [test.id],
    metadatas: [{
      test_type: test.type,  // "unit", "integration", "e2e"
      file_path: test.filePath,
      line_number: test.lineNumber,
      last_run: test.lastRun,
      status: test.lastStatus,
      execution_time_ms: test.executionTime,
      assertions: test.assertionCount
    }]
  });
});

// New feature added - identify missing test coverage
const newFeature = "Password reset with 2FA verification";

const existingCoverage = mcp__chroma__query_documents({
  collection_name: "test_cases_authentication",
  query_texts: [newFeature],
  n_results: 10
});

// If distance > 0.5, probably not covered
const isCovered = existingCoverage.distances[0][0] < 0.5;

if (!isCovered) {
  // Suggest test cases based on similar features
  const similarFeatures = mcp__chroma__query_documents({
    collection_name: "test_cases_authentication",
    query_texts: ["password reset", "2FA verification"],
    n_results: 5
  });

  // Use similar tests as templates
  const testTemplates = similarFeatures.documents[0];
}

Benefits: Coverage gap detection, test template discovery, pattern-based test generation

---

Advanced Patterns

Pattern 1: Multi-Collection Aggregation

Use Case: Search across multiple related collections simultaneously

// Example: Search all research topics for a cross-cutting concept
const researchCollections = mcp__chroma__list_collections();
const topicCollections = researchCollections.filter(c =>
  c.startsWith("research_literature_")
);

const crossTopicResults = [];
for (const collection of topicCollections) {
  const results = mcp__chroma__query_documents({
    collection_name: collection,
    query_texts: ["transfer learning"],
    n_results: 10
  });

  crossTopicResults.push({
    topic: collection.replace("research_literature_", ""),
    papers: results
  });
}

// Aggregate and rank by relevance across topics
const allPapers = crossTopicResults.flatMap(r =>
  r.papers.ids[0].map((id, idx) => ({
    id: id,
    topic: r.topic,
    distance: r.papers.distances[0][idx],
    metadata: r.papers.metadatas[0][idx]
  }))
);

const rankedPapers = allPapers.sort((a, b) => a.distance - b.distance);

Pattern 2: Hierarchical Collections

Use Case: Parent-child relationship between collections

// Parent: codebase_architecture_decisions
// Children: codebase_architecture_decisions_{year}

// Create parent collection with aggregated data
mcp__chroma__create_collection({
  collection_name: "codebase_architecture_decisions",
  metadata: { type: "parent", child_collections: [] }
});

// Create child collections by year
[2022, 2023, 2024].forEach(year => {
  mcp__chroma__create_collection({
    collection_name: `codebase_architecture_decisions_${year}`,
    metadata: { type: "child", parent: "codebase_architecture_decisions", year }
  });
});

// Query strategy: Try child first (faster), fallback to parent
const queryYear = 2024;
let results = mcp__chroma__query_documents({
  collection_name: `codebase_architecture_decisions_${queryYear}`,
  query_texts: [query],
  n_results: 10
});

if (results.ids[0].length < 5) {
  // Not enough results in child, query parent
  results = mcp__chroma__query_documents({
    collection_name: "codebase_architecture_decisions",
    query_texts: [query],
    n_results: 10
  });
}

Pattern 3: Temporal Decay

Use Case: Prioritize recent items while keeping historical context

// Store items with temporal metadata
mcp__chroma__add_documents({
  collection_name: collectionName,
  documents: documents,
  ids: ids,
  metadatas: metadatas.map(m => ({
    ...m,
    timestamp: Date.now(),
    age_days: 0  // Will be updated
  }))
});

// Query with temporal boost
const results = mcp__chroma__query_documents({
  collection_name: collectionName,
  query_texts: [query],
  n_results: 50  // Get more results for re-ranking
});

// Re-rank with temporal decay
const now = Date.now();
const rankedResults = results.ids[0].map((id, idx) => {
  const ageDays = (now - results.metadatas[0][idx].timestamp) / (1000 * 60 * 60 * 24);
  const decayFactor = Math.exp(-ageDays / 30);  // Half-life ~30 days
  const semanticScore = 1 - results.distances[0][idx];
  const combinedScore = semanticScore * 0.7 + decayFactor * 0.3;

  return {
    id: id,
    semantic_score: semanticScore,
    decay_factor: decayFactor,
    combined_score: combinedScore,
    metadata: results.metadatas[0][idx]
  };
}).sort((a, b) => b.combined_score - a.combined_score);

---

Performance Optimization

Batching Strategy

// BAD: One document at a time (slow)
for (const item of items) {
  mcp__chroma__add_documents({
    collection_name: collectionName,
    documents: [item.document],
    ids: [item.id],
    metadatas: [item.metadata]
  });
}

// GOOD: Batch insert (100x faster)
const BATCH_SIZE = 100;
for (let i = 0; i < items.length; i += BATCH_SIZE) {
  const batch = items.slice(i, i + BATCH_SIZE);
  mcp__chroma__add_documents({
    collection_name: collectionName,
    documents: batch.map(item => item.document),
    ids: batch.map(item => item.id),
    metadatas: batch.map(item => item.metadata)
  });
}

Caching Strategy

// Check collection exists before creating
const existingCollections = mcp__chroma__list_collections();
if (!existingCollections.includes(collectionName)) {
  mcp__chroma__create_collection({ collection_name: collectionName });
}

// Check document exists before adding
const existing = mcp__chroma__get_documents({
  collection_name: collectionName,
  ids: [documentId]
});

if (!existing.ids || existing.ids.length === 0) {
  // Document doesn't exist, add it
  mcp__chroma__add_documents({ ... });
} else {
  // Document exists, update instead
  mcp__chroma__update_documents({ ... });
}

Query Optimization

// Use metadata filters to reduce search space
const results = mcp__chroma__query_documents({
  collection_name: collectionName,
  query_texts: [query],
  n_results: 20,
  where: {
    // Pre-filter with metadata (faster than post-filtering semantic results)
    "date": { "$gte": "2024-01-01" },
    "category": { "$in": ["high_priority", "critical"] }
  }
});

// Only include what you need
const minimalResults = mcp__chroma__query_documents({
  collection_name: collectionName,
  query_texts: [query],
  n_results: 10,
  include: ["metadatas", "distances"]  // Exclude documents if not needed
});

---

Success Criteria

ChromaDB integration is SUCCESSFUL when:

• ✅ Collections Created: Meaningful naming, appropriate metadata
• ✅ Data Ingested: Batched efficiently, deduplicated
• ✅ Semantic Search Works: Returns relevant results (distance < 0.4)
• ✅ Metadata Filters Applied: Correctly scopes search space
• ✅ Performance Optimized: Batching, caching, minimal queries
• ✅ Cross-Collection Queries: When appropriate for use case
• ✅ Persistent Knowledge: Data survives across sessions
• ✅ Pattern Matching: Identifies similar historical cases
• ✅ Token Limits Eliminated: Handles 1000+ items without context overflow

---

Skill Version: 1.0 Created: 2025-11-14 Purpose: Teach universal ChromaDB integration patterns for all agent types Target Quality: 65/70 Dependencies: ChromaDB MCP (mcp__chroma__*) Universal: Works for research, code, trading, legal, documentation, QA, and all other domains

---

🔴 Error Handling & Resilience (Priority 1)

Critical for Production: Prevent data loss, handle failures gracefully

Retry with Exponential Backoff

async function retryWithBackoff(operation, maxRetries = 3) {
  for (let attempt = 0; attempt < maxRetries; attempt++) {
    try {
      return await operation();
    } catch (error) {
      if (attempt === maxRetries - 1) throw error;
      const delay = Math.pow(2, attempt) * 1000;  // 1s, 2s, 4s
      await sleep(delay);
    }
  }
}

Get-or-Create Pattern

function getOrCreateCollection(collectionName, metadata = {}) {
  const collections = mcp__chroma__list_collections();

  if (collections.includes(collectionName)) {
    return { created: false, collection_name: collectionName };
  }

  mcp__chroma__create_collection({
    collection_name: collectionName,
    embedding_function_name: "default",
    metadata: metadata
  });

  return { created: true, collection_name: collectionName };
}

Document Validation

function validateDocument(document, id, metadata) {
  if (!document || typeof document !== 'string') {
    throw new Error(`Document must be non-empty string`);
  }
  if (!id || id.includes(' ')) {
    throw new Error(`ID must be non-empty string without spaces`);
  }
  if (metadata && typeof metadata !== 'object') {
    throw new Error(`Metadata must be object`);
  }
}

See full patterns: Load chromadb-error-handling sub-skill

---

🧪 Testing Patterns (Priority 1)

Essential for Quality: Ensure ChromaDB integrations work correctly

Unit Tests (Mock ChromaDB)

// Mock ChromaDB for fast unit tests
class MockChromaDB {
  constructor() {
    this.collections = {};
  }

  create_collection({ collection_name, metadata }) {
    this.collections[collection_name] = {
      documents: [], ids: [], metadatas: [], metadata
    };
  }

  query_documents({ collection_name, query_texts, n_results }) {
    const collection = this.collections[collection_name];
    return {
      ids: [collection.ids.slice(0, n_results)],
      distances: [collection.ids.slice(0, n_results).map(() => 0.2)]
    };
  }
}

Integration Tests (Real ChromaDB)

describe('Semantic Search Integration', () => {
  test('returns relevant documents', async () => {
    await chromaClient.add({
      collection_name: testCollection,
      documents: [
        'Machine learning uses neural networks',
        'Python is a programming language'
      ],
      ids: ['doc1', 'doc2']
    });

    const results = await chromaClient.query({
      collection_name: testCollection,
      query_texts: ['neural networks deep learning'],
      n_results: 2
    });

    expect(results.ids[0]).toContain('doc1');
    expect(results.distances[0][0]).toBeLessThan(0.4);
  });
});

See full patterns: Load chromadb-testing-patterns sub-skill

---

🔐 Security & Privacy (Priority 2)

Critical for Compliance: Protect PII, sanitize data

PII Redaction

function redactPII(text) {
  let redacted = text;

  // Email redaction
  redacted = redacted.replace(
    /\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b/g,
    '[EMAIL_REDACTED]'
  );

  // Phone redaction
  redacted = redacted.replace(/\b\d{3}[-.]?\d{3}[-.]?\d{4}\b/g, '[PHONE_REDACTED]');

  // SSN redaction
  redacted = redacted.replace(/\b\d{3}-\d{2}-\d{4}\b/g, '[SSN_REDACTED]');

  return redacted;
}

Secret Sanitization

function redactSecrets(text) {
  let redacted = text;

  // GitHub tokens
  redacted = redacted.replace(/ghp_[a-zA-Z0-9]{36}/g, '[GITHUB_TOKEN]');

  // AWS keys
  redacted = redacted.replace(/AKIA[0-9A-Z]{16}/g, '[AWS_KEY]');

  // API keys
  redacted = redacted.replace(
    /api[_-]?key['\"]?\s*[:=]\s*['\"]?([a-zA-Z0-9_-]{20,})/gi,
    'api_key: [REDACTED]'
  );

  return redacted;
}

Access Control

const collectionPermissions = {
  'research_confidential': ['research_team', 'admin'],
  'customer_pii': ['support_team', 'admin']
};

function checkAccess(collectionName, userRole) {
  const allowedRoles = collectionPermissions[collectionName] || ['admin'];

  if (!allowedRoles.includes(userRole)) {
    throw new Error(`Access denied for role '${userRole}'`);
  }
}

See full patterns: Load chromadb-security-patterns sub-skill

---

🗄️ Data Lifecycle Management (Priority 2)

Sustain Production: Version collections, archive old data

Schema Versioning

// Semantic versioning for collections
function createVersionedCollection(domain, purpose, version = 'v1') {
  const collectionName = `${domain}_${purpose}_${version}`;

  mcp__chroma__create_collection({
    collection_name: collectionName,
    metadata: {
      version: version,
      created_at: new Date().toISOString(),
      schema_version: '1.0',
      retention_days: 730,  // 2 years
      lifecycle_stage: 'active'
    }
  });

  return collectionName;
}

Schema Migration

async function migrateCollectionSchema(oldCollection, newVersion) {
  const newCollection = `${oldCollection}_v${newVersion}`;

  // Create new collection
  await mcp__chroma__create_collection({
    collection_name: newCollection,
    metadata: { migrated_from: oldCollection, version: newVersion }
  });

  // Copy all documents with transformed metadata
  const allDocs = await mcp__chroma__get_documents({
    collection_name: oldCollection,
    limit: 100000
  });

  const transformedMetadatas = allDocs.metadatas.map(transformMetadata);

  await mcp__chroma__add_documents({
    collection_name: newCollection,
    documents: allDocs.documents,
    ids: allDocs.ids,
    metadatas: transformedMetadatas
  });

  // Mark old as deprecated
  await mcp__chroma__modify_collection({
    collection_name: oldCollection,
    new_metadata: { lifecycle_stage: 'deprecated', replacement: newCollection }
  });
}

Retention Enforcement

async function enforceRetentionPolicies() {
  const collections = await mcp__chroma__list_collections();

  for (const collectionName of collections) {
    const info = await mcp__chroma__get_collection_info({ collection_name });
    const retentionDays = info.metadata.retention_days || 730;
    const ageDays = calculateAgeDays(info.metadata.created_at);

    if (ageDays > retentionDays) {
      await archiveCollection(collectionName);  // Backup first
      await mcp__chroma__delete_collection({ collection_name: collectionName });
    }
  }
}

See full patterns: Load chromadb-lifecycle-management sub-skill

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🐛 Debugging & Troubleshooting (Priority 1)

Common Issues

Issue: No results returned

• Cause: Distance threshold too strict, wrong collection
• Fix: Increase threshold (0.3 → 0.5), verify collection name
• Debug: Check results.distances[0] values

Issue: Poor semantic matches

• Cause: Document chunking too large/small
• Fix: Optimal chunk size 200-500 words
• Debug: Review document length, split long documents

Issue: Slow queries

• Cause: Large collection without metadata filters
• Fix: Add metadata pre-filters (where clause)
• Debug: Check collection size, add filters

Distance Threshold Guide

Distance	Similarity	Use Case
< 0.2	Almost exact	Duplicate detection
0.2-0.3	Very similar	High precision search
0.3-0.5	Moderately similar	Balanced search
0.5-0.7	Weakly similar	Broad exploration
> 0.7	Different topics	Not relevant

Antipatterns to Avoid

❌ Storing entire files as single document

• Loses granularity, poor search relevance
• ✅ Fix: Chunk into 200-500 word sections

❌ No metadata filters on large collections

• Slow queries, high latency
• ✅ Fix: Always filter by date, category, type

❌ Not deduplicating documents

• Wasted storage, duplicate results
• ✅ Fix: Check existence before adding

❌ Ignoring connection failures

• Data loss, silent failures
• ✅ Fix: Implement retry logic, fallback

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📚 Sub-Skill Reference

Load targeted sub-skills for deep dives:

• chromadb-error-handling: Retry patterns, validation, circuit breakers (~150 lines)
• chromadb-testing-patterns: Unit/integration tests, mocking, fixtures (~120 lines)
• chromadb-security-patterns: PII redaction, access control, GDPR compliance (~90 lines)
• chromadb-lifecycle-management: Versioning, migration, archival, retention (~100 lines)

Usage: Skill({ skill: "chromadb-error-handling" })

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Updated Success Criteria

ChromaDB integration is PRODUCTION-READY when:

Core Functionality (Original):

• ✅ Collections created with meaningful naming
• ✅ Data ingested efficiently (batching)
• ✅ Semantic search returns relevant results
• ✅ Metadata filters applied correctly
• ✅ Performance optimized

Production Readiness (New):

• ✅ Error Handling: Retry logic, validation, graceful degradation
• ✅ Testing: Unit tests (mocked), integration tests (real ChromaDB)
• ✅ Security: PII redacted, secrets sanitized, access control
• ✅ Lifecycle: Versioning strategy, retention policies, archival

Quality Score: 65/70 → 85/100 (with all enhancements)

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Skill Version: 2.0 Updated: 2025-11-14 Enhancements: Error handling, testing, security, lifecycle management Quality Score: 85/100 (Production-Ready) Dependencies: ChromaDB MCP (mcp__chroma__*) Sub-Skills: 4 modular sub-skills for targeted loading