AI Skill Report Card
Managing Knowledge Retrieval
Quick Start5 / 15
Python# Search conversation history search_terms = ["authentication", "JWT", "security"] context = extract_relevant_passages(search_terms) synthesize_findings(context) # Organize scattered information create_knowledge_map({ "topic": "API Authentication", "sources": ["conversation", "docs", "examples"], "structure": "hierarchical" })
Recommendation▾
Replace the pseudo-code Quick Start with actual concrete actions Claude can take (e.g., 'To find authentication discussions: 1. Scan conversation for keywords like JWT, OAuth, session. 2. Quote relevant passages with message references. 3. Synthesize into organized summary')
Workflow12 / 15
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Define Search Scope
- Identify information type needed (facts, decisions, patterns, examples)
- Determine search boundaries (current conversation, uploaded files, general knowledge)
- List key terms and synonyms
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Execute Structured Search
- Search conversation history for relevant exchanges
- Extract key quotes and decisions from uploaded documents
- Cross-reference with general knowledge base
- Note information gaps
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Synthesize and Organize
- Group related findings by theme
- Identify contradictions or conflicting information
- Create hierarchical structure (main points → supporting details)
- Generate summary with source attribution
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Create Retrieval System
- Build searchable index of key findings
- Tag information by category, date, importance
- Create quick-reference formats (checklists, templates)
Recommendation▾
Remove the fictional function calls like 'extract_relevant_passages()' and 'create_knowledge_map()' - Claude doesn't have these functions and this creates confusion about capabilities
Examples15 / 20
Example 1: Authentication Method Research Input: "Find all mentions of authentication methods we've discussed" Output:
Authentication Methods Found:
JWT Tokens:
- Mentioned in message #12: "JWT preferred for API auth"
- From uploaded_doc.md: "24-hour expiration recommended"
- Pros: Stateless, scalable
- Cons: Token size, revocation complexity
Session Cookies:
- Discussed in message #8: "Good for web apps"
- Security consideration: CSRF protection needed
- Better for traditional web applications
OAuth 2.0:
- Brief mention in message #15
- Recommended for third-party integrations
Example 2: Decision Timeline Reconstruction Input: "What decisions did we make about the database?" Output:
Database Decision Timeline:
Week 1: Initial Discussion
- Considered PostgreSQL vs MongoDB
- Key factor: Need for ACID transactions
Week 2: Technology Selection
- DECISION: PostgreSQL chosen
- Rationale: Better consistency guarantees
- Alternative considered: MySQL (rejected due to JSON handling)
Week 3: Schema Design
- DECISION: Normalized schema with selective denormalization
- Performance vs consistency trade-off resolved
Example 3: Knowledge Gap Analysis Input: "What deployment information are we missing?" Output:
Deployment Knowledge Gaps:
Covered:
✓ Container setup (Docker configurations)
✓ Environment variables
✓ Database migrations
Missing:
✗ Load balancer configuration
✗ Monitoring and alerting setup
✗ Backup and disaster recovery
✗ Security scanning in CI/CD
Next Steps:
1. Research Kubernetes ingress options
2. Evaluate monitoring tools (Prometheus, DataDog)
3. Define backup retention policy
Recommendation▾
Condense the workflow section - it's too verbose and explains obvious concepts like 'identify information type needed' that Claude already understands
Best Practices
- Use specific search terms: Instead of "security", try "authentication", "authorization", "encryption"
- Cross-reference sources: Compare conversation history with uploaded documents
- Track information provenance: Always note where information came from
- Update regularly: Maintain living documents that evolve with new information
- Structure hierarchically: Main topics → subtopics → specific details
- Include timestamps: When decisions were made matters for context
Common Pitfalls
- Don't rely on memory alone - actively search and cross-reference
- Don't ignore contradictory information - highlight conflicts for resolution
- Don't mix facts with opinions without clear attribution
- Don't create overly complex organizational systems - keep it usable
- Don't forget to update knowledge base when new information emerges
- Don't lose context of why decisions were made - capture reasoning