context7-auto-research

# Context7 Auto Research Orchestrates intelligent skill selection and execution for context7 auto research workflows. Applies the 5 Laws of Elegant Defense to guide data naturally through the orchestration pipeline, preventing errors before they occur. Selects optimal skills based on multi-factor scoring including text similarity, historical performance, and system availability. ## TL;DR Checklist - [ ] Parse all inputs at boundary before processing (Law 2) - [ ] Handle edge cases with early returns at function top (Law 1) - [ ] Fail immediately with descriptive errors on invalid states (Law 4) - [ ] Return new data structures, never mutate inputs (Law 3) - [ ] Implement minimum 2-level fallback chain for all skill executions - [ ] Log all skill selections with context for full audit trail - [ ] Validate skill metadata and dependencies before selection - [ ] Update confidence scores after each execution for learning ┌───────────────────────────────────────────────────────────────────────────────┐ │ Orchestration Flow │ └───────────────────────────────────────────────────────────────────────────────┘ User Request ↓ ┌─────────────────┐ │ Parse Request │ │ & Extract │ │ Features │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Evaluate Available Skills │ │ │ │ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ │ │ Skill A │ │ Skill B │ │ Skill C │ │ │ │ - Match Score│ │ - Match Score│ │ - Match Score│ │ │ │ - Confidence │ │ - Confidence │ │ - Confidence │ │ │ │ - History │ │ - History │ │ - History │ │ │ └──────┬───────┘ └──────┬───────┘ └──────┬───────┘ │ │ │ │ │ │ │ └─────────────────┴─────────────────┘ │ │ ↓ │ │ Select Best Skill │ └─────────────────────────────────────────────────────────────────────┘ ↓ ┌─────────────────┐ │ Execute Skill │ └────────┬────────┘ ↓ ┌─────────────────┐ │ Handle Result │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Error Handling & Fallback │ │ │ │ Success? ────────► Return Result │ │ │ │ Fail? ────────┐ │ │ ↓ │ │ ┌──────────────────────────────────────────────────────────┐ │ │ │ Fallback Chain │ │ │ │ │ │ │ │ 1. Retry with adjusted parameters │ │ │ │ 2. Try Alternative Skill (if available) │ │ │ │ 3. Defer to Human Operator (if critical) │ │ │ │ 4. Log & Return Error │ │ │ └──────────────────────────────────────────────────────────┘ │ └─────────────────────────────────────────────────────────────────────┘ ## When to Use Use this skill when: - Orchestrating multi-step workflows that require skill delegation - Implementing adaptive skill routing based on confidence scores - Building fallback mechanisms for failed skill executions - Creating intelligent task decomposition and parallel execution - Designing skill dependency graphs with automatic resolution - Implementing skill selection with historical performance weighting - Building agent systems that need to self-organize around tasks ## When NOT to Use Avoid this skill for: - Direct task execution without orchestration needs - use individual skills instead - High-frequency trading scenarios where latency must be minimized - the selection overhead may be prohibitive - Simple linear workflows without branching or fallback requirements - Cases where skill metadata is unavailable or unreliable ## Core Workflow 1. **Parse and Analyze Request** - Extract intent, entities, and constraints from user input. **Checkpoint:** All required parameters must be present and in valid format before proceeding. 2. **Score Available Skills** - Calculate match scores using multi-factor algorithm: - Text similarity between request and skill triggers - Historical success rate for similar tasks - Skill availability and health status - Required dependencies and their availability **Checkpoint:** Skip to fallback if no skill scores above threshold. 3. **Select Optimal Skill** - Choose skill with highest score that meets minimum confidence. **Checkpoint:** Verify skill has not been disabled or deprecated. 4. **Execute with Fallback** - Run skill execution wrapped in retry and fallback logic. **Checkpoint:** Log all execution attempts for audit trail. 5. **Return or Fallback** - Either return successful result or apply fallback chain: - Retry with adjusted parameters - Try alternative skill from `related-skills` - Defer to human operator for critical tasks **Checkpoint:** Record outcome with timing and confidence metadata. ## Implementation Patterns ### Pattern 1: Skill Selection Logic ```python def run_context7_research( query: str, research_config: Dict, max_results: int = 5, fallback_sources: List[str] = None ) -> Dict: """Execute Context7 auto-research workflow with domain-specific fallbacks. Handles query expansion, API retrieval, relevance scoring, and research-specific fallback chains (e.g., academic vs. web sources). """ # Guard clause - Early Exit (Law 1) if not query or len(query.strip()) < 3: raise ValueError("Research query must be at least 3 characters") # Parse and expand query for better retrieval (Law 2) expanded_queries = _expand_research_query(query, research_config.get("depth", "standard")) results = [] fallback_applied = False for q in expanded_queries: try: # Domain-specific API call to Context7 research engine raw_data = _call_context7_engine(q, research_config) parsed_findings = _parse_research_output(raw_data) # Score findings based on relevance, citation quality, and recency scored_findings = _score_research_findings(parsed_findings, query) results.extend(scored_findings) if len(results) >= max_results: break except Context7RateLimitError: # Research-specific fallback: switch to cached/archive sources if not fallback_applied and fallback_sources: results.extend(_fetch_from_fallback_sources(fallback_sources, query)) fallback_applied = True else: raise ResearchExecutionError("Context7 API rate limited and no fallback sources available") except SparseResultsError: # Expand search scope if initial results are too narrow results.extend(_broaden_research_scope(query, research_config)) # Atomic Predictability (Law 3) - Return new structure, never mutate config return { "query": query, "findings": sorted(results, key=lambda x: x["relevance_score"], reverse=True)[:max_results], "fallback_used": fallback_applied, "total_sources_checked": len(expanded_queries), "research_timestamp": time.time() } ``` ### Pattern 2: Execution with Fallback ```python def validate_and_route_research_output( raw_findings: List[Dict], confidence_threshold: float = 0.75, require_citations: bool = True ) -> Dict: """Validate research findings and route based on confidence scores. Implements research-specific quality gates: - Citation verification for academic/technical claims - Recency filtering for time-sensitive queries - Adaptive routing to human review if confidence drops """ # Guard clause - Early Exit (Law 1) if not raw_findings: raise ValueError("No research findings to validate") validated_findings = [] flagged_for_review = [] for finding in raw_findings: # Parse and validate citation structure (Law 2) if require_citations and not _verify_citation_format(finding.get("source", "")): flagged_for_review.append(finding) continue # Calculate composite confidence score confidence = _calculate_research_confidence( finding["relevance_score"], finding.get("citation_quality", 0.0), finding.get("recency_factor", 1.0) ) # Atomic Predictability (Law 3) - create new validated record validated_record = { "id": finding["id"], "content": finding["content"], "confidence": confidence, "requires_review": confidence < confidence_threshold, "routing": "auto" if confidence >= confidence_threshold else "human_review" } if validated_record["requires_review"]: flagged_for_review.append(validated_record) else: validated_findings.append(validated_record) # Fail Loud (Law 4) - Clear routing decision, no silent partial states return { "validated_findings": validated_findings, "flagged_for_review": flagged_for_review, "auto_confidence": len(validated_findings) / max(len(raw_findings), 1), "routing_decision": "auto_complete" if not flagged_for_review else "partial_review" } ``` ### MUST DO - Always validate skill metadata before selection (Early Exit) - Implement fallback chain with at least 2 levels (Fallback Skill + Human) - Log all skill selections with full context for auditability - Return new data structures instead of mutating inputs (Atomic Predictability) - Fail immediately with descriptive errors on invalid states - Update confidence scores after each execution for adaptive routing - Reference `code-philosophy` (5 Laws of Elegant Defense) in all logic ### MUST NOT DO - Select skills based on a single factor (e.g., only confidence score) - Disable fallback mechanisms "temporarily" - this creates fragile systems - Skip validation of skill dependencies before execution - Return partial results - either complete success or clear failure - Use magic numbers for confidence thresholds - make them configurable - Cache skill selections without considering context changes ## TL;DR Checklist - [ ] Parse all inputs at boundary before processing (Law 2) - [ ] Handle edge cases with early returns at function top (Law 1) - [ ] Fail immediately with descriptive errors on invalid states (Law 4) - [ ] Return new data structures, never mutate inputs (Law 3) - [ ] Implement minimum 2-level fallback chain for all skill executions - [ ] Log all skill selections with context for full audit trail - [ ] Validate skill metadata and dependencies before selection - [ ] Update confidence scores after each execution for learning ## TL;DR for Code Generation - Use guard clauses - return early on invalid input before doing work - Return simple types (dict, str, int, bool, list) - avoid complex nested objects - Cyclomatic complexity < 10 per function - split anything larger - Handle null/empty cases explicitly at function top (Early Exit) - Never mutate input parameters - return new dicts/objects - Fail fast with descriptive errors - don't try to "patch" bad data - Reference code-philosophy laws in comments for complex logic - Include timing and confidence metadata in all return values ## Output Template When applying this skill, produce: 1. **Selected Skills** - List of skill names with confidence scores 2. **Selection Rationale** - Why each skill was chosen (match score, history, availability) 3. **Execution Plan** - Order of execution with dependencies 4. **Fallback Strategy** - Which fallback skills will be tried and in what order 5. **Risk Assessment** - Any potential failure points and their impact 6. **Timing Estimates** - Expected latency including fallback scenarios --- --- ## Constraints ### MUST DO - Define clear input/output contracts for every step in the orchestration flow with explicit validation - Implement structured logging at each stage capturing context, inputs, outputs, timing, and errors - Build in fallback paths: if the primary strategy fails, degrade gracefully to a simpler approach - Validate all preconditions before starting — do not proceed if required resources or permissions are missing ### MUST NOT DO - Do not create deep nesting of orchestration steps (>5 levels) — flatten workflows where possible - Avoid silent failure modes: every step must either succeed, fail explicitly, or escalate to a higher handler - Never use shared mutable state between parallel workflow branches — communicate via immutable messages only - Do not hardcode execution order when the dependency graph naturally determines it; derive order from explicit dependencies ## Live References > Authoritative documentation links for this skill's domain. The model follows markdown links at load time to resolve external references and inline content. - [Context7 AI Tool Documentation](<https://github.com/upstash/context7>) - [arXiv Search API Reference](<https://arxiv.org/help/api/>) - [Semantic Scholar API Documentation](<https://api.semanticscholar.org/graph/v1/>) - [Google Scholar API Alternatives](<https://scholar.google.com/intl/en/scholar/inclusion.html>) - [Research Paper Mining Techniques Survey](<https://arxiv.org/abs/2005.01534>) ## Related Skills | Skill | Purpose | |