Finding Online References

Finding Online References

Overview

Orchestrates parallel subagents to search multiple sources (GitHub, web, docs), validates relevance, and returns a curated table of 3-5 references with metadata. Prioritizes relevance over popularity.

When to Use

• User explicitly asks to "look online", "search", "find references", "find examples"
• Need external code examples or documentation
• Looking for how others implement something
• Comparing libraries or approaches

When NOT to Use

• User wants to search the local codebase (use Grep/Glob)
• Answer exists in project files already open
• Simple questions answerable from knowledge
• User asks for inline code (this skill returns links, not snippets)

The Pattern

Step 1: Gather Context & Expand Query

Before searching, check if clarification is needed:

Ask for clarification when:

• Single-word query with multiple meanings (e.g., "hooks" - React? Git? Webhooks?)
• No clear technology/framework context
• Query could apply to multiple domains

Gather user code context when relevant:

• What framework/version are they using?
• What's their current architecture?
• Can they share the relevant code snippet?

Example: "I see you're looking for auth patterns. Are you using Express, Fastify, or another framework? What's your current auth setup?"

Detect Multi-Technology Queries:

Indicators:

• "X to Y" patterns → migration query
• "X with Y" patterns → integration query
• "X and Y" / "X vs Y" patterns → comparison query
• Multiple framework names in query

If detected:

• Note primary and secondary technologies
• Spawn separate agent per technology when comparing
• Add dedicated "integration" agent for cross-tech queries
• See Integration Search Agent in references/search-strategies.md

Query Expansion (NEW):

Before spawning agents, expand the query with synonyms and related terms:

See references/search-strategies.md for full query expansion templates.

Step 2: Select Search Directions

Based on query type, select 2-4 of these search directions:

Niche Technology Detection:

If query contains emerging/niche technology:

• Library < 1 year old
• Framework with < 1000 GitHub stars
• Emerging tech keywords: Bun, Deno, Tauri, SolidJS, Qwik, Htmx, Effect-TS, Drizzle, etc.

Then adjust search strategy:

• Lower star threshold from 100 to 10
• Weight recency higher than stars
• Include "trending" repos even with low stars
• Check npm weekly downloads as alternative credibility signal
• Personal blogs and newer sources become acceptable

See references/relevance-scoring.md for niche scoring criteria.

Step 3: Spawn Parallel Agents

Launch agents IN PARALLEL using a single message with multiple Task tool calls.

See references/search-strategies.md for detailed agent prompts.

Critical: Each agent must return:

• Top 3 candidates only
• For each: URL, title, stars (if GitHub), last updated, relevance explanation
• Validation that content actually matches query (not just title)

Step 4: Aggregate, Validate & Score Confidence

After agents return:

1. Merge all results
2. Remove duplicates
3. Validate relevance using criteria from references/relevance-scoring.md
4. Filter out noise (high stars but irrelevant)
5. Track filtered results with reasons (for transparency section)
6. Assign confidence score to each result

Confidence Scoring (NEW):

See references/relevance-scoring.md for detailed confidence scoring formula.

Step 5: Rank, Group & Report

Ranking priority:

1. Relevance to query (primary)
2. GitHub stars / credibility
3. Recency (prefer updated in last 12 months)

Output format (NEW - Grouped with Confidence & Freshness):

## Results by Category

### Official Documentation
| # | Source | Confidence | Fresh | Why Relevant |
|---|--------|------------|-------|--------------|
| 1 | [React Docs: useEffect](url) | High | 🟢 | Canonical reference for hook lifecycle |

### Implementation Examples
| # | Source | Stars | Confidence | Fresh | Why Relevant |
|---|--------|-------|------------|-------|--------------|
| 1 | [zustand](url) | 45k | High | 🟢 | Minimal state management, TypeScript-first |
| 2 | [example-repo](url) | 2.3k | Medium | 🟡 | Real-world usage pattern |

### Tutorials & Guides
| # | Source | Confidence | Fresh | Why Relevant |
|---|--------|------------|-------|--------------|
| 1 | [State Management in 2024](url) | High | 🟢 | Comprehensive comparison with code examples |

---

**Filtered out:**
- ~~[redux-toolkit](url)~~ - 50k stars but query was for alternatives to Redux
- ~~[old-patterns-guide](url)~~ - Good content but from 2021, patterns outdated

**Related searches:**
- "zustand vs jotai comparison"
- "react state management best practices 2024"
- "when to use context vs state library"

Freshness Indicators:

Category Definitions:

After results, offer: "Would you like me to fetch code snippets from any of these?"

Agent Prompt Templates

GitHub Repos Agent

Search GitHub for repositories matching: [QUERY]
Context: User is using [FRAMEWORK/LANGUAGE]

Use these commands:
- gh search repos "[query]" --sort stars --limit 10
- gh search repos "[query]" --sort updated --limit 10

For each promising result, validate relevance by:
1. Reading the repo description
2. Checking the README summary
3. Verifying it actually implements the pattern, not just mentions it

Return your top 3 as JSON:
[
  {
    "url": "https://github.com/...",
    "name": "repo-name",
    "stars": 5200,
    "updated": "2024-12",
    "relevance": "Why this matches the query"
  }
]

Web Search Agent

Search the web for: [QUERY]
Context: User is using [FRAMEWORK/LANGUAGE]

Use WebSearch tool with query variations:
- "[query] tutorial 2024"
- "[query] best practices"
- "[framework] [query] implementation"

For each result, validate by fetching the page and confirming:
- It contains actual implementation details (not just theory)
- It's recent (prefer 2023-2025)
- It matches the user's tech stack

Return your top 3 as JSON (same format as above, stars can be null)

GitHub Issues Agent

Search GitHub issues for: [QUERY]
This is used when the user is looking for solutions to problems.

Use: gh search issues "[query]" --sort comments --limit 10

Look for issues that:
- Have accepted answers or solutions
- Are marked as closed/resolved
- Have helpful discussion

Return top 3 with the solution summary in the relevance field.

Common Mistakes

Red Flags - STOP and Reconsider

If you catch yourself thinking any of these, pause:

Hard Rules (No Exceptions)

1. Never return irrelevant high-star results - 0 relevant results > 5 noisy ones
2. Always use parallel agents - Single message, multiple Task tool calls
3. Max 5 results - Quality over quantity
4. Links only, no inline code - Offer to fetch, don't assume
5. Ask about ambiguity - When query has multiple interpretations, clarify first
6. Always expand queries - Use synonyms to catch alternate terminology
7. Always group results - Categories make scanning easier
8. Always show filtered results - Transparency builds trust
9. Always include freshness indicators - 🟢🟡🔴 on every result
10. Always suggest related searches - 2-3 follow-up queries

References

• references/search-strategies.md - Detailed agent prompts, query expansion templates
• references/relevance-scoring.md - Validation criteria, confidence scoring, freshness thresholds

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