# Source Tiering & Authority Anchoring — Reference ## Source Tiers | Tier | Source Type | Purpose | Credibility | |------|------------|---------|-------------| | **L1** | Official docs, papers, specs, RFCs | Definitions, mechanisms, verifiable facts | High | | **L2** | Official blogs, tech talks, white papers | Design intent, architectural thinking | High | | **L3** | Authoritative media, expert commentary, tutorials | Supplementary intuition, case studies | Medium | | **L4** | Community discussions, personal blogs, forums | Discover blind spots, validate understanding | Low | ## L4 Community Source Specifics (mandatory for product comparison research) | Source Type | Access Method | Value | |------------|---------------|-------| | **GitHub Issues** | Visit `github.com///issues` | Real user pain points, feature requests, bug reports | | **GitHub Discussions** | Visit `github.com///discussions` | Feature discussions, usage insights, community consensus | | **Reddit** | Search `site:reddit.com ""` | Authentic user reviews, comparison discussions | | **Hacker News** | Search `site:news.ycombinator.com ""` | In-depth technical community discussions | | **Discord/Telegram** | Product's official community channels | Active user feedback (must annotate [limited source]) | ## Principles - Conclusions must be traceable to L1/L2 - L3/L4 serve only as supplementary and validation - L4 community discussions are used to discover "what users truly care about" - Record all information sources - **Search broadly before searching deeply** — cast a wide net with multiple query variants before diving deep into any single source - **Cross-domain search** — when direct results are sparse, search adjacent fields, analogous problems, and related industries - **Never rely on a single search** — each sub-question requires multiple searches from different angles (synonyms, negations, practitioner language, academic language) ## Timeliness Filtering Rules (execute based on Step 0.5 sensitivity level) | Sensitivity Level | Source Filtering Rule | Suggested Search Parameters | |-------------------|----------------------|-----------------------------| | Critical | Only accept sources within 6 months as factual evidence | `time_range: "month"` or `start_date` set to last 3 months | | High | Prefer sources within 1 year; annotate if older than 1 year | `time_range: "year"` | | Medium | Sources within 2 years used normally; older ones need validity check | Default search | | Low | No time limit | Default search | ## High-Sensitivity Domain Search Strategy ``` 1. Round 1: Targeted official source search - Use include_domains to restrict to official domains - Example: include_domains: ["anthropic.com", "openai.com", "docs.xxx.com"] 2. Round 2: Official download/release page direct verification (BLOCKING) - Directly visit official download pages; don't rely on search caches - Use tavily-extract or WebFetch to extract page content - Verify: platform support, current version number, release date 3. Round 3: Product-specific protocol/feature search (BLOCKING) - Search protocol names the product supports (MCP, ACP, LSP, etc.) - Format: " " site:official_domain 4. Round 4: Time-limited broad search - time_range: "month" or start_date set to recent - Exclude obviously outdated sources 5. Round 5: Version verification - Cross-validate version numbers from search results - If inconsistency found, immediately consult official Changelog 6. Round 6: Community voice mining (BLOCKING - mandatory for product comparison research) - Visit the product's GitHub Issues page, review popular/pinned issues - Search Issues for key feature terms (e.g., "MCP", "plugin", "integration") - Review discussion trends from the last 3-6 months - Identify the feature points and differentiating characteristics users care most about ``` ## Community Voice Mining Detailed Steps ``` GitHub Issues Mining Steps: 1. Visit github.com///issues 2. Sort by "Most commented" to view popular discussions 3. Search keywords: - Feature-related: feature request, enhancement, MCP, plugin, API - Comparison-related: vs, compared to, alternative, migrate from 4. Review issue labels: enhancement, feature, discussion 5. Record frequently occurring feature demands and user pain points Value Translation: - Frequently discussed features -> likely differentiating highlights - User complaints/requests -> likely product weaknesses - Comparison discussions -> directly obtain user-perspective difference analysis ``` ## Source Registry Entry Template For each source consulted, immediately append to `01_source_registry.md`: ```markdown ## Source #[number] - **Title**: [source title] - **Link**: [URL] - **Tier**: L1/L2/L3/L4 - **Publication Date**: [YYYY-MM-DD] - **Timeliness Status**: Currently valid / Needs verification / Outdated (reference only) - **Version Info**: [If involving a specific version, must annotate] - **Target Audience**: [Explicitly annotate the group/geography/level this source targets] - **Research Boundary Match**: Full match / Partial overlap / Reference only - **Summary**: [1-2 sentence key content] - **Related Sub-question**: [which sub-question this corresponds to] ``` ## Target Audience Verification (BLOCKING) Before including each source, verify that its target audience matches the research boundary: | Source Type | Target audience to verify | Verification method | |------------|---------------------------|---------------------| | **Policy/Regulation** | Who is it for? (K-12/university/all) | Check document title, scope clauses | | **Academic Research** | Who are the subjects? (vocational/undergraduate/graduate) | Check methodology/sample description sections | | **Statistical Data** | Which population is measured? | Check data source description | | **Case Reports** | What type of institution is involved? | Confirm institution type | Handling mismatched sources: - Target audience completely mismatched -> do not include - Partially overlapping -> include but annotate applicable scope - Usable as analogous reference -> include but explicitly annotate "reference only"