Discourse
Pull practitioner experience reports from HN, Lobsters, Reddit, and tech blogs before you commit to a stack or architecture.
Overview
Discourse is an agent skill most often used in Idea (also Validate, Build) that gathers practitioner experience reports from HN, Lobsters, Reddit, and tech blogs on a topic.
Install
npx skills add https://github.com/athola/claude-night-market --skill discourseWhat is this skill?
- Four channels: Hacker News (Algolia API), Lobsters (site search), Reddit (JSON API), curated tech blogs via WebSearch
- Workflow: build queries with tome.channels.discourse.*, fetch via WebFetch/WebSearch, merge into attributed Finding obje
- Explicit exclusions: not for academic papers (/tome:papers) or code examples (/tome:code-search)
- Library-role skill (~200 estimated tokens) with standard model hint for lightweight scans
- Cross-source merge preserves source attribution for downstream synthesis
- 4 discourse channels: Hacker News, Lobsters, Reddit, tech blogs
- ~200 estimated tokens for the library skill definition
Adoption & trust: 1 installs on skills.sh; 304 GitHub stars; 2/3 security scanners passed (skills.sh audits); trending (+100% hot-view momentum).
What problem does it solve?
You are deciding on a framework or pattern but only have marketing pages and star counts, not grounded community experience.
Who is it for?
Early technology due diligence and gut-checking hype with threaded community reports before writing a spec or POC.
Skip if: Peer-reviewed literature reviews, licensed dataset studies, or finding copy-paste code snippets—use the dedicated Tome paper and code-search skills instead.
When should I use this skill?
Gathering community opinions on a technology or approach; finding experience reports from HN, Reddit, or Lobsters (alwaysApply: false).
What do I get? / Deliverables
You receive merged, source-attributed findings from multiple discourse channels ready to inform scope, prototype, or integration choices.
- Merged Finding objects with per-source attribution
- Channel-specific query URLs or search plans executed in one workflow
Recommended Skills
Journey fit
Spans multiple journey phases - primary shelf plus alternate fits below.
Community discourse is the default early signal when choosing what to build or which tool to adopt—canonical shelf is Idea research. Research subphase is where qualitative practitioner reports complement competitive and audience discovery.
Where it fits
Compare queue vs workflow engines by mining HN and Reddit threads before you draft a product brief.
Validate whether a 'boring' Postgres extension is production-ready using Lobsters postmortems.
Shortlist billing providers by aggregating blog and forum complaints about webhook reliability.
How it compares
Qualitative community scanner—not an academic corpus or GitHub code indexer.
Common Questions / FAQ
Who is discourse for?
Solo and indie builders using Claude Night Market / Tome who want structured community sentiment from HN, Reddit, Lobsters, and blogs in one pass.
When should I use discourse?
Use in Idea/research when picking a stack; in Validate/scope when stress-testing an approach; in Build/integrations when comparing third-party services practitioners complain about or praise.
Is discourse safe to install?
It performs outbound fetches and searches; review the Security Audits panel on this Prism page and respect rate limits on public APIs you call through your agent.
SKILL.md
READMESKILL.md - Discourse
# Discourse Search ## When To Use - Gathering community opinions on a technology or approach - Finding experience reports from HN, Reddit, or Lobsters ## When NOT To Use - Academic research (use `/tome:papers`) - Code examples (use `/tome:code-search`) Scan community channels for discussions on a topic. ## Channels - **Hacker News**: Algolia API at hn.algolia.com - **Lobsters**: WebSearch with site:lobste.rs - **Reddit**: JSON API (append .json to URLs) - **Tech blogs**: WebSearch targeting curated domains ## Workflow 1. Build search URLs/queries per channel using `tome.channels.discourse.*` functions 2. Execute via WebFetch (APIs) or WebSearch (fallback) 3. Parse responses into Finding objects 4. Merge across sources with source attribution